Effect of multiplicative noise on stationary stochastic process

NASA Astrophysics Data System (ADS)

Kargovsky, A. V.; Chikishev, A. Yu.; Chichigina, O. A.

2018-03-01

An open system that can be analyzed using the Langevin equation with multiplicative noise is considered. The stationary state of the system results from a balance of deterministic damping and random pumping simulated as noise with controlled periodicity. The dependence of statistical moments of the variable that characterizes the system on parameters of the problem is studied. A nontrivial decrease in the mean value of the main variable with an increase in noise stochasticity is revealed. Applications of the results in several physical, chemical, biological, and technical problems of natural and humanitarian sciences are discussed.

Modern methods and systems for precise control of the quality of agricultural and food production

NASA Astrophysics Data System (ADS)

Bednarjevsky, Sergey S.; Veryasov, Yuri V.; Akinina, Evgeniya V.; Smirnov, Gennady I.

1999-01-01

The results on the modeling of non-linear dynamics of strong continuous and impulse radiation in the laser nephelometry of polydisperse biological systems, important from the viewpoint of applications in biotechnologies, are presented. The processes of nonlinear self-action of the laser radiation by the multiple scattering in the disperse biological agro-media are considered. The simplified algorithms of the calculation of the parameters of the biological media under investigation are indicated and the estimates of the errors of the laser-nephelometric measurements are given. The universal high-informative optical analyzers and the standard etalon specimens of agro- objects make the technological foundation of the considered methods and systems.

Clinical and Biological Predictors of Plasma Levels of Soluble RAGE in Critically Ill Patients: Secondary Analysis of a Prospective Multicenter Observational Study.

PubMed

Pranal, Thibaut; Pereira, Bruno; Berthelin, Pauline; Roszyk, Laurence; Godet, Thomas; Chabanne, Russell; Eisenmann, Nathanael; Lautrette, Alexandre; Belville, Corinne; Blondonnet, Raiko; Cayot, Sophie; Gillart, Thierry; Skrzypczak, Yvan; Souweine, Bertrand; Bouvier, Damien; Blanchon, Loic; Sapin, Vincent; Constantin, Jean-Michel; Jabaudon, Matthieu

2018-01-01

Although soluble forms of the receptor for advanced glycation end products (RAGE) have been recently proposed as biomarkers in multiple acute or chronic diseases, few studies evaluated the influence of usual clinical and biological parameters, or of patient characteristics and comorbidities, on circulating levels of soluble RAGE in the intensive care unit (ICU) setting. To determine, among clinical and biological parameters that are usually recorded upon ICU admission, which variables, if any, could be associated with plasma levels of soluble RAGE. Data for this ancillary study were prospectively obtained from adult patients with at least one ARDS risk factor upon ICU admission enrolled in a large multicenter observational study. At ICU admission, plasma levels of total soluble RAGE (sRAGE) and endogenous secretory (es)RAGE were measured by duplicate ELISA and baseline patient characteristics, comorbidities, and usual clinical and biological indices were recorded. After univariate analyses, significant variables were used in multivariate, multidimensional analyses. 294 patients were included in this ancillary study, among whom 62% were admitted for medical reasons, including septic shock (11%), coma (11%), and pneumonia (6%). Although some variables were associated with plasma levels of RAGE soluble forms in univariate analysis, multidimensional analyses showed no significant association between admission parameters and baseline plasma sRAGE or esRAGE. We found no obvious association between circulating levels of soluble RAGE and clinical and biological indices that are usually recorded upon ICU admission. This trial is registered with NCT02070536.

The Effects of Statistical Multiplicity of Infection on Virus Quantification and Infectivity Assays.

PubMed

Mistry, Bhaven A; D'Orsogna, Maria R; Chou, Tom

2018-06-19

Many biological assays are employed in virology to quantify parameters of interest. Two such classes of assays, virus quantification assays (VQAs) and infectivity assays (IAs), aim to estimate the number of viruses present in a solution and the ability of a viral strain to successfully infect a host cell, respectively. VQAs operate at extremely dilute concentrations, and results can be subject to stochastic variability in virus-cell interactions. At the other extreme, high viral-particle concentrations are used in IAs, resulting in large numbers of viruses infecting each cell, enough for measurable change in total transcription activity. Furthermore, host cells can be infected at any concentration regime by multiple particles, resulting in a statistical multiplicity of infection and yielding potentially significant variability in the assay signal and parameter estimates. We develop probabilistic models for statistical multiplicity of infection at low and high viral-particle-concentration limits and apply them to the plaque (VQA), endpoint dilution (VQA), and luciferase reporter (IA) assays. A web-based tool implementing our models and analysis is also developed and presented. We test our proposed new methods for inferring experimental parameters from data using numerical simulations and show improvement on existing procedures in all limits. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Choosing an Appropriate Modelling Framework for Analysing Multispecies Co-culture Cell Biology Experiments.

PubMed

Markham, Deborah C; Simpson, Matthew J; Baker, Ruth E

2015-04-01

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insufficient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets.

PubMed

Naegle, Kristen M; Welsch, Roy E; Yaffe, Michael B; White, Forest M; Lauffenburger, Douglas A

2011-07-01

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems. © 2011 Naegle et al.

Control of Oscillation Patterns in a Symmetric Coupled Biological Oscillator System

NASA Astrophysics Data System (ADS)

Takamatsu, Atsuko; Tanaka, Reiko; Yamamoto, Takatoki; Fujii, Teruo

2003-08-01

A chain of three-oscillator system was constructed with living biological oscillators of phasmodial slime mold, Physarum polycehalum and the oscillation patterns were analyzed by the symmetric Hopf bifurcation theory using group theory. Multi-stability of oscillation patterns was observed, even when the coupling strength was fixed. This suggests that the coupling strength is not an effective parameter to obtain a desired oscillation pattern among the multiple patterns. Here we propose a method to control oscillation patterns using resonance to external stimulus and demonstrate pattern switching induced by frequency resonance given to only one of oscillators in the system.

[Development and Application of Metabonomics in Forensic Toxicology].

PubMed

Yan, Hui; Shen, Min

2015-06-01

Metabonomics is an important branch of system biology following the development of genomics, transcriptomics and proteomics. It can perform high-throughput detection and data processing with multiple parameters, potentially enabling the identification and quantification of all small metabolites in a biological system. It can be used to provide comprehensive information on the toxicity effects, toxicological mechanisms and biomarkers, sensitively finding the unusual metabolic changes caused by poison. This article mainly reviews application of metabonomics in toxicological studies of abused drugs, pesticides, poisonous plants and poisonous animals, and also illustrates the new direction of forensic toxicology research.

The principle of sufficiency and the evolution of control: using control analysis to understand the design principles of biological systems.

PubMed

Brown, Guy C

2010-10-01

Control analysis can be used to try to understand why (quantitatively) systems are the way that they are, from rate constants within proteins to the relative amount of different tissues in organisms. Many biological parameters appear to be optimized to maximize rates under the constraint of minimizing space utilization. For any biological process with multiple steps that compete for control in series, evolution by natural selection will tend to even out the control exerted by each step. This is for two reasons: (i) shared control maximizes the flux for minimum protein concentration, and (ii) the selection pressure on any step is proportional to its control, and selection will, by increasing the rate of a step (relative to other steps), decrease its control over a pathway. The control coefficient of a parameter P over fitness can be defined as (∂N/N)/(∂P/P), where N is the number of individuals in the population, and ∂N is the change in that number as a result of the change in P. This control coefficient is equal to the selection pressure on P. I argue that biological systems optimized by natural selection will conform to a principle of sufficiency, such that the control coefficient of all parameters over fitness is 0. Thus in an optimized system small changes in parameters will have a negligible effect on fitness. This principle naturally leads to (and is supported by) the dominance of wild-type alleles over null mutants.

Complex, non-monotonic dose-response curves with multiple maxima: Do we (ever) sample densely enough?

PubMed

Cvrčková, Fatima; Luštinec, Jiří; Žárský, Viktor

2015-01-01

We usually expect the dose-response curves of biological responses to quantifiable stimuli to be simple, either monotonic or exhibiting a single maximum or minimum. Deviations are often viewed as experimental noise. However, detailed measurements in plant primary tissue cultures (stem pith explants of kale and tobacco) exposed to varying doses of sucrose, cytokinins (BA or kinetin) or auxins (IAA or NAA) revealed that growth and several biochemical parameters exhibit multiple reproducible, statistically significant maxima over a wide range of exogenous substance concentrations. This results in complex, non-monotonic dose-response curves, reminiscent of previous reports of analogous observations in both metazoan and plant systems responding to diverse pharmacological treatments. These findings suggest the existence of a hitherto neglected class of biological phenomena resulting in dose-response curves exhibiting periodic patterns of maxima and minima, whose causes remain so far uncharacterized, partly due to insufficient sampling frequency used in many studies.

Apical polarity in three-dimensional culture systems: where to now?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Inman, J.L.; Bissell, Mina

2010-01-21

Delineation of the mechanisms that establish and maintain the polarity of epithelial tissues is essential to understanding morphogenesis, tissue specificity and cancer. Three-dimensional culture assays provide a useful platform for dissecting these processes but, as discussed in a recent study in BMC Biology on the culture of mammary gland epithelial cells, multiple parameters that influence the model must be taken into account.

Clinical and Biological Predictors of Plasma Levels of Soluble RAGE in Critically Ill Patients: Secondary Analysis of a Prospective Multicenter Observational Study

PubMed Central

Pranal, Thibaut; Pereira, Bruno; Berthelin, Pauline; Roszyk, Laurence; Chabanne, Russell; Eisenmann, Nathanael; Lautrette, Alexandre; Belville, Corinne; Blondonnet, Raiko; Gillart, Thierry; Skrzypczak, Yvan; Souweine, Bertrand; Bouvier, Damien; Constantin, Jean-Michel

2018-01-01

Rationale Although soluble forms of the receptor for advanced glycation end products (RAGE) have been recently proposed as biomarkers in multiple acute or chronic diseases, few studies evaluated the influence of usual clinical and biological parameters, or of patient characteristics and comorbidities, on circulating levels of soluble RAGE in the intensive care unit (ICU) setting. Objectives To determine, among clinical and biological parameters that are usually recorded upon ICU admission, which variables, if any, could be associated with plasma levels of soluble RAGE. Methods Data for this ancillary study were prospectively obtained from adult patients with at least one ARDS risk factor upon ICU admission enrolled in a large multicenter observational study. At ICU admission, plasma levels of total soluble RAGE (sRAGE) and endogenous secretory (es)RAGE were measured by duplicate ELISA and baseline patient characteristics, comorbidities, and usual clinical and biological indices were recorded. After univariate analyses, significant variables were used in multivariate, multidimensional analyses. Measurements and Main Results 294 patients were included in this ancillary study, among whom 62% were admitted for medical reasons, including septic shock (11%), coma (11%), and pneumonia (6%). Although some variables were associated with plasma levels of RAGE soluble forms in univariate analysis, multidimensional analyses showed no significant association between admission parameters and baseline plasma sRAGE or esRAGE. Conclusions We found no obvious association between circulating levels of soluble RAGE and clinical and biological indices that are usually recorded upon ICU admission. This trial is registered with NCT02070536. PMID:29861796

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Set membership experimental design for biological systems.

PubMed

Marvel, Skylar W; Williams, Cranos M

2012-03-21

Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models.

Set membership experimental design for biological systems

PubMed Central

2012-01-01

Background Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. Results In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. Conclusions The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models. PMID:22436240

Kinematic parameters that influence the aesthetic perception of beauty in contemporary dance.

PubMed

Torrents, Carlota; Castañer, Marta; Jofre, Toni; Morey, Gaspar; Reverter, Ferran

2013-01-01

Some experiments have stablished that certain kinematic parameters can influence the subjective aesthetic perception of the dance audience. Neave, McCarty, Freynik, Caplan, Hönekopp, and Fink (2010, Biology Letters 7 221-224) reported eleven movement parameters in non-expert male dancers, showing a significant positive correlation with perceived dance quality. We aim to identify some of the kinematic parameters of expert dancers' movements that influence the subjective aesthetic perception of observers in relation to specific skills of contemporary dance. Four experienced contemporary dancers performed three repetitions of four dance-related motor skills. Motion was captured by a VICON-MX system. The resulting 48 animations were viewed by 108 observers. The observers judged beauty using a semantic differential. The data were then subjected to multiple factor analysis. The results suggested that there were strong associations between higher beauty scores and certain kinematic parameters, especially those related to amplitude of movement.

Experimental identification of a comb-shaped chaotic region in multiple parameter spaces simulated by the Hindmarsh—Rose neuron model

NASA Astrophysics Data System (ADS)

Jia, Bing

2014-03-01

A comb-shaped chaotic region has been simulated in multiple two-dimensional parameter spaces using the Hindmarsh—Rose (HR) neuron model in many recent studies, which can interpret almost all of the previously simulated bifurcation processes with chaos in neural firing patterns. In the present paper, a comb-shaped chaotic region in a two-dimensional parameter space was reproduced, which presented different processes of period-adding bifurcations with chaos with changing one parameter and fixed the other parameter at different levels. In the biological experiments, different period-adding bifurcation scenarios with chaos by decreasing the extra-cellular calcium concentration were observed from some neural pacemakers at different levels of extra-cellular 4-aminopyridine concentration and from other pacemakers at different levels of extra-cellular caesium concentration. By using the nonlinear time series analysis method, the deterministic dynamics of the experimental chaotic firings were investigated. The period-adding bifurcations with chaos observed in the experiments resembled those simulated in the comb-shaped chaotic region using the HR model. The experimental results show that period-adding bifurcations with chaos are preserved in different two-dimensional parameter spaces, which provides evidence of the existence of the comb-shaped chaotic region and a demonstration of the simulation results in different two-dimensional parameter spaces in the HR neuron model. The results also present relationships between different firing patterns in two-dimensional parameter spaces.

A new statistical method for transfer coefficient calculations in the framework of the general multiple-compartment model of transport for radionuclides in biological systems.

PubMed

Garcia, F; Arruda-Neto, J D; Manso, M V; Helene, O M; Vanin, V R; Rodriguez, O; Mesa, J; Likhachev, V P; Filho, J W; Deppman, A; Perez, G; Guzman, F; de Camargo, S P

1999-10-01

A new and simple statistical procedure (STATFLUX) for the calculation of transfer coefficients of radionuclide transport to animals and plants is proposed. The method is based on the general multiple-compartment model, which uses a system of linear equations involving geometrical volume considerations. By using experimentally available curves of radionuclide concentrations versus time, for each animal compartment (organs), flow parameters were estimated by employing a least-squares procedure, whose consistency is tested. Some numerical results are presented in order to compare the STATFLUX transfer coefficients with those from other works and experimental data.

A distributed system for fast alignment of next-generation sequencing data.

PubMed

Srimani, Jaydeep K; Wu, Po-Yen; Phan, John H; Wang, May D

2010-12-01

We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.

Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models

PubMed Central

2011-01-01

In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison. PMID:21989173

Differential Multiphoton Laser Scanning Microscopy

PubMed Central

Field, Jeffrey J.; Sheetz, Kraig E.; Chandler, Eric V.; Hoover, Erich E.; Young, Michael D.; Ding, Shi-you; Sylvester, Anne W.; Kleinfeld, David; Squier, Jeff A.

2016-01-01

Multifocal multiphoton microscopy (MMM) in the biological and medical sciences has become an important tool for obtaining high resolution images at video rates. While current implementations of MMM achieve very high frame rates, they are limited in their applicability to essentially those biological samples that exhibit little or no scattering. In this paper, we report on a method for MMM in which imaging detection is not necessary (single element point detection is implemented), and is therefore fully compatible for use in imaging through scattering media. Further, we demonstrate that this method leads to a new type of MMM wherein it is possible to simultaneously obtain multiple images and view differences in excitation parameters in a single shot. PMID:27390511

A systematic petri net approach for multiple-scale modeling and simulation of biochemical processes.

PubMed

Chen, Ming; Hu, Minjie; Hofestädt, Ralf

2011-06-01

A method to exploit hybrid Petri nets for modeling and simulating biochemical processes in a systematic way was introduced. Both molecular biology and biochemical engineering aspects are manipulated. With discrete and continuous elements, the hybrid Petri nets can easily handle biochemical factors such as metabolites concentration and kinetic behaviors. It is possible to translate both molecular biological behavior and biochemical processes workflow into hybrid Petri nets in a natural manner. As an example, penicillin production bioprocess is modeled to illustrate the concepts of the methodology. Results of the dynamic of production parameters in the bioprocess were simulated and observed diagrammatically. Current problems and post-genomic perspectives were also discussed.

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Prediction of EST functional relationships via literature mining with user-specified parameters.

PubMed

Wang, Hei-Chia; Huang, Tian-Hsiang

2009-04-01

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

Multiple graph regularized protein domain ranking.

PubMed

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2012-11-19

Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

Multiple graph regularized protein domain ranking

PubMed Central

2012-01-01

Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. PMID:23157331

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models

PubMed Central

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization. PMID:27243005

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models.

PubMed

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.

Biological definition of multiple chemical sensitivity from redox state and cytokine profiling and not from polymorphisms of xenobiotic-metabolizing enzymes

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Luca, Chiara; Scordo, Maria G.; Cesareo, Eleonora

Background: Multiple chemical sensitivity (MCS) is a poorly clinically and biologically defined environment-associated syndrome. Although dysfunctions of phase I/phase II metabolizing enzymes and redox imbalance have been hypothesized, corresponding genetic and metabolic parameters in MCS have not been systematically examined. Objectives: We sought for genetic, immunological, and metabolic markers in MCS. Methods: We genotyped patients with diagnosis of MCS, suspected MCS and Italian healthy controls for allelic variants of cytochrome P450 isoforms (CYP2C9, CYP2C19, CYP2D6, and CYP3A5), UDP-glucuronosyl transferase (UGT1A1), and glutathione S-transferases (GSTP1, GSTM1, and GSTT1). Erythrocyte membrane fatty acids, antioxidant (catalase, superoxide dismutase (SOD)) and glutathione metabolizing (GST,more » glutathione peroxidase (Gpx)) enzymes, whole blood chemiluminescence, total antioxidant capacity, levels of nitrites/nitrates, glutathione, HNE-protein adducts, and a wide spectrum of cytokines in the plasma were determined. Results: Allele and genotype frequencies of CYPs, UGT, GSTM, GSTT, and GSTP were similar in the Italian MCS patients and in the control populations. The activities of erythrocyte catalase and GST were lower, whereas Gpx was higher than normal. Both reduced and oxidised glutathione were decreased, whereas nitrites/nitrates were increased in the MCS groups. The MCS fatty acid profile was shifted to saturated compartment and IFNgamma, IL-8, IL-10, MCP-1, PDGFbb, and VEGF were increased. Conclusions: Altered redox and cytokine patterns suggest inhibition of expression/activity of metabolizing and antioxidant enzymes in MCS. Metabolic parameters indicating accelerated lipid oxidation, increased nitric oxide production and glutathione depletion in combination with increased plasma inflammatory cytokines should be considered in biological definition and diagnosis of MCS.« less

An easy-to-build and re-usable microfluidic system for live-cell imaging.

PubMed

Babic, Julien; Griscom, Laurent; Cramer, Jeremy; Coudreuse, Damien

2018-06-20

Real-time monitoring of cellular responses to dynamic changes in their environment or to specific treatments has become central to cell biology. However, when coupled to live-cell imaging, such strategies are difficult to implement with precision and high time resolution, and the simultaneous alteration of multiple parameters is a major challenge. Recently, microfluidics has provided powerful solutions for such analyses, bringing an unprecedented level of control over the conditions and the medium in which cells under microscopic observation are grown. However, such technologies have remained under-exploited, largely as a result of the complexity associated with microfabrication procedures. In this study, we have developed simple but powerful microfluidic devices dedicated to live-cell imaging. These microsystems take advantage of a robust elastomer that is readily available to researchers and that presents excellent bonding properties, in particular to microscopy-grade glass coverslips. Importantly, the chips are easy-to-build without sophisticated equipment, and they are compatible with the integration of complex, customized fluidic networks as well as with the multiplexing of independent assays on a single device. We show that the chips are re-usable, a significant advantage for the popularization of microfluidics in cell biology. Moreover, we demonstrate that they allow for the dynamic, accurate and simultaneous control of multiple parameters of the cellular environment. While they do not possess all the features of the microdevices that are built using complex and costly procedures, the simplicity and versatility of the chips that we have developed make them an attractive alternative for a range of applications. The emergence of such devices, which can be fabricated and used by any laboratory, will provide the possibility for a larger number of research teams to take full advantage of these new methods for investigating cell biology.

A 1.26 μW Cytomimetic IC Emulating Complex Nonlinear Mammalian Cell Cycle Dynamics: Synthesis, Simulation and Proof-of-Concept Measured Results.

PubMed

Houssein, Alexandros; Papadimitriou, Konstantinos I; Drakakis, Emmanuel M

2015-08-01

Cytomimetic circuits represent a novel, ultra low-power, continuous-time, continuous-value class of circuits, capable of mapping on silicon cellular and molecular dynamics modelled by means of nonlinear ordinary differential equations (ODEs). Such monolithic circuits are in principle able to emulate on chip, single or multiple cell operations in a highly parallel fashion. Cytomimetic topologies can be synthesized by adopting the Nonlinear Bernoulli Cell Formalism (NBCF), a mathematical framework that exploits the striking similarities between the equations describing weakly-inverted Metal-Oxide Semiconductor (MOS) devices and coupled nonlinear ODEs, typically appearing in models of naturally encountered biochemical systems. The NBCF maps biological state variables onto strictly positive subthreshold MOS circuit currents. This paper presents the synthesis, the simulation and proof-of-concept chip results corresponding to the emulation of a complex cellular network mechanism, the skeleton model for the network of Cyclin-dependent Kinases (CdKs) driving the mammalian cell cycle. This five variable nonlinear biological model, when appropriate model parameter values are assigned, can exhibit multiple oscillatory behaviors, varying from simple periodic oscillations, to complex oscillations such as quasi-periodicity and chaos. The validity of our approach is verified by simulated results with realistic process parameters from the commercially available AMS 0.35 μm technology and by chip measurements. The fabricated chip occupies an area of 2.27 mm2 and consumes a power of 1.26 μW from a power supply of 3 V. The presented cytomimetic topology follows closely the behavior of its biological counterpart, exhibiting similar time-dependent solutions of the Cdk complexes, the transcription factors and the proteins.

Virtual Plant Tissue: Building Blocks for Next-Generation Plant Growth Simulation

PubMed Central

De Vos, Dirk; Dzhurakhalov, Abdiravuf; Stijven, Sean; Klosiewicz, Przemyslaw; Beemster, Gerrit T. S.; Broeckhove, Jan

2017-01-01

Motivation: Computational modeling of plant developmental processes is becoming increasingly important. Cellular resolution plant tissue simulators have been developed, yet they are typically describing physiological processes in an isolated way, strongly delimited in space and time. Results: With plant systems biology moving toward an integrative perspective on development we have built the Virtual Plant Tissue (VPTissue) package to couple functional modules or models in the same framework and across different frameworks. Multiple levels of model integration and coordination enable combining existing and new models from different sources, with diverse options in terms of input/output. Besides the core simulator the toolset also comprises a tissue editor for manipulating tissue geometry and cell, wall, and node attributes in an interactive manner. A parameter exploration tool is available to study parameter dependence of simulation results by distributing calculations over multiple systems. Availability: Virtual Plant Tissue is available as open source (EUPL license) on Bitbucket (https://bitbucket.org/vptissue/vptissue). The project has a website https://vptissue.bitbucket.io. PMID:28523006

Identifying parameter regions for multistationarity

PubMed Central

Conradi, Carsten; Mincheva, Maya; Wiuf, Carsten

2017-01-01

Mathematical modelling has become an established tool for studying the dynamics of biological systems. Current applications range from building models that reproduce quantitative data to identifying systems with predefined qualitative features, such as switching behaviour, bistability or oscillations. Mathematically, the latter question amounts to identifying parameter values associated with a given qualitative feature. We introduce a procedure to partition the parameter space of a parameterized system of ordinary differential equations into regions for which the system has a unique or multiple equilibria. The procedure is based on the computation of the Brouwer degree, and it creates a multivariate polynomial with parameter depending coefficients. The signs of the coefficients determine parameter regions with and without multistationarity. A particular strength of the procedure is the avoidance of numerical analysis and parameter sampling. The procedure consists of a number of steps. Each of these steps might be addressed algorithmically using various computer programs and available software, or manually. We demonstrate our procedure on several models of gene transcription and cell signalling, and show that in many cases we obtain a complete partitioning of the parameter space with respect to multistationarity. PMID:28972969

Systems modelling methodology for the analysis of apoptosis signal transduction and cell death decisions.

PubMed

Rehm, Markus; Prehn, Jochen H M

2013-06-01

Systems biology and systems medicine, i.e. the application of systems biology in a clinical context, is becoming of increasing importance in biology, drug discovery and health care. Systems biology incorporates knowledge and methods that are applied in mathematics, physics and engineering, but may not be part of classical training in biology. We here provide an introduction to basic concepts and methods relevant to the construction and application of systems models for apoptosis research. We present the key methods relevant to the representation of biochemical processes in signal transduction models, with a particular reference to apoptotic processes. We demonstrate how such models enable a quantitative and temporal analysis of changes in molecular entities in response to an apoptosis-inducing stimulus, and provide information on cell survival and cell death decisions. We introduce methods for analyzing the spatial propagation of cell death signals, and discuss the concepts of sensitivity analyses that enable a prediction of network responses to disturbances of single or multiple parameters. Copyright © 2013 Elsevier Inc. All rights reserved.

Sequential Injection Analysis for Optimization of Molecular Biology Reactions

PubMed Central

Allen, Peter B.; Ellington, Andrew D.

2011-01-01

In order to automate the optimization of complex biochemical and molecular biology reactions, we developed a Sequential Injection Analysis (SIA) device and combined this with a Design of Experiment (DOE) algorithm. This combination of hardware and software automatically explores the parameter space of the reaction and provides continuous feedback for optimizing reaction conditions. As an example, we optimized the endonuclease digest of a fluorogenic substrate, and showed that the optimized reaction conditions also applied to the digest of the substrate outside of the device, and to the digest of a plasmid. The sequential technique quickly arrived at optimized reaction conditions with less reagent use than a batch process (such as a fluid handling robot exploring multiple reaction conditions in parallel) would have. The device and method should now be amenable to much more complex molecular biology reactions whose variable spaces are correspondingly larger. PMID:21338059

Histogram analysis parameters identify multiple associations between DWI and DCE MRI in head and neck squamous cell carcinoma.

PubMed

Meyer, Hans Jonas; Leifels, Leonard; Schob, Stefan; Garnov, Nikita; Surov, Alexey

2018-01-01

Nowadays, multiparametric investigations of head and neck squamous cell carcinoma (HNSCC) are established. These approaches can better characterize tumor biology and behavior. Diffusion weighted imaging (DWI) can by means of apparent diffusion coefficient (ADC) quantitatively characterize different tissue compartments. Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) reflects perfusion and vascularization of tissues. Recently, a novel approach of data acquisition, namely histogram analysis of different images is a novel diagnostic approach, which can provide more information of tissue heterogeneity. The purpose of this study was to analyze possible associations between DWI, and DCE parameters derived from histogram analysis in patients with HNSCC. Overall, 34 patients, 9 women and 25 men, mean age, 56.7±10.2years, with different HNSCC were involved in the study. DWI was obtained by using of an axial echo planar imaging sequence with b-values of 0 and 800s/mm 2 . Dynamic T1w DCE sequence after intravenous application of contrast medium was performed for estimation of the following perfusion parameters: volume transfer constant (K trans ), volume of the extravascular extracellular leakage space (Ve), and diffusion of contrast medium from the extravascular extracellular leakage space back to the plasma (Kep). Both ADC and perfusion parameters maps were processed offline in DICOM format with custom-made Matlab-based application. Thereafter, polygonal ROIs were manually drawn on the transferred maps on each slice. For every parameter, mean, maximal, minimal, and median values, as well percentiles 10th, 25th, 75th, 90th, kurtosis, skewness, and entropy were estimated. Сorrelation analysis identified multiple statistically significant correlations between the investigated parameters. Ve related parameters correlated well with different ADC values. Especially, percentiles 10 and 75, mode, and median values showed stronger correlations in comparison to other parameters. Thereby, the calculated correlation coefficients ranged from 0.62 to 0.69. Furthermore, K trans related parameters showed multiple slightly to moderate significant correlations with different ADC values. Strongest correlations were identified between ADC P75 and K trans min (p=0.58, P=0.0007), and ADC P75 and K trans P10 (p=0.56, P=0.001). Only four K ep related parameters correlated statistically significant with ADC fractions. Strongest correlation was found between K ep max and ADC mode (p=-0.47, P=0.008). Multiple statistically significant correlations between, DWI and DCE MRI parameters derived from histogram analysis were identified in HNSCC. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparison of the protein-coding genomes of three deep-sea, sulfur-oxidising bacteria: "Candidatus Ruthia magnifica", "Candidatus Vesicomyosocius okutanii" and Thiomicrospira crunogena.

PubMed

McGill, Susan E; Barker, Daniel

2017-07-20

" Candidatus Ruthia magnifica", "Candidatus Vesicomyosocius okutanii" and Thiomicrospira crunogena are all sulfur-oxidising bacteria found in deep-sea vent environments. Recent research suggests that the two symbiotic organisms, "Candidatus R. magnifica" and "Candidatus V. okutanii", may share common ancestry with the autonomously living species T. crunogena. We used comparative genomics to examine the genome-wide protein-coding content of all three species to explore their similarities. In particular, we used the OrthoMCL algorithm to sort proteins into groups of putative orthologs on the basis of sequence similarity. The OrthoMCL inflation parameter was tuned using biological criteria. Using the tuned value, OrthoMCL delimited 1070 protein groups. 63.5% of these groups contained one protein from each species. Two groups contained duplicate protein copies from all three species. 123 groups were unique to T. crunogena and ten groups included multiple copies of T. crunogena proteins but only single copies from the other species. "Candidatus R. magnifica" had one unique group, and had multiple copies in one group where the other species had a single copy. There were no groups unique to "Candidatus V. okutanii", and no groups in which there were multiple "Candidatus V. okutanii" proteins but only single proteins from the other species. Results align with previous suggestions that all three species share a common ancestor. However this is not definitive evidence to make taxonomic conclusions and the possibility of horizontal gene transfer was not investigated. Methodologically, the tuning of the OrthoMCL inflation parameter using biological criteria provides further methods to refine the OrthoMCL procedure.

Determining the optimal number of independent components for reproducible transcriptomic data analysis.

PubMed

Kairov, Ulykbek; Cantini, Laura; Greco, Alessandro; Molkenov, Askhat; Czerwinska, Urszula; Barillot, Emmanuel; Zinovyev, Andrei

2017-09-11

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

Using LabView for real-time monitoring and tracking of multiple biological objects

NASA Astrophysics Data System (ADS)

Nikolskyy, Aleksandr I.; Krasilenko, Vladimir G.; Bilynsky, Yosyp Y.; Starovier, Anzhelika

2017-04-01

Today real-time studying and tracking of movement dynamics of various biological objects is important and widely researched. Features of objects, conditions of their visualization and model parameters strongly influence the choice of optimal methods and algorithms for a specific task. Therefore, to automate the processes of adaptation of recognition tracking algorithms, several Labview project trackers are considered in the article. Projects allow changing templates for training and retraining the system quickly. They adapt to the speed of objects and statistical characteristics of noise in images. New functions of comparison of images or their features, descriptors and pre-processing methods will be discussed. The experiments carried out to test the trackers on real video files will be presented and analyzed.

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.

An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.

PubMed

Scott, Finlay; Jardim, Ernesto; Millar, Colin P; Cerviño, Santiago

2016-01-01

Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Ocean acidification in the coastal zone from an organism's perspective: multiple system parameters, frequency domains, and habitats.

PubMed

Waldbusser, George G; Salisbury, Joseph E

2014-01-01

Multiple natural and anthropogenic processes alter the carbonate chemistry of the coastal zone in ways that either exacerbate or mitigate ocean acidification effects. Freshwater inputs and multiple acid-base reactions change carbonate chemistry conditions, sometimes synergistically. The shallow nature of these systems results in strong benthic-pelagic coupling, and marine invertebrates at different life history stages rely on both benthic and pelagic habitats. Carbonate chemistry in coastal systems can be highly variable, responding to processes with temporal modes ranging from seconds to centuries. Identifying scales of variability relevant to levels of biological organization requires a fuller characterization of both the frequency and magnitude domains of processes contributing to or reducing acidification in pelagic and benthic habitats. We review the processes that contribute to coastal acidification with attention to timescales of variability and habitats relevant to marine bivalves.

Concanavalin-A conjugated fine-multiple emulsion loaded with 6-mercaptopurine.

PubMed

Khopade, A J; Jain, N K

2000-01-01

Fine-multiple (water-in-oil-in-water) emulsions were prepared by two-step emulsification using sonication. They were coated with concanavalin-A (Con-A) by three methods. The one involving covalent coupling of Con-A to the multiple emulsion incorporated anchor was better compared with lipid derivatized Con-A anchoring or the glutaraldehyde-based cross-linking method, as shown by the faster rate of dextran-induced aggregation. The selected multiple emulsions were characterized by physical properties such as droplet size, encapsulation efficiency, and zeta potential. Stability parameters such as droplet size, creaming, leakage, and aggregation as a function of relative turbidity were monitored over a 1-month period, which revealed good stability of the formulations. The release profile of 6-mercaptopurine followed zero-order kinetics. Pharmacokinetic studies showed an increase in half-life and bioavailability from multiple emulsion formulations administered intravenously. There was prolonged retention of drug in various tissues of rats when treated with Con-A-coated multiple emulsion as compared with uncoated one. Our study demonstrates the suitability of fine-multiple emulsion for intravenous administration and the potential for prolonged retention of drugs and targeting in biological systems.

Slide Set: Reproducible image analysis and batch processing with ImageJ.

PubMed

Nanes, Benjamin A

2015-11-01

Most imaging studies in the biological sciences rely on analyses that are relatively simple. However, manual repetition of analysis tasks across multiple regions in many images can complicate even the simplest analysis, making record keeping difficult, increasing the potential for error, and limiting reproducibility. While fully automated solutions are necessary for very large data sets, they are sometimes impractical for the small- and medium-sized data sets common in biology. Here we present the Slide Set plugin for ImageJ, which provides a framework for reproducible image analysis and batch processing. Slide Set organizes data into tables, associating image files with regions of interest and other relevant information. Analysis commands are automatically repeated over each image in the data set, and multiple commands can be chained together for more complex analysis tasks. All analysis parameters are saved, ensuring transparency and reproducibility. Slide Set includes a variety of built-in analysis commands and can be easily extended to automate other ImageJ plugins, reducing the manual repetition of image analysis without the set-up effort or programming expertise required for a fully automated solution.

Simulating the Incorporation of Geochemical Proxies into Scleractinian Coral Skeletons: Effects of Different Environmental and Biological Factors and Implications for Paleo-reconstruction

NASA Astrophysics Data System (ADS)

Guo, W.

2017-12-01

Chemical and isotopic compositions of scleractinian coral skeletons reflect the physicochemical condition of the seawater in which corals grow. This makes coral skeleton one of the best archives of ocean climate and biogeochemical changes. A number of coral-based geochemical proxies have been developed and applied to reconstruct past seawater conditions, such as temperature, pH, carbonate chemistry and nutrient concentrations. Detailed laboratory and field-based studies of these proxies, however, indicate interpretation of the geochemistry of coral skeletons is not straightforward, due to the presence of `vital effects' and the variations of empirical proxy calibrations among and within different species. This poses challenges for the broad application of many geochemical proxies in corals, and highlights the need to better understand the fundamental processes governing the incorporation of different proxies. Here I present a numerical model that simulates the incorporation of a suite of geochemical proxies into coral skeletons, including δ11B, Mg/Ca, Sr/Ca, U/Ca, B/Ca and Ba/Ca. This model, building on previous theoretical studies of coral calcification, combines our current understanding of coral calcification mechanism with experimental constraints on the isotope and element partition during carbonate precipitation. It enables quantitative evaluation of the effects of different environmental and biological factors on each proxy. Specifically, this model shows that (1) the incorporation of every proxy is affected by multiple seawater parameters (e.g. temperature, pH, DIC) as opposed to one single parameter, and (2) biological factors, particularly the interplay between enzymatic alkalinity pumping and the exchange of coral calcifying fluid with external seawater, also exert significant controls. Based on these findings, I propose an inverse method for simultaneously reconstructing multiple seawater physicochemical parameters, and compare the performance of this new method with conventional paleo-reconstruction methods that are based on empirical calibrations. In addition, the extension of this model to simulate carbon, oxygen and clumped isotope (δ13C, δ18O, Δ47) composition of coral skeletons will also be discussed at the meeting.

Mechanical Fluidity of Fully Suspended Biological Cells

PubMed Central

Maloney, John M.; Lehnhardt, Eric; Long, Alexandra F.; Van Vliet, Krystyn J.

2013-01-01

Mechanical characteristics of single biological cells are used to identify and possibly leverage interesting differences among cells or cell populations. Fluidity—hysteresivity normalized to the extremes of an elastic solid or a viscous liquid—can be extracted from, and compared among, multiple rheological measurements of cells: creep compliance versus time, complex modulus versus frequency, and phase lag versus frequency. With multiple strategies available for acquisition of this nondimensional property, fluidity may serve as a useful and robust parameter for distinguishing cell populations, and for understanding the physical origins of deformability in soft matter. Here, for three disparate eukaryotic cell types deformed in the suspended state via optical stretching, we examine the dependence of fluidity on chemical and environmental influences at a timescale of ∼1 s. We find that fluidity estimates are consistent in the time and frequency domains under a structural damping (power-law or fractional-derivative) model, but not under an equivalent-complexity, lumped-component (spring-dashpot) model; the latter predicts spurious time constants. Although fluidity is suppressed by chemical cross-linking, we find that ATP depletion in the cell does not measurably alter the parameter, and we thus conclude that active ATP-driven events are not a crucial enabler of fluidity during linear viscoelastic deformation of a suspended cell. Finally, by using the capacity of optical stretching to produce near-instantaneous increases in cell temperature, we establish that fluidity increases with temperature—now measured in a fully suspended, sortable cell without the complicating factor of cell-substratum adhesion. PMID:24138852

Biogeochemical and hydrological constraints on concentration-discharge curves

NASA Astrophysics Data System (ADS)

Moatar, Florentina; Abbott, Ben; Minaudo, Camille; Curie, Florence; Pinay, Gilles

2017-04-01

The relationship between concentration and discharge (C-Q) can give insight into the location, abundance, rate of production or consumption, and transport dynamics of elements in coupled terrestrial-aquatic ecosystems. Consequently, the investigation of C-Q relationships for multiple elements at multiple spatial and temporal scales can be a powerful tool to address three of ecohydrology's fundamental questions: where does water comes from, how long does it stay, and what happens to the solutes and particulates it carries along the way. We analyzed long-term water quality data from 300 monitoring stations covering nearly half of France to investigate how elemental properties, catchment characteristics, and hydrological parameters influence C-Q. Based on previous work, we segmented the hydrograph, calculating independent C-Q slopes for flows above and below the median discharge. We found that most elements only expressed two of the nine possible C-Q modalities, indicating strong elemental control of C-Q shape. Catchment characteristics including land use and human population had a strong impact on concentration but typically did not influence the C-Q slopes, also suggesting inherent constraints on elemental production and transport. Biological processes appeared to regulate C-Q slope at low flows for biologically-reactive elements, but at high flows, these processes became unimportant, and most parameters expressed chemostatic behavior. This study provides a robust description of possible C-Q shapes for a wide variety of catchments and elements and demonstrates the value of low-frequency, long-term data collected by water quality agencies.

Quantitative analysis of three-dimensional biological cells using interferometric microscopy

NASA Astrophysics Data System (ADS)

Shaked, Natan T.; Wax, Adam

2011-06-01

Live biological cells are three-dimensional microscopic objects that constantly adjust their sizes, shapes and other biophysical features. Wide-field digital interferometry (WFDI) is a holographic technique that is able to record the complex wavefront of the light which has interacted with in-vitro cells in a single camera exposure, where no exogenous contrast agents are required. However, simple quasi-three-dimensional holographic visualization of the cell phase profiles need not be the end of the process. Quantitative analysis should permit extraction of numerical parameters which are useful for cytology or medical diagnosis. Using a transmission-mode setup, the phase profile represents the multiplication between the integral refractive index and the thickness of the sample. These coupled variables may not be distinct when acquiring the phase profiles of dynamic cells. Many morphological parameters which are useful for cell biologists are based on the cell thickness profile rather than on its phase profile. We first overview methods to decouple the cell thickness and its refractive index using the WFDI-based phase profile. Then, we present a whole-cell-imaging approach which is able to extract useful numerical parameters on the cells even in cases where decoupling of cell thickness and refractive index is not possible or desired.

Biological oxygen apparent transmissibility of hydrogel contact lenses with and without organosilicon moieties.

PubMed

Compañ, V; López-Alemany, A; Riande, E; Refojo, M F

2004-01-01

The instrument oxygen transmissibility (IOT) of organosilicon hydrogels, measured by electrochemical procedures, is 5-10 times larger than that of conventional hydrogels. A method is described that allows the estimation of the oxygen tension at the lens-cornea interface for closed- and open-eyelids situations by combining the IOT of the hydrogels and corneal parameters such as corneal thickness, corneal permeability and oxygen flux across the cornea. From these results the biological oxygen apparent transmissibility (BOAT) is obtained, an important parameter which an multiplication with the pressure of oxygen on the external part of the lens gives the oxygen flux onto the cornea. Contact lenses with oxygen transmissibility higher than 100 Dk/t units [1 Dk/t unit=10(-9) [cm(3) O(2) (STp) cm(-2)s(-1)(mmHg)(-1)] posses a large oxygen tension at the lens-cornea interface that substantially reduces the oxygen flux onto the cornea. Lenses whose oxygen transmissibility is lower than 50 Dk/t units allow a rather small oxygen flux onto the cornea under closed eyelids condition that prevent their use for extended wear.

Spontaneous switching of frequency-locking by periodic stimulus in oscillators of plasmodium of the true slime mold.

PubMed

Takamatsu, A; Yamamoto, T; Fujii, T

2004-01-01

Microfabrication technique was used to construct a model system with a living cell of plasmodium of the true slime mold, Physarum polycephalum, a living coupled oscillator system. Its parameters can be systematically controlled as in computer simulations, so that results are directly comparable to those of general mathematical models. As the first step, we investigated responses in oscillatory cells, the oscillators of the plasmodium, to periodic stimuli by temperature changes to elucidate characteristics of the cells as nonlinear systems whose internal dynamics are unknown because of their complexity. We observed that the forced oscillator of the plasmodium show 1:1, 2:1, 3:1 frequency locking inside so-called Arnold tongues regions as well as in other nonlinear systems such as chemical systems and other biological systems. In addition, we found spontaneous switching behavior from certain frequency locking states to other states, even under certain fixed parameters. This technique can be applied to more complex systems with multiple elements, such as coupled oscillator systems, and would be useful to investigate complicated phenomena in biological systems such as information processing.

Assessment of the factors contributing to the variations in microcystins biodegradability of the biofilms on a practical biological treatment facility.

PubMed

Li, Jieming; Shimizu, Kazuya; Akasako, Haruna; Lu, Zhijiang; Akiyama, Shohei; Goto, Masafumi; Utsumi, Motoo; Sugiura, Norio

2015-01-01

This study revealed the biotic and abiotic parameters driving the variations in microcystins (MCs) biodegradability of a practical biological treatment facility (BTF). Results showed that similar trends of seasonal variation were seen for microcystin-LR (MCLR) biodegradability of biofilms on the BTF and indigenous MCLR-degrader population, where both peaks co-occurred in October, following the peaks of natural MCLR concentration and water temperature observed in August. The lag period might be required for accumulation of MCLR-degraders and MCLR-degrading enzyme activity. The MCLR-degrader population was correlated to temperature, MCLR and chlorophyll-a concentration in water where the biofilms submerged, indicating that these abiotic and biotic parameters exerted direct and/or indirect influences on seasonal variation in MCLR-biodegradability. In comparison, no effect of other co-existing MCs on biodegradation of one MC was observed. However, proliferation of MC-degraders along biodegradation processes positively responded to total amount of MCs, suggesting that multiple MCs contributed additively to MC-degrader proliferation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multiple Replica Repulsion Technique for Efficient Conformational Sampling of Biological Systems

PubMed Central

Malevanets, Anatoly; Wodak, Shoshana J.

2011-01-01

Here, we propose a technique for sampling complex molecular systems with many degrees of freedom. The technique, termed “multiple replica repulsion” (MRR), does not suffer from poor scaling with the number of degrees of freedom associated with common replica exchange procedures and does not require sampling at high temperatures. The algorithm involves creation of multiple copies (replicas) of the system, which interact with one another through a repulsive potential that can be applied to the system as a whole or to portions of it. The proposed scheme prevents oversampling of the most populated states and provides accurate descriptions of conformational perturbations typically associated with sampling ground-state energy wells. The performance of MRR is illustrated for three systems of increasing complexity. A two-dimensional toy potential surface is used to probe the sampling efficiency as a function of key parameters of the procedure. MRR simulations of the Met-enkephalin pentapeptide, and the 76-residue protein ubiquitin, performed in presence of explicit water molecules and totaling 32 ns each, investigate the ability of MRR to characterize the conformational landscape of the peptide, and the protein native basin, respectively. Results obtained for the enkephalin peptide reflect more closely the extensive conformational flexibility of this peptide than previously reported simulations. Those obtained for ubiquitin show that conformational ensembles sampled by MRR largely encompass structural fluctuations relevant to biological recognition, which occur on the microsecond timescale, or are observed in crystal structures of ubiquitin complexes with other proteins. MRR thus emerges as a very promising simple and versatile technique for modeling the structural plasticity of complex biological systems. PMID:21843487

Urinary inorganic arsenic concentrations and semen quality of male partners of subfertile couples in Tokyo.

PubMed

Oguri, Tomoko; Yoshinaga, Jun; Toshima, Hiroki; Mizumoto, Yoshifumi; Hatakeyama, Shota; Tokuoka, Susumu

2016-01-01

Inorganic arsenic (iAs) has been known as a testicular toxicant in experimental rodents. Possible association between iAs exposure and semen quality (semen volume, sperm concentration, and sperm motility) was explored in male partners of couples (n = 42) who visited a gynecology clinic in Tokyo for infertility consultation. Semen parameters were measured according to WHO guideline at the clinic, and urinary iAs and methylarsonic acid (MMA), and dimethylarsinic acid concentrations were determined by liquid chromatography-hydride generation-ICP mass spectrometry. Biological attributes, dietary habits, and exposure levels to other chemicals with known effects on semen parameters were taken into consideration as covariates. Multiple regression analyses and logistic regression analyses did not find iAs exposure as significant contributor to semen parameters. Lower exposure level of subjects (estimated to be 0.5 μg kg(-1) day(-1)) was considered a reason of the absence of adverse effects on semen parameters, which were seen in rodents dosed with 4-7.5 mg kg(-1).

[The overall assessment of psychological well - being of patients with multiple sclerosis after the application of physical therapy. Part 2].

PubMed

Kubsik-Gidlewska, Anna; Klimkiewicz, Robert; Klimkiewicz, Paulina; Janczewska, Katarzyna; Jankowska, Agnieszka; Nowakowski, Tomasz; Woldańska-Okońska, Marta

2017-01-01

Multiple sclerosis is a chronic demyelinating disease of the central nervous system, which results a progressive disability. The disease reduces the quality of life of patients, changes the general health perceptions, and also limits performing social roles because of emotional problems. Evaluation of the impact of the methods of rehabilitation to improve the mental health of patients with multiple sclerosis, and also to change individual parameters included in the overall assessment of mental health. The study was conducted in 2010-2014 at the Department of Physical Medicine and Rehabilitation in Lodz. The study included 120 patients with multiple sclerosis. Patients were classified into 4 test groups: in the first was used the laser, in the second - laser and magnetostimulation, in the third - kinesiotherapy, and in the fourth - magnetostimulation. The tests were carried out three times. To evaluate the quality of life was used Quality of Life Questionnaire (MSQOL-54), analyzed the overall assessment of mental health. The improvement in a range of parameters, an overall assessment of the quality of mental health has allowed to get a better overall psychological well-being. ,There was oserved a statistically significant difference at the level of p<0.001 between groups in 4/5 investigated parameters, statistically significant differences weren't obserwed at the evaluation of cognitive functions. The greatest improvement was observed in Group II and Group IV. In the examination it was confirmed an effectiveness of physical treatment, such a the laser radiation and magnetostimulation. Synergism of both methods in their biological activity, allows for evoke of hysteresis fenomenon, resulting in the maintenance of the treatment effects after cessation of rehabilitation. Applying the classical kinesiotherapy only doesn't allow to get long-term effects.

Modular electron transfer circuits for synthetic biology

PubMed Central

Agapakis, Christina M

2010-01-01

Electron transfer is central to a wide range of essential metabolic pathways, from photosynthesis to fermentation. The evolutionary diversity and conservation of proteins that transfer electrons makes these pathways a valuable platform for engineered metabolic circuits in synthetic biology. Rational engineering of electron transfer pathways containing hydrogenases has the potential to lead to industrial scale production of hydrogen as an alternative source of clean fuel and experimental assays for understanding the complex interactions of multiple electron transfer proteins in vivo. We designed and implemented a synthetic hydrogen metabolism circuit in Escherichia coli that creates an electron transfer pathway both orthogonal to and integrated within existing metabolism. The design of such modular electron transfer circuits allows for facile characterization of in vivo system parameters with applications toward further engineering for alternative energy production. PMID:21468209

A Systems' Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

PubMed Central

Kunz, Manfred; Vera, Julio; Wolkenhauer, Olaf

2013-01-01

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts. PMID:24350286

Estimation of kinetic parameters from list-mode data using an indirect apporach

NASA Astrophysics Data System (ADS)

Ortiz, Joseph Christian

This dissertation explores the possibility of using an imaging approach to model classical pharmacokinetic (PK) problems. The kinetic parameters which describe the uptake rates of a drug within a biological system, are parameters of interest. Knowledge of the drug uptake in a system is useful in expediting the drug development process, as well as providing a dosage regimen for patients. Traditionally, the uptake rate of a drug in a system is obtained via sampling the concentration of the drug in a central compartment, usually the blood, and fitting the data to a curve. In a system consisting of multiple compartments, the number of kinetic parameters is proportional to the number of compartments, and in classical PK experiments, the number of identifiable parameters is less than the total number of parameters. Using an imaging approach to model classical PK problems, the support region of each compartment within the system will be exactly known, and all the kinetic parameters are uniquely identifiable. To solve for the kinetic parameters, an indirect approach, which is a two part process, was used. First the compartmental activity was obtained from data, and next the kinetic parameters were estimated. The novel aspect of the research is using listmode data to obtain the activity curves from a system as opposed to a traditional binned approach. Using techniques from information theoretic learning, particularly kernel density estimation, a non-parametric probability density function for the voltage outputs on each photo-multiplier tube, for each event, was generated on the fly, which was used in a least squares optimization routine to estimate the compartmental activity. The estimability of the activity curves for varying noise levels as well as time sample densities were explored. Once an estimate for the activity was obtained, the kinetic parameters were obtained using multiple cost functions, and the compared to each other using the mean squared error as the figure of merit.

Topsoil structure stability in a restored floodplain: Impacts of fluctuating water levels, soil parameters and ecosystem engineers.

PubMed

Schomburg, A; Schilling, O S; Guenat, C; Schirmer, M; Le Bayon, R C; Brunner, P

2018-10-15

Ecosystem services provided by floodplains are strongly controlled by the structural stability of soils. The development of a stable structure in floodplain soils is affected by a complex and poorly understood interplay of hydrological, physico-chemical and biological processes. This paper aims at analysing relations between fluctuating groundwater levels, soil physico-chemical and biological parameters on soil structure stability in a restored floodplain. Water level fluctuations in the soil are modelled using a numerical surface-water-groundwater flow model and correlated to soil physico-chemical parameters and abundances of plants and earthworms. Causal relations and multiple interactions between the investigated parameters are tested through structural equation modelling (SEM). Fluctuating water levels in the soil did not directly affect the topsoil structure stability, but indirectly through affecting plant roots and soil parameters that in turn determine topsoil structure stability. These relations remain significant for mean annual days of complete and partial (>25%) water saturation. Ecosystem functioning of a restored floodplain might already be affected by the fluctuation of groundwater levels alone, and not only through complete flooding by surface water during a flood period. Surprisingly, abundances of earthworms did not show any relation to other variables in the SEM. These findings emphasise that earthworms have efficiently adapted to periodic stress and harsh environmental conditions. Variability of the topsoil structure stability is thus stronger driven by the influence of fluctuating water levels on plants than by the abundance of earthworms. This knowledge about the functional network of soil engineering organisms, soil parameters and fluctuating water levels and how they affect soil structural stability is of fundamental importance to define management strategies of near-natural or restored floodplains in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

A new approach to estimate parameters of speciation models with application to apes.

PubMed

Becquet, Celine; Przeworski, Molly

2007-10-01

How populations diverge and give rise to distinct species remains a fundamental question in evolutionary biology, with important implications for a wide range of fields, from conservation genetics to human evolution. A promising approach is to estimate parameters of simple speciation models using polymorphism data from multiple loci. Existing methods, however, make a number of assumptions that severely limit their applicability, notably, no gene flow after the populations split and no intralocus recombination. To overcome these limitations, we developed a new Markov chain Monte Carlo method to estimate parameters of an isolation-migration model. The approach uses summaries of polymorphism data at multiple loci surveyed in a pair of diverging populations or closely related species and, importantly, allows for intralocus recombination. To illustrate its potential, we applied it to extensive polymorphism data from populations and species of apes, whose demographic histories are largely unknown. The isolation-migration model appears to provide a reasonable fit to the data. It suggests that the two chimpanzee species became reproductively isolated in allopatry approximately 850 Kya, while Western and Central chimpanzee populations split approximately 440 Kya but continued to exchange migrants. Similarly, Eastern and Western gorillas and Sumatran and Bornean orangutans appear to have experienced gene flow since their splits approximately 90 and over 250 Kya, respectively.

Low Dose Radiation Cancer Risks: Epidemiological and Toxicological Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

David G. Hoel, PhD

2012-04-19

The basic purpose of this one year research grant was to extend the two stage clonal expansion model (TSCE) of carcinogenesis to exposures other than the usual single acute exposure. The two-stage clonal expansion model of carcinogenesis incorporates the biological process of carcinogenesis, which involves two mutations and the clonal proliferation of the intermediate cells, in a stochastic, mathematical way. The current TSCE model serves a general purpose of acute exposure models but requires numerical computation of both the survival and hazard functions. The primary objective of this research project was to develop the analytical expressions for the survival functionmore » and the hazard function of the occurrence of the first cancer cell for acute, continuous and multiple exposure cases within the framework of the piece-wise constant parameter two-stage clonal expansion model of carcinogenesis. For acute exposure and multiple exposures of acute series, it is either only allowed to have the first mutation rate vary with the dose, or to have all the parameters be dose dependent; for multiple exposures of continuous exposures, all the parameters are allowed to vary with the dose. With these analytical functions, it becomes easy to evaluate the risks of cancer and allows one to deal with the various exposure patterns in cancer risk assessment. A second objective was to apply the TSCE model with varing continuous exposures from the cancer studies of inhaled plutonium in beagle dogs. Using step functions to estimate the retention functions of the pulmonary exposure of plutonium the multiple exposure versions of the TSCE model was to be used to estimate the beagle dog lung cancer risks. The mathematical equations of the multiple exposure versions of the TSCE model were developed. A draft manuscript which is attached provides the results of this mathematical work. The application work using the beagle dog data from plutonium exposure has not been completed due to the fact that the research project did not continue beyond its first year.« less

Balance between apical membrane growth and luminal matrix resistance determines epithelial tubule shape.

PubMed

Dong, Bo; Hannezo, Edouard; Hayashi, Shigeo

2014-05-22

The morphological stability of biological tubes is crucial for the efficient circulation of fluids and gases. Failure of this stability causes irregularly shaped tubes found in multiple pathological conditions. Here, we report that Drosophila mutants of the ESCRT III component Shrub/Vps32 exhibit a strikingly elongated sinusoidal tube phenotype. This is caused by excessive apical membrane synthesis accompanied by the ectopic accumulation and overactivation of Crumbs in swollen endosomes. Furthermore, we demonstrate that the apical extracellular matrix (aECM) of the tracheal tube is a viscoelastic material coupled with the apical membrane. We present a simple mechanical model in which aECM elasticity, apical membrane growth, and their interaction are three vital parameters determining the stability of biological tubes. Our findings demonstrate a mechanical role for the extracellular matrix and suggest that the interaction of the apical membrane and an elastic aECM determines the final morphology of biological tubes independent of cell shape. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Functional Imaging Biomarkers: Potential to Guide an Individualised Approach to Radiotherapy.

PubMed

Prestwich, R J D; Vaidyanathan, S; Scarsbrook, A F

2015-10-01

The identification of robust prognostic and predictive biomarkers would transform the ability to implement an individualised approach to radiotherapy. In this regard, there has been a surge of interest in the use of functional imaging to assess key underlying biological processes within tumours and their response to therapy. Importantly, functional imaging biomarkers hold the potential to evaluate tumour heterogeneity/biology both spatially and temporally. An ever-increasing range of functional imaging techniques is now available primarily involving positron emission tomography and magnetic resonance imaging. Small-scale studies across multiple tumour types have consistently been able to correlate changes in functional imaging parameters during radiotherapy with disease outcomes. Considerable challenges remain before the implementation of functional imaging biomarkers into routine clinical practice, including the inherent temporal variability of biological processes within tumours, reproducibility of imaging, determination of optimal imaging technique/combinations, timing during treatment and design of appropriate validation studies. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Multiple expression patterns of biopathological markers in primary invasive breast carcinoma: a useful tool for elucidating its biological behaviour.

PubMed

Ceccarelli, C; Santini, D; Chieco, P; Taffurelli, M; Marrano, D; Mancini, A M

1995-03-01

Commonly used clinical and morphologic criteria have been reported to be of limited value in predicting the outcome of malignant tumours of the breast. Integrated information from the quantitative analysis in tumour tissue of biological parameters such as oestrogen and progesterone receptors (ER and PGR), proliferative activity, and proto-oncogene p53, c-erB2, and bcl-2 expression, may be useful for defining the biology of growth of breast carcinoma and to plan effective therapeutic strategies. Immunohistochemistry with antibodies recognizing ER, PGR, Ki-67, and the p53, c-erbB2, and bcl-2 encoded proteins was performed on 291 primary breast carcinomas. Results were integrated with clinico-pathological indicators and examined with multivariate statistical procedures and modeling. P53, c-erbB2, and bcl-2 gene products were detected, respectively, in 30.6%, 31.6%, and 85.9% of the examined invasive breast carcinomas, revealing variable associations with cellular differentiation and proliferation as defined by ER/PGR status, Ki-67, tumour mass and histologic and nuclear grading. A multivariate graphical display on a subset of the most informative cases revealed that bcl-2 expression parallels ER/PGR status and is of importance in separating tumour clusters with different degrees of aggressiveness. The results of this study indicate that multivariate explorative analyses conducted on biological and clinico-pathological parameters might constitute an integrated approach to data analysis useful for distinguishing different biological behaviours and therapeutic groups in breast carcinoma. Our findings also suggest that bcl-2 expression may play a pivotal role in tumours lacking ER-mediated growth regulation.

Engineering challenges of BioNEMS: the integration of microfluidics, micro- and nanodevices, models and external control for systems biology.

PubMed

Wikswo, J P; Prokop, A; Baudenbacher, F; Cliffel, D; Csukas, B; Velkovsky, M

2006-08-01

Systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models that may eventually have 10(6) dynamic variables with non-linear interactions. Historically, single biological measurements are made over minutes, suggesting the challenge of specifying 10(6) model parameters. Except for fluorescence and micro-electrode recordings, most cellular measurements have inadequate bandwidth to discern the time course of critical intracellular biochemical events. Micro-array expression profiles of thousands of genes cannot determine quantitative dynamic cellular signalling and metabolic variables. Major gaps must be bridged between the computational vision and experimental reality. The analysis of cellular signalling dynamics and control requires, first, micro- and nano-instruments that measure simultaneously multiple extracellular and intracellular variables with sufficient bandwidth; secondly, the ability to open existing internal control and signalling loops; thirdly, external BioMEMS micro-actuators that provide high bandwidth feedback and externally addressable intracellular nano-actuators; and, fourthly, real-time, closed-loop, single-cell control algorithms. The unravelling of the nested and coupled nature of cellular control loops requires simultaneous recording of multiple single-cell signatures. Externally controlled nano-actuators, needed to effect changes in the biochemical, mechanical and electrical environment both outside and inside the cell, will provide a major impetus for nanoscience.

A molecular informatics view on best practice in multi-parameter compound optimization.

PubMed

Lusher, Scott J; McGuire, Ross; Azevedo, Rita; Boiten, Jan-Willem; van Schaik, Rene C; de Vlieg, Jacob

2011-07-01

The difference between biologically active molecules and drugs is that the latter balance an array of related and unrelated properties required for administration to patients. Inevitability, during optimization, some of these multiple factors will conflict. Although informatics has a crucial role in addressing the challenges of modern compound optimization, it is arguably still undervalued and underutilized. We present here some of the basic requirements of multi-parameter drug design, the crucial role of informatics and examples of favorable practice. The most crucial of these best practices are the need for informaticians to align their technologies and insights directly to discovery projects and for all scientists in drug discovery to become more proficient in the use of in silico methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Bistability and delay-induced stability switches in a cancer network with the regulation of microRNA

NASA Astrophysics Data System (ADS)

Song, Yongli; Cao, Xin; Zhang, Tonghua

2018-01-01

In this paper, we are concerned with a cancer network including a protein module and a corresponding microRNA cluster that inhibits the synthesis of proteins. The existence of multiple steady states and their stability depending on the parameters are firstly determined. Bistability and dependency on the parameters, Hopf bifurcations and the corresponding properties like direction and stability of Hopf bifurcations are determined by computing the normal form on the center manifold. Then, the role of the delay in the process of synthesis of the protein is investigated. We show that the delay can stabilize the unstable equilibrium and destabilize the stable equilibrium. Some simulations are carried out to numerically illustrate the obtained theoretical results. Finally, the biological interpretation of the theoretical results is discussed.

Bayesian parameter estimation for nonlinear modelling of biological pathways.

PubMed

Ghasemi, Omid; Lindsey, Merry L; Yang, Tianyi; Nguyen, Nguyen; Huang, Yufei; Jin, Yu-Fang

2011-01-01

The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC) method. We applied this approach to the biological pathways involved in the left ventricle (LV) response to myocardial infarction (MI) and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly parameterized dynamic systems. Our proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models for biological pathways. This method can be further extended to high order systems and thus provides a useful tool to analyze biological dynamics and extract information using temporal data.

Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions.

PubMed

Morris, Melody K; Shriver, Zachary; Sasisekharan, Ram; Lauffenburger, Douglas A

2012-03-01

Mathematical models have substantially improved our ability to predict the response of a complex biological system to perturbation, but their use is typically limited by difficulties in specifying model topology and parameter values. Additionally, incorporating entities across different biological scales ranging from molecular to organismal in the same model is not trivial. Here, we present a framework called "querying quantitative logic models" (Q2LM) for building and asking questions of constrained fuzzy logic (cFL) models. cFL is a recently developed modeling formalism that uses logic gates to describe influences among entities, with transfer functions to describe quantitative dependencies. Q2LM does not rely on dedicated data to train the parameters of the transfer functions, and it permits straight-forward incorporation of entities at multiple biological scales. The Q2LM framework can be employed to ask questions such as: Which therapeutic perturbations accomplish a designated goal, and under what environmental conditions will these perturbations be effective? We demonstrate the utility of this framework for generating testable hypotheses in two examples: (i) a intracellular signaling network model; and (ii) a model for pharmacokinetics and pharmacodynamics of cell-cytokine interactions; in the latter, we validate hypotheses concerning molecular design of granulocyte colony stimulating factor. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

PubMed

Cankorur-Cetinkaya, Ayca; Dias, Joao M L; Kludas, Jana; Slater, Nigel K H; Rousu, Juho; Oliver, Stephen G; Dikicioglu, Duygu

2017-06-01

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

The application of multiple biophysical cues to engineer functional neocartilage for treatment of osteoarthritis. Part II: signal transduction.

PubMed

Brady, Mariea A; Waldman, Stephen D; Ethier, C Ross

2015-02-01

The unique mechanoelectrochemical environment of cartilage has motivated researchers to investigate the effect of multiple biophysical cues, including mechanical, magnetic, and electrical stimulation, on chondrocyte biology. It is well established that biophysical stimuli promote chondrocyte proliferation, differentiation, and maturation within "biological windows" of defined dose parameters, including mode, frequency, magnitude, and duration of stimuli (see companion review Part I: Cellular Response). However, the underlying molecular mechanisms and signal transduction pathways activated in response to multiple biophysical stimuli remain to be elucidated. Understanding the mechanisms of biophysical signal transduction will deepen knowledge of tissue organogenesis, remodeling, and regeneration and aiding in the treatment of pathologies such as osteoarthritis. Further, this knowledge will provide the tissue engineer with a potent toolset to manipulate and control cell fate and subsequently develop functional replacement cartilage. The aim of this article is to review chondrocyte signal transduction pathways in response to mechanical, magnetic, and electrical cues. Signal transduction does not occur along a single pathway; rather a number of parallel pathways appear to be activated, with calcium signaling apparently common to all three types of stimuli, though there are different modes of activation. Current tissue engineering strategies, such as the development of "smart" functionalized biomaterials that enable the delivery of growth factors or integration of conjugated nanoparticles, may further benefit from targeting known signal transduction pathways in combination with external biophysical cues.

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery

PubMed Central

Valdés, Pablo A.; Kim, Anthony; Leblond, Frederic; Conde, Olga M.; Harris, Brent T.; Paulsen, Keith D.; Wilson, Brian C.; Roberts, David W.

2011-01-01

Biomarkers are indicators of biological processes and hold promise for the diagnosis and treatment of disease. Gliomas represent a heterogeneous group of brain tumors with marked intra- and inter-tumor variability. The extent of surgical resection is a significant factor influencing post-surgical recurrence and prognosis. Here, we used fluorescence and reflectance spectral signatures for in vivo quantification of multiple biomarkers during glioma surgery, with fluorescence contrast provided by exogenously-induced protoporphyrin IX (PpIX) following administration of 5-aminolevulinic acid. We performed light-transport modeling to quantify multiple biomarkers indicative of tumor biological processes, including the local concentration of PpIX and associated photoproducts, total hemoglobin concentration, oxygen saturation, and optical scattering parameters. We developed a diagnostic algorithm for intra-operative tissue delineation that accounts for the combined tumor-specific predictive capabilities of these quantitative biomarkers. Tumor tissue delineation achieved accuracies of up to 94% (specificity = 94%, sensitivity = 94%) across a range of glioma histologies beyond current state-of-the-art optical approaches, including state-of-the-art fluorescence image guidance. This multiple biomarker strategy opens the door to optical methods for surgical guidance that use quantification of well-established neoplastic processes. Future work would seek to validate the predictive power of this proof-of-concept study in a separate larger cohort of patients. PMID:22112112

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery

NASA Astrophysics Data System (ADS)

Valdés, Pablo A.; Kim, Anthony; Leblond, Frederic; Conde, Olga M.; Harris, Brent T.; Paulsen, Keith D.; Wilson, Brian C.; Roberts, David W.

2011-11-01

Biomarkers are indicators of biological processes and hold promise for the diagnosis and treatment of disease. Gliomas represent a heterogeneous group of brain tumors with marked intra- and inter-tumor variability. The extent of surgical resection is a significant factor influencing post-surgical recurrence and prognosis. Here, we used fluorescence and reflectance spectral signatures for in vivo quantification of multiple biomarkers during glioma surgery, with fluorescence contrast provided by exogenously-induced protoporphyrin IX (PpIX) following administration of 5-aminolevulinic acid. We performed light-transport modeling to quantify multiple biomarkers indicative of tumor biological processes, including the local concentration of PpIX and associated photoproducts, total hemoglobin concentration, oxygen saturation, and optical scattering parameters. We developed a diagnostic algorithm for intra-operative tissue delineation that accounts for the combined tumor-specific predictive capabilities of these quantitative biomarkers. Tumor tissue delineation achieved accuracies of up to 94% (specificity = 94%, sensitivity = 94%) across a range of glioma histologies beyond current state-of-the-art optical approaches, including state-of-the-art fluorescence image guidance. This multiple biomarker strategy opens the door to optical methods for surgical guidance that use quantification of well-established neoplastic processes. Future work would seek to validate the predictive power of this proof-of-concept study in a separate larger cohort of patients.

Father Involvement and Young, Rural African American Men's Engagement in Substance Misuse and Multiple Sexual Partnerships.

PubMed

Barton, Allen W; Kogan, Steven M; Cho, Junhan; Brown, Geoffrey L

2015-12-01

This study was designed to examine the associations of biological father and social father involvement during childhood with African American young men's development and engagement in risk behaviors. With a sample of 505 young men living in the rural South of the United States, a dual mediation model was tested in which retrospective reports of involvement from biological fathers and social fathers were linked to young men's substance misuse and multiple sexual partnerships through men's relational schemas and future expectations. Results from structural equation modeling indicated that levels of involvement from biological fathers and social fathers predicted young men's relational schemas; only biological fathers' involvement predicted future expectations. In turn, future expectations predicted levels of substance misuse, and negative relational schemas predicted multiple sexual partnerships. Biological fathers' involvement evinced significant indirect associations with young men's substance misuse and multiple sexual partnerships through both schemas and expectations; social fathers' involvement exhibited an indirect association with multiple sexual partnerships through relational schemas. Findings highlight the unique influences of biological fathers and social fathers on multiple domains of African American young men's psychosocial development that subsequently render young men more or less likely to engage in risk behaviors.

A statistical approach to quasi-extinction forecasting.

PubMed

Holmes, Elizabeth Eli; Sabo, John L; Viscido, Steven Vincent; Fagan, William Fredric

2007-12-01

Forecasting population decline to a certain critical threshold (the quasi-extinction risk) is one of the central objectives of population viability analysis (PVA), and such predictions figure prominently in the decisions of major conservation organizations. In this paper, we argue that accurate forecasting of a population's quasi-extinction risk does not necessarily require knowledge of the underlying biological mechanisms. Because of the stochastic and multiplicative nature of population growth, the ensemble behaviour of population trajectories converges to common statistical forms across a wide variety of stochastic population processes. This paper provides a theoretical basis for this argument. We show that the quasi-extinction surfaces of a variety of complex stochastic population processes (including age-structured, density-dependent and spatially structured populations) can be modelled by a simple stochastic approximation: the stochastic exponential growth process overlaid with Gaussian errors. Using simulated and real data, we show that this model can be estimated with 20-30 years of data and can provide relatively unbiased quasi-extinction risk with confidence intervals considerably smaller than (0,1). This was found to be true even for simulated data derived from some of the noisiest population processes (density-dependent feedback, species interactions and strong age-structure cycling). A key advantage of statistical models is that their parameters and the uncertainty of those parameters can be estimated from time series data using standard statistical methods. In contrast for most species of conservation concern, biologically realistic models must often be specified rather than estimated because of the limited data available for all the various parameters. Biologically realistic models will always have a prominent place in PVA for evaluating specific management options which affect a single segment of a population, a single demographic rate, or different geographic areas. However, for forecasting quasi-extinction risk, statistical models that are based on the convergent statistical properties of population processes offer many advantages over biologically realistic models.

Structured plant metabolomics for the simultaneous exploration of multiple factors.

PubMed

Vasilev, Nikolay; Boccard, Julien; Lang, Gerhard; Grömping, Ulrike; Fischer, Rainer; Goepfert, Simon; Rudaz, Serge; Schillberg, Stefan

2016-11-17

Multiple factors act simultaneously on plants to establish complex interaction networks involving nutrients, elicitors and metabolites. Metabolomics offers a better understanding of complex biological systems, but evaluating the simultaneous impact of different parameters on metabolic pathways that have many components is a challenging task. We therefore developed a novel approach that combines experimental design, untargeted metabolic profiling based on multiple chromatography systems and ionization modes, and multiblock data analysis, facilitating the systematic analysis of metabolic changes in plants caused by different factors acting at the same time. Using this method, target geraniol compounds produced in transgenic tobacco cell cultures were grouped into clusters based on their response to different factors. We hypothesized that our novel approach may provide more robust data for process optimization in plant cell cultures producing any target secondary metabolite, based on the simultaneous exploration of multiple factors rather than varying one factor each time. The suitability of our approach was verified by confirming several previously reported examples of elicitor-metabolite crosstalk. However, unravelling all factor-metabolite networks remains challenging because it requires the identification of all biochemically significant metabolites in the metabolomics dataset.

Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches.

PubMed

Farley, Kevin J; Meyer, Joseph S; Balistrieri, Laurie S; De Schamphelaere, Karel A C; Iwasaki, Yuichi; Janssen, Colin R; Kamo, Masashi; Lofts, Stephen; Mebane, Christopher A; Naito, Wataru; Ryan, Adam C; Santore, Robert C; Tipping, Edward

2015-04-01

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed. © 2014 SETAC.

Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches

USGS Publications Warehouse

Farley, Kevin J.; Meyer, Joe; Balistrieri, Laurie S.; DeSchamphelaere, Karl; Iwasaki, Yuichi; Janssen, Colin; Kamo, Masashi; Lofts, Steve; Mebane, Christopher A.; Naito, Wataru; Ryan, Adam C.; Santore, Robert C.; Tipping, Edward

2015-01-01

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the U.S. Geological Survey (USA), HDR⎪HydroQual, Inc. (USA), and the Centre for Ecology and Hydrology (UK) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME Workshop in Brussels, Belgium (May 2012), is provided herein. Overall, the models were found to be similar in structure (free ion activities computed by WHAM; specific or non-specific binding of metals/cations in or on the organism; specification of metal potency factors and/or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single versus multiple types of binding site on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong inter-relationships among the model parameters (log KM values, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

A global parallel model based design of experiments method to minimize model output uncertainty.

PubMed

Bazil, Jason N; Buzzard, Gregory T; Rundell, Ann E

2012-03-01

Model-based experiment design specifies the data to be collected that will most effectively characterize the biological system under study. Existing model-based design of experiment algorithms have primarily relied on Fisher Information Matrix-based methods to choose the best experiment in a sequential manner. However, these are largely local methods that require an initial estimate of the parameter values, which are often highly uncertain, particularly when data is limited. In this paper, we provide an approach to specify an informative sequence of multiple design points (parallel design) that will constrain the dynamical uncertainty of the biological system responses to within experimentally detectable limits as specified by the estimated experimental noise. The method is based upon computationally efficient sparse grids and requires only a bounded uncertain parameter space; it does not rely upon initial parameter estimates. The design sequence emerges through the use of scenario trees with experimental design points chosen to minimize the uncertainty in the predicted dynamics of the measurable responses of the system. The algorithm was illustrated herein using a T cell activation model for three problems that ranged in dimension from 2D to 19D. The results demonstrate that it is possible to extract useful information from a mathematical model where traditional model-based design of experiments approaches most certainly fail. The experiments designed via this method fully constrain the model output dynamics to within experimentally resolvable limits. The method is effective for highly uncertain biological systems characterized by deterministic mathematical models with limited data sets. Also, it is highly modular and can be modified to include a variety of methodologies such as input design and model discrimination.

Reconstructing the hidden states in time course data of stochastic models.

PubMed

Zimmer, Christoph

2015-11-01

Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems' method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka-Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Biological parameters used in setting captive-breeding quotas for Indonesia's breeding facilities.

PubMed

Janssen, Jordi; Chng, Serene C L

2018-02-01

The commercial captive breeding of wildlife is often seen as a potential conservation tool to relieve pressure on wild populations, but laundering of wild-sourced specimens as captive bred can seriously undermine conservation efforts and provide a false sense of sustainability. Indonesia is at the center of such controversy; therefore, we examined Indonesia's captive-breeding production plan (CBPP) for 2016. We compared the biological parameters used in the CBPP with parameters in the literature and with parameters suggested by experts on each species and identified shortcomings of the CBPP. Production quotas for 99 out of 129 species were based on inaccurate or unrealistic biological parameters and production quotas deviated more than 10% from what parameters in the literature allow for. For 38 species, the quota exceeded the number of animals that can be bred based on the biological parameters (range 100-540%) calculated with equations in the CBPP. We calculated a lower reproductive output for 88 species based on published biological parameters compared with the parameters used in the CBPP. The equations used in the production plan did not appear to account for other factors (e.g., different survival rate for juveniles compared to adult animals) involved in breeding the proposed large numbers of specimens. We recommend the CBPP be adjusted so that realistic published biological parameters are applied and captive-breeding quotas are not allocated to species if their captive breeding is unlikely to be successful or no breeding stock is available. The shortcomings in the current CBPP create loopholes that mean mammals, reptiles, and amphibians from Indonesia declared captive bred may have been sourced from the wild. © 2017 Society for Conservation Biology.

Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects.

PubMed

Dresch, Jacqueline M; Liu, Xiaozhou; Arnosti, David N; Ay, Ahmet

2010-10-24

Quantitative models of gene expression generate parameter values that can shed light on biological features such as transcription factor activity, cooperativity, and local effects of repressors. An important element in such investigations is sensitivity analysis, which determines how strongly a model's output reacts to variations in parameter values. Parameters of low sensitivity may not be accurately estimated, leading to unwarranted conclusions. Low sensitivity may reflect the nature of the biological data, or it may be a result of the model structure. Here, we focus on the analysis of thermodynamic models, which have been used extensively to analyze gene transcription. Extracted parameter values have been interpreted biologically, but until now little attention has been given to parameter sensitivity in this context. We apply local and global sensitivity analyses to two recent transcriptional models to determine the sensitivity of individual parameters. We show that in one case, values for repressor efficiencies are very sensitive, while values for protein cooperativities are not, and provide insights on why these differential sensitivities stem from both biological effects and the structure of the applied models. In a second case, we demonstrate that parameters that were thought to prove the system's dependence on activator-activator cooperativity are relatively insensitive. We show that there are numerous parameter sets that do not satisfy the relationships proferred as the optimal solutions, indicating that structural differences between the two types of transcriptional enhancers analyzed may not be as simple as altered activator cooperativity. Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models. Knowledge of parameter sensitivities can provide the necessary context to determine how modeling results should be interpreted in biological systems.

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

PubMed Central

Liu, Fei; Heiner, Monika; Yang, Ming

2016-01-01

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. PMID:26910830

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

Krunglevicius, Dalius

2015-08-01

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms.

PubMed

Li, Le; Yip, Kevin Y

2016-12-15

Currently most terms and term-term relationships in Gene Ontology (GO) are defined manually, which creates cost, consistency and completeness issues. Recent studies have demonstrated the feasibility of inferring GO automatically from biological networks, which represents an important complementary approach to GO construction. These methods (NeXO and CliXO) are unsupervised, which means 1) they cannot use the information contained in existing GO, 2) the way they integrate biological networks may not optimize the accuracy, and 3) they are not customized to infer the three different sub-ontologies of GO. Here we present a semi-supervised method called Unicorn that extends these previous methods to tackle the three problems. Unicorn uses a sub-tree of an existing GO sub-ontology as training part to learn parameters in integrating multiple networks. Cross-validation results show that Unicorn reliably inferred the left-out parts of each specific GO sub-ontology. In addition, by training Unicorn with an old version of GO together with biological networks, it successfully re-discovered some terms and term-term relationships present only in a new version of GO. Unicorn also successfully inferred some novel terms that were not contained in GO but have biological meanings well-supported by the literature. Source code of Unicorn is available at http://yiplab.cse.cuhk.edu.hk/unicorn/.

A Robust Unified Approach to Analyzing Methylation and Gene Expression Data

PubMed Central

Khalili, Abbas; Huang, Tim; Lin, Shili

2009-01-01

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines. PMID:20161265

Effects of processing parameters in thermally induced phase separation technique on porous architecture of scaffolds for bone tissue engineering.

PubMed

Akbarzadeh, Rosa; Yousefi, Azizeh-Mitra

2014-08-01

Tissue engineering makes use of 3D scaffolds to sustain three-dimensional growth of cells and guide new tissue formation. To meet the multiple requirements for regeneration of biological tissues and organs, a wide range of scaffold fabrication techniques have been developed, aiming to produce porous constructs with the desired pore size range and pore morphology. Among different scaffold fabrication techniques, thermally induced phase separation (TIPS) method has been widely used in recent years because of its potential to produce highly porous scaffolds with interconnected pore morphology. The scaffold architecture can be closely controlled by adjusting the process parameters, including polymer type and concentration, solvent composition, quenching temperature and time, coarsening process, and incorporation of inorganic particles. The objective of this review is to provide information pertaining to the effect of these parameters on the architecture and properties of the scaffolds fabricated by the TIPS technique. © 2014 Wiley Periodicals, Inc.

A three-dimensional bioprinting system for use with a hydrogel-based biomaterial and printing parameter characterization.

PubMed

Song, Seung-Joon; Choi, Jaesoon; Park, Yong-Doo; Lee, Jung-Joo; Hong, So Young; Sun, Kyung

2010-11-01

Bioprinting is an emerging technology for constructing tissue or bioartificial organs with complex three-dimensional (3D) structures. It provides high-precision spatial shape forming ability on a larger scale than conventional tissue engineering methods, and simultaneous multiple components composition ability. Bioprinting utilizes a computer-controlled 3D printer mechanism for 3D biological structure construction. To implement minimal pattern width in a hydrogel-based bioprinting system, a study on printing characteristics was performed by varying printer control parameters. The experimental results showed that printing pattern width depends on associated printer control parameters such as printing flow rate, nozzle diameter, and nozzle velocity. The system under development showed acceptable feasibility of potential use for accurate printing pattern implementation in tissue engineering applications and is another example of novel techniques for regenerative medicine based on computer-aided biofabrication system. © 2010, Copyright the Authors. Artificial Organs © 2010, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Application of separable parameter space techniques to multi-tracer PET compartment modeling.

PubMed

Zhang, Jeff L; Michael Morey, A; Kadrmas, Dan J

2016-02-07

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

NASA Astrophysics Data System (ADS)

Zhang, Jeff L.; Morey, A. Michael; Kadrmas, Dan J.

2016-02-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Anthropics of aluminum-26 decay and biological homochirality

NASA Astrophysics Data System (ADS)

Sandora, McCullen

2017-11-01

Results of recent experiment reinstate feasibility to the hypothesis that biomolecular homochirality originates from beta decay. Coupled with hints that this process occurred extraterrestrially suggests aluminum-26 as the most likely source. If true, then its appropriateness is highly dependent on the half-life and energy of this decay. Demanding that this mechanism hold places new constraints on the anthropically allowed range for multiple parameters, including the electron mass, difference between up and down quark masses, the fine structure constant, and the electroweak scale. These new constraints on particle masses are tighter than those previously found. However, one edge of the allowed region is nearly degenerate with an existing bound, which, using what is termed here as `the principle of noncoincident peril', is argued to be a strong indicator that the fine structure constant must be an environmental parameter in the multiverse.

Synoptic thermal and oceanographic parameter distributions in the New York Bight Apex

NASA Technical Reports Server (NTRS)

Johnson, R. W.; Bahn, G. S.; Thomas, J. P.

1981-01-01

Concurrent surface water measurements made from a moving oceanographic research vessel were used to calibrate and interpret remotely sensed data collected over a plume in the New York Bight Apex on 23 June 1977. Multiple regression techniques were used to develop equations to map synoptic distributions of chlorophyll a and total suspended matter in the remotely sensed scene. Thermal (which did not have surface calibration values) and water quality parameter distributions indicated a cold mass of water in the Bight Apex with an overflowing nutrient-rich warm water plume that originated in the Sandy Hook Bay and flowed south near the New Jersey shoreline. Data analysis indicates that remotely sensed data may be particularly useful for studying physical and biological processes in the top several metres of surface water at plume boundaries.

Pubertal effects of 17α-methyltestosterone on GH-IGF-related genes of the hypothalamic-pituitary-liver-gonadal axis and other biological parameters in male, female and sex-reversed Nile tilapia.

PubMed

Phumyu, Nonglak; Boonanuntanasarn, Surintorn; Jangprai, Araya; Yoshizaki, Goro; Na-Nakorn, Uthairat

2012-06-01

The influence of 17α-methyltestosterone (MT) on growth responses, biological parameters and the expression of genes involved in the GH-IGF pathway of the hypothalamic-pituitary-liver-gonadal axis were investigated in female, male, and sex-reversed Nile tilapia to evaluate the relationship between sex and MT-induced changes in these parameters. Female fish had a lower growth rate than male and sex-reversed fish, and MT increased growth performance and duodenal villi in females. Most but not all biological parameters of sex-reversed fish were similar to those of male fish. Male fish had higher red blood cell counts and hemoglobin levels than female and sex-reversed fish, suggesting that these hematological indices reflect a higher metabolic rate in male fish. Greater blood triglyceride levels indicated the vitellogenin process in female fish. MT increased the alternative complement activity in female fish (P<0.05). Sex and MT had no significant effects on the hypothalamic mRNAs of GHRH and PACAP. Although not statistically significant, females tended to have higher GH mRNA levels than male and sex-reversed fish. Additionally, MT tended to decrease and increase GH mRNA levels in female and male fish, respectively. There were significant differences among sexes in the expression of GHR, and IGF mRNAs at the peripheral level in the liver and gonads. Females had lower hepatic GHRs and higher ovarian GHRs than male and sex-reversed fish. While the mRNA levels of IGF-1 were lower in the ovary, the levels of IGF-2 were higher compared with those in testes. A significant correlation between GHRs and IGFs was demonstrated in the liver and gonad (except for IGF-1). Multiple regression analysis showed a significant relationship between GH mRNA and both GHRs and IGFs in the liver and gonad. MT exerted androgenic and, to some extent, estrogenic effects on several physiological parameters and GH-IGF action. Copyright © 2012 Elsevier Inc. All rights reserved.

cuTauLeaping: A GPU-Powered Tau-Leaping Stochastic Simulator for Massive Parallel Analyses of Biological Systems

PubMed Central

Besozzi, Daniela; Pescini, Dario; Mauri, Giancarlo

2014-01-01

Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. PMID:24663957

Fundamentals of microfluidic cell culture in controlled microenvironments†

PubMed Central

Young, Edmond W. K.; Beebe, David J.

2010-01-01

Microfluidics has the potential to revolutionize the way we approach cell biology research. The dimensions of microfluidic channels are well suited to the physical scale of biological cells, and the many advantages of microfluidics make it an attractive platform for new techniques in biology. One of the key benefits of microfluidics for basic biology is the ability to control parameters of the cell microenvironment at relevant length and time scales. Considerable progress has been made in the design and use of novel microfluidic devices for culturing cells and for subsequent treatment and analysis. With the recent pace of scientific discovery, it is becoming increasingly important to evaluate existing tools and techniques, and to synthesize fundamental concepts that would further improve the efficiency of biological research at the microscale. This tutorial review integrates fundamental principles from cell biology and local microenvironments with cell culture techniques and concepts in microfluidics. Culturing cells in microscale environments requires knowledge of multiple disciplines including physics, biochemistry, and engineering. We discuss basic concepts related to the physical and biochemical microenvironments of the cell, physicochemical properties of that microenvironment, cell culture techniques, and practical knowledge of microfluidic device design and operation. We also discuss the most recent advances in microfluidic cell culture and their implications on the future of the field. The goal is to guide new and interested researchers to the important areas and challenges facing the scientific community as we strive toward full integration of microfluidics with biology. PMID:20179823

Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes.

PubMed

Frömke, Cornelia; Hothorn, Ludwig A; Kropf, Siegfried

2008-01-27

In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a significant difference in location parameters from zero or one for ratios thereof for each variable. However, in some studies a significant deviation of the difference in locations from zero (or 1 in terms of the ratio) is biologically meaningless. A relevant difference or ratio is sought in such cases. This article addresses the use of relevance-shifted tests on ratios for a multivariate parallel two-sample group design. Two empirical procedures are proposed which embed the relevance-shifted test on ratios. As both procedures test a hypothesis for each variable, the resulting multiple testing problem has to be considered. Hence, the procedures include a multiplicity correction. Both procedures are extensions of available procedures for point null hypotheses achieving exact control of the familywise error rate. Whereas the shift of the null hypothesis alone would give straight-forward solutions, the problems that are the reason for the empirical considerations discussed here arise by the fact that the shift is considered in both directions and the whole parameter space in between these two limits has to be accepted as null hypothesis. The first algorithm to be discussed uses a permutation algorithm, and is appropriate for designs with a moderately large number of observations. However, many experiments have limited sample sizes. Then the second procedure might be more appropriate, where multiplicity is corrected according to a concept of data-driven order of hypotheses.

SBML-PET: a Systems Biology Markup Language-based parameter estimation tool.

PubMed

Zi, Zhike; Klipp, Edda

2006-11-01

The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling pathways, gene regulation networks and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data from different experimental conditions. SBML-PET has a unique feature of supporting event definition in the SMBL model. SBML models can also be simulated in SBML-PET. Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system. http://sysbio.molgen.mpg.de/SBML-PET/. The website also contains detailed documentation for SBML-PET.

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

PubMed Central

Cankorur-Cetinkaya, Ayca; Dias, Joao M. L.; Kludas, Jana; Slater, Nigel K. H.; Rousu, Juho; Dikicioglu, Duygu

2017-01-01

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple‐to‐use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257). PMID:28635591

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

PubMed

Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

A multispecies framework for landscape conservation planning.

PubMed

Schwenk, W Scott; Donovan, Therese M

2011-10-01

Rapidly changing landscapes have spurred the need for quantitative methods for conservation assessment and planning that encompass large spatial extents. We devised and tested a multispecies framework for conservation planning to complement single-species assessments and ecosystem-level approaches. Our framework consisted of 4 elements: sampling to effectively estimate population parameters, measuring how human activity affects landscapes at multiple scales, analyzing the relation between landscape characteristics and individual species occurrences, and evaluating and comparing the responses of multiple species to landscape modification. We applied the approach to a community of terrestrial birds across 25,000 km(2) with a range of intensities of human development. Human modification of land cover, road density, and other elements of the landscape, measured at multiple spatial extents, had large effects on occupancy of the 67 species studied. Forest composition within 1 km of points had a strong effect on occupancy of many species and a range of negative, intermediate, and positive associations. Road density within 1 km of points, percent evergreen forest within 300 m, and distance from patch edge were also strongly associated with occupancy for many species. We used the occupancy results to group species into 11 guilds that shared patterns of association with landscape characteristics. Our multispecies approach to conservation planning allowed us to quantify the trade-offs of different scenarios of land-cover change in terms of species occupancy. Conservation Biology © 2011 Society for Conservation Biology. No claim to original US government works.

Large scale study of multiple-molecule queries

PubMed Central

2009-01-01

Background In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family. Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics. Results Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics. Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data. Conclusion Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/. PMID:20298525

Optimal growth trajectories with finite carrying capacity.

PubMed

Caravelli, F; Sindoni, L; Caccioli, F; Ududec, C

2016-08-01

We consider the problem of finding optimal strategies that maximize the average growth rate of multiplicative stochastic processes. For a geometric Brownian motion, the problem is solved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction of resources at any step of the dynamics. We generalize these finding to the case of dynamical equations with finite carrying capacity, which can find applications in biology, mathematical ecology, and finance. We formulate the problem in terms of a stochastic process with multiplicative noise and a nonlinear drift term that is determined by the specific functional form of carrying capacity. We solve the stochastic equation for two classes of carrying capacity functions (power laws and logarithmic), and in both cases we compute the optimal trajectories of the control parameter. We further test the validity of our analytical results using numerical simulations.

Optimal growth trajectories with finite carrying capacity

NASA Astrophysics Data System (ADS)

Caravelli, F.; Sindoni, L.; Caccioli, F.; Ududec, C.

2016-08-01

We consider the problem of finding optimal strategies that maximize the average growth rate of multiplicative stochastic processes. For a geometric Brownian motion, the problem is solved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction of resources at any step of the dynamics. We generalize these finding to the case of dynamical equations with finite carrying capacity, which can find applications in biology, mathematical ecology, and finance. We formulate the problem in terms of a stochastic process with multiplicative noise and a nonlinear drift term that is determined by the specific functional form of carrying capacity. We solve the stochastic equation for two classes of carrying capacity functions (power laws and logarithmic), and in both cases we compute the optimal trajectories of the control parameter. We further test the validity of our analytical results using numerical simulations.

Microscale Symmetrical Electroporator Array as a Versatile Molecular Delivery System

NASA Astrophysics Data System (ADS)

Ouyang, Mengxing; Hill, Winfield; Lee, Jung Hyun; Hur, Soojung Claire

2017-03-01

Successful developments of new therapeutic strategies often rely on the ability to deliver exogenous molecules into cytosol. We have developed a versatile on-chip vortex-assisted electroporation system, engineered to conduct sequential intracellular delivery of multiple molecules into various cell types at low voltage in a dosage-controlled manner. Micro-patterned planar electrodes permit substantial reduction in operational voltages and seamless integration with an existing microfluidic technology. Equipped with real-time process visualization functionality, the system enables on-chip optimization of electroporation parameters for cells with varying properties. Moreover, the system’s dosage control and multi-molecular delivery capabilities facilitate intracellular delivery of various molecules as a single agent or in combination and its utility in biological research has been demonstrated by conducting RNA interference assays. We envision the system to be a powerful tool, aiding a wide range of applications, requiring single-cell level co-administrations of multiple molecules with controlled dosages.

Architecture effects on multivalent interactions by polypeptide-based multivalent ligands

NASA Astrophysics Data System (ADS)

Liu, Shuang

Multivalent interactions are characterized by the simultaneous binding between multiple ligands and multiple binding sites, either in solutions or at interfaces. In biological systems, most multivalent interactions occur between protein receptors and carbohydrate ligands through hydrogen-bonding and hydrophobic interactions. Compared with weak affinity binding between one ligand and one binding site, i.e. monovalent interaction, multivalent interactioins provide greater avidity and specificity, and therefore play unique roles in a broad range of biological activities. Moreover, the studies of multivalent interactions are also essential for producing effective inhibitors and effectors of biological processes that could have important therapeutic applications. Synthetic multivalent ligands have been designed to mimic the biological functions of natural multivalent interactions, and various types of scaffolds have been used to display multiple ligands, including small molecules, linear polymers, dendrimers, nanoparticle surfaces, monolayer surfaces and liposomes. Studies have shown that multivalent interactions can be highly affected by various architectural parameters of these multivalent ligands, including ligand identities, valencies, spacing, ligand densities, nature of linker arms, scaffold length and scaffold conformation. Most of these multivalent ligands are chemically synthesized and have limitations of controlling over sequence and conformation, which is a barrier for mimicking ordered and controlled natural biological systems. Therefore, multivalent ligands with precisely controlled architecture are required for improved structure-function relationship studies. Protein engineering methods with subsequent chemical coupling of ligands provide significant advantages of controlling over backbone conformation and functional group placement, and therefore have been used to synthesize recombinant protein-based materials with desired properties similar to natural protein materials, including structural as well as functional proteins. Therefore, polypeptide-based multivalent scaffolds are used to display ligands to assess the contribution of different architectural parameters to the multivalent binding events. In this work, a family of alanine-rich alpha-helical glycopolypeptides was designed and synthesized by a combination of protein engineering and chemical coupling, to display two types of saccharide ligands for two different multivalent binding systems. The valencies, chain length and spacing between adjacent ligands of these multivalent ligands were designed in order to study architecture effects on multivalent interactions. The polypeptides and their glycoconjugates were characterized via various methods, including SDS-PAGE, NMR, HPLC, amino acid analysis (AAA), MALDI, circular dichroism (CD) and GPC. In the first multivalent binding system, cholera toxin B pentamer (CT B5) was chosen to be the protein receptor due to its well-characterized structure, lack of significant steric interference of binding to multiple binding sites, and requirement of only simple monosaccharide as ligands. Galactopyranoside was incorporated into polypeptide scaffolds through amine-carboxylic acid coupling to the side chains of glutamic acid residues. The inhibition and binding to CT B5 of these glycopolypeptide ligands were evaluated by direct enzyme-linked assay (DELA). As a complement method, weak affinity chromatography (WAC) was also used to evaluate glycopolypeptides binding to a CT B5 immobilized column. The architecture effects on CT B 5 inhibition are discussed. In the second system, cell surface receptor L-selectin was targeted by polypeptide-based multivalent ligands containing disulfated galactopyranoside ligands, due to its important roles in various immunological activities. The effects of glycopolypeptide architectural variables L-selectin shedding were evaluated via ELISA-based assays. These polypeptide-based multivalent ligands are suggested to be useful for elucidating architecture effects on multivalent interactions, manipulating multivalent interactions and the subsequent cellular responses in different systems. These materials have great potential applications in therapeutics and could also provide guidelines for design of multivalent ligands for other protein receptors.

Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

PubMed

Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

2016-10-01

Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Proteasome 20S in multiple myeloma: comparison of concentration and chymotrypsin-like activity in plasma and serum.

PubMed

Romaniuk, Wioletta; Kalita, Joanna; Ostrowska, Halina; Kloczko, Janusz

2018-03-05

The ubiquitin-proteasome system is relevant in the pathobiology of many haematological malignancies, including multiple myeloma. The assessment of proteasome concentration and chymotrypsin-like (ChT-L) activity might constitute a new approach to diagnosis, prognosis and monitoring of anticancer treatment of patients with haematological malignancies and other diseases. The aim of our study was to determine which material, plasma or serum, is better for measuring chymotrypsin-like (ChT-L) activity and proteasome concentration. We analysed proteasome concentration and chymotrypsin-like (ChT-L) activity in 70 plasma and serum samples drawn from 28 patients at different treatment stages for multiple myeloma (MM) and 31 healthy volunteers. Proteasome ChT-L activity and concentration in multiple myeloma patients were significantly higher in plasma compared to serum. In this group we observed significant and positive correlations both between the plasma and serum proteasome ChT-L activity and plasma and serum proteasome concentration. The higher values of proteasome concentration and ChT-L activity in plasma than in serum and their better correlations with parameters of tumour load and prognosis suggest that plasma constitutes a better biological material for measuring ChT-L activity and proteasome concentration than serum in multiple myeloma patients.

EUD-based biological optimization for carbon ion therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brüningk, Sarah C., E-mail: sarah.brueningk@icr.ac.uk; Kamp, Florian; Wilkens, Jan J.

2015-11-15

Purpose: Treatment planning for carbon ion therapy requires an accurate modeling of the biological response of each tissue to estimate the clinical outcome of a treatment. The relative biological effectiveness (RBE) accounts for this biological response on a cellular level but does not refer to the actual impact on the organ as a whole. For photon therapy, the concept of equivalent uniform dose (EUD) represents a simple model to take the organ response into account, yet so far no formulation of EUD has been reported that is suitable to carbon ion therapy. The authors introduce the concept of an equivalentmore » uniform effect (EUE) that is directly applicable to both ion and photon therapies and exemplarily implemented it as a basis for biological treatment plan optimization for carbon ion therapy. Methods: In addition to a classical EUD concept, which calculates a generalized mean over the RBE-weighted dose distribution, the authors propose the EUE to simplify the optimization process of carbon ion therapy plans. The EUE is defined as the biologically equivalent uniform effect that yields the same probability of injury as the inhomogeneous effect distribution in an organ. Its mathematical formulation is based on the generalized mean effect using an effect-volume parameter to account for different organ architectures and is thus independent of a reference radiation. For both EUD concepts, quadratic and logistic objective functions are implemented into a research treatment planning system. A flexible implementation allows choosing for each structure between biological effect constraints per voxel and EUD constraints per structure. Exemplary treatment plans are calculated for a head-and-neck patient for multiple combinations of objective functions and optimization parameters. Results: Treatment plans optimized using an EUE-based objective function were comparable to those optimized with an RBE-weighted EUD-based approach. In agreement with previous results from photon therapy, the optimization by biological objective functions resulted in slightly superior treatment plans in terms of final EUD for the organs at risk (OARs) compared to voxel-based optimization approaches. This observation was made independent of the underlying objective function metric. An absolute gain in OAR sparing was observed for quadratic objective functions, whereas intersecting DVHs were found for logistic approaches. Even for considerable under- or overestimations of the used effect- or dose–volume parameters during the optimization, treatment plans were obtained that were of similar quality as the results of a voxel-based optimization. Conclusions: EUD-based optimization with either of the presented concepts can successfully be applied to treatment plan optimization. This makes EUE-based optimization for carbon ion therapy a useful tool to optimize more specifically in the sense of biological outcome while voxel-to-voxel variations of the biological effectiveness are still properly accounted for. This may be advantageous in terms of computational cost during treatment plan optimization but also enables a straight forward comparison of different fractionation schemes or treatment modalities.« less

Modelling biological invasions: species traits, species interactions, and habitat heterogeneity.

PubMed

Cannas, Sergio A; Marco, Diana E; Páez, Sergio A

2003-05-01

In this paper we explore the integration of different factors to understand, predict and control ecological invasions, through a general cellular automaton model especially developed. The model includes life history traits of several species in a modular structure interacting multiple cellular automata. We performed simulations using field values corresponding to the exotic Gleditsia triacanthos and native co-dominant trees in a montane area. Presence of G. triacanthos juvenile bank was a determinant condition for invasion success. Main parameters influencing invasion velocity were mean seed dispersal distance and minimum reproductive age. Seed production had a small influence on the invasion velocity. Velocities predicted by the model agreed well with estimations from field data. Values of population density predicted matched field values closely. The modular structure of the model, the explicit interaction between the invader and the native species, and the simplicity of parameters and transition rules are novel features of the model.

A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion

PubMed Central

Szczecinski, Nicholas S.; Hunt, Alexander J.; Quinn, Roger D.

2017-01-01

A dynamical model of an animal’s nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot. PMID:28848419

Design of a Single Motor Based Leg Structure with the Consideration of Inherent Mechanical Stability

NASA Astrophysics Data System (ADS)

Taha Manzoor, Muhammad; Sohail, Umer; Noor-e-Mustafa; Nizami, Muhammad Hamza Asif; Ayaz, Yasar

2017-07-01

The fundamental aspect of designing a legged robot is constructing a leg design that is robust and presents a simple control problem. In this paper, we have successfully designed a robotic leg based on a unique four bar mechanism with only one motor per leg. The leg design parameters used in our platform are extracted from design principles used in biological systems, multiple iterations and previous research findings. These principles guide a robotic leg to have minimal mechanical passive impedance, low leg mass and inertia, a suitable foot trajectory utilizing a practical balance between leg kinematics and robot usage, and the resultant inherent mechanical stability. The designed platform also exhibits the key feature of self-locking. Theoretical tools and software iterations were used to derive these practical features and yield an intuitive sense of the required leg design parameters.

Prediction of kinase-inhibitor binding affinity using energetic parameters

PubMed Central

Usha, Singaravelu; Selvaraj, Samuel

2016-01-01

The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor. PMID:28149052

Parameter estimation using meta-heuristics in systems biology: a comprehensive review.

PubMed

Sun, Jianyong; Garibaldi, Jonathan M; Hodgman, Charlie

2012-01-01

This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms

PubMed Central

Li, Le; Yip, Kevin Y.

2016-01-01

Currently most terms and term-term relationships in Gene Ontology (GO) are defined manually, which creates cost, consistency and completeness issues. Recent studies have demonstrated the feasibility of inferring GO automatically from biological networks, which represents an important complementary approach to GO construction. These methods (NeXO and CliXO) are unsupervised, which means 1) they cannot use the information contained in existing GO, 2) the way they integrate biological networks may not optimize the accuracy, and 3) they are not customized to infer the three different sub-ontologies of GO. Here we present a semi-supervised method called Unicorn that extends these previous methods to tackle the three problems. Unicorn uses a sub-tree of an existing GO sub-ontology as training part to learn parameters in integrating multiple networks. Cross-validation results show that Unicorn reliably inferred the left-out parts of each specific GO sub-ontology. In addition, by training Unicorn with an old version of GO together with biological networks, it successfully re-discovered some terms and term-term relationships present only in a new version of GO. Unicorn also successfully inferred some novel terms that were not contained in GO but have biological meanings well-supported by the literature.Availability: Source code of Unicorn is available at http://yiplab.cse.cuhk.edu.hk/unicorn/. PMID:27976738

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

NASA Astrophysics Data System (ADS)

Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O'Brien, Katherine R.

2017-01-01

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species.

PubMed

Adams, Matthew P; Collier, Catherine J; Uthicke, Sven; Ow, Yan X; Langlois, Lucas; O'Brien, Katherine R

2017-01-04

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (T opt ) for maximum photosynthetic rate (P max ). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.

Model fit versus biological relevance: Evaluating photosynthesis-temperature models for three tropical seagrass species

PubMed Central

Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O’Brien, Katherine R.

2017-01-01

When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike. PMID:28051123

Demographic History and Reproductive Output Correlates with Intraspecific Genetic Variation in Seven Species of Indo-Pacific Mangrove Crabs

PubMed Central

Fratini, Sara; Ragionieri, Lapo; Cannicci, Stefano

2016-01-01

The spatial distribution and the amount of intraspecific genetic variation of marine organisms are strongly influenced by many biotic and abiotic factors. Comparing biological and genetic data characterizing species living in the same habitat can help to elucidate the processes driving these variation patterns. Here, we present a comparative multispecies population genetic study on seven mangrove crabs co-occurring in the West Indian Ocean characterized by planktotrophic larvae with similar pelagic larval duration. Our main aim was to investigate whether a suite of biological, behavioural and ecological traits could affect genetic diversities of the study species in combination with historical demographic parameters. As possible current explanatory factors, we used the intertidal micro-habitat colonised by adult populations, various parameters of individual and population fecundity, and the timing of larval release. As the genetic marker, we used partial sequences of cytochrome oxidase subunit I gene. Genetic and ecological data were collected by the authors and/or gathered from primary literature. Permutational multiple regression models and ANOVA tests showed that species density and their reproductive output in combination with historical demographic parameters could explain the intraspecific genetic variation indexes across the seven species. In particular, species producing consistently less eggs per spawning event showed higher values of haplotype diversity. Moreover, Tajima’s D parameters well explained the recorded values for haplotype diversity and average γst. We concluded that current intraspecific gene diversities in crabs inhabiting mangrove forests were affected by population fecundity as well as past demographic history. The results were also discussed in terms of management and conservation of fauna in the Western Indian Ocean mangroves. PMID:27379532

Demographic History and Reproductive Output Correlates with Intraspecific Genetic Variation in Seven Species of Indo-Pacific Mangrove Crabs.

PubMed

Fratini, Sara; Ragionieri, Lapo; Cannicci, Stefano

2016-01-01

The spatial distribution and the amount of intraspecific genetic variation of marine organisms are strongly influenced by many biotic and abiotic factors. Comparing biological and genetic data characterizing species living in the same habitat can help to elucidate the processes driving these variation patterns. Here, we present a comparative multispecies population genetic study on seven mangrove crabs co-occurring in the West Indian Ocean characterized by planktotrophic larvae with similar pelagic larval duration. Our main aim was to investigate whether a suite of biological, behavioural and ecological traits could affect genetic diversities of the study species in combination with historical demographic parameters. As possible current explanatory factors, we used the intertidal micro-habitat colonised by adult populations, various parameters of individual and population fecundity, and the timing of larval release. As the genetic marker, we used partial sequences of cytochrome oxidase subunit I gene. Genetic and ecological data were collected by the authors and/or gathered from primary literature. Permutational multiple regression models and ANOVA tests showed that species density and their reproductive output in combination with historical demographic parameters could explain the intraspecific genetic variation indexes across the seven species. In particular, species producing consistently less eggs per spawning event showed higher values of haplotype diversity. Moreover, Tajima's D parameters well explained the recorded values for haplotype diversity and average γst. We concluded that current intraspecific gene diversities in crabs inhabiting mangrove forests were affected by population fecundity as well as past demographic history. The results were also discussed in terms of management and conservation of fauna in the Western Indian Ocean mangroves.

Novel image encryption algorithm based on multiple-parameter discrete fractional random transform

NASA Astrophysics Data System (ADS)

Zhou, Nanrun; Dong, Taiji; Wu, Jianhua

2010-08-01

A new method of digital image encryption is presented by utilizing a new multiple-parameter discrete fractional random transform. Image encryption and decryption are performed based on the index additivity and multiple parameters of the multiple-parameter fractional random transform. The plaintext and ciphertext are respectively in the spatial domain and in the fractional domain determined by the encryption keys. The proposed algorithm can resist statistic analyses effectively. The computer simulation results show that the proposed encryption algorithm is sensitive to the multiple keys, and that it has considerable robustness, noise immunity and security.

Physiological frailty index (PFI): quantitative in-life estimate of individual biological age in mice.

PubMed

Antoch, Marina P; Wrobel, Michelle; Kuropatwinski, Karen K; Gitlin, Ilya; Leonova, Katerina I; Toshkov, Ilia; Gleiberman, Anatoli S; Hutson, Alan D; Chernova, Olga B; Gudkov, Andrei V

2017-03-19

The development of healthspan-extending pharmaceuticals requires quantitative estimation of age-related progressive physiological decline. In humans, individual health status can be quantitatively assessed by means of a frailty index (FI), a parameter which reflects the scale of accumulation of age-related deficits. However, adaptation of this methodology to animal models is a challenging task since it includes multiple subjective parameters. Here we report a development of a quantitative non-invasive procedure to estimate biological age of an individual animal by creating physiological frailty index (PFI). We demonstrated the dynamics of PFI increase during chronological aging of male and female NIH Swiss mice. We also demonstrated acceleration of growth of PFI in animals placed on a high fat diet, reflecting aging acceleration by obesity and provide a tool for its quantitative assessment. Additionally, we showed that PFI could reveal anti-aging effect of mTOR inhibitor rapatar (bioavailable formulation of rapamycin) prior to registration of its effects on longevity. PFI revealed substantial sex-related differences in normal chronological aging and in the efficacy of detrimental (high fat diet) or beneficial (rapatar) aging modulatory factors. Together, these data introduce PFI as a reliable, non-invasive, quantitative tool suitable for testing potential anti-aging pharmaceuticals in pre-clinical studies.

Electrobioremediation of oil spills.

PubMed

Daghio, Matteo; Aulenta, Federico; Vaiopoulou, Eleni; Franzetti, Andrea; Arends, Jan B A; Sherry, Angela; Suárez-Suárez, Ana; Head, Ian M; Bestetti, Giuseppina; Rabaey, Korneel

2017-05-01

Annually, thousands of oil spills occur across the globe. As a result, petroleum substances and petrochemical compounds are widespread contaminants causing concern due to their toxicity and recalcitrance. Many remediation strategies have been developed using both physicochemical and biological approaches. Biological strategies are most benign, aiming to enhance microbial metabolic activities by supplying limiting inorganic nutrients, electron acceptors or donors, thus stimulating oxidation or reduction of contaminants. A key issue is controlling the supply of electron donors/acceptors. Bioelectrochemical systems (BES) have emerged, in which an electrical current serves as either electron donor or acceptor for oil spill bioremediation. BES are highly controllable and can possibly also serve as biosensors for real time monitoring of the degradation process. Despite being promising, multiple aspects need to be considered to make BES suitable for field applications including system design, electrode materials, operational parameters, mode of action and radius of influence. The microbiological processes, involved in bioelectrochemical contaminant degradation, are currently not fully understood, particularly in relation to electron transfer mechanisms. Especially in sulfate rich environments, the sulfur cycle appears pivotal during hydrocarbon oxidation. This review provides a comprehensive analysis of the research on bioelectrochemical remediation of oil spills and of the key parameters involved in the process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Retrieving the optical parameters of biological tissues using diffuse reflectance spectroscopy and Fourier series expansions. I. theory and application.

PubMed

Muñoz Morales, Aarón A; Vázquez Y Montiel, Sergio

2012-10-01

The determination of optical parameters of biological tissues is essential for the application of optical techniques in the diagnosis and treatment of diseases. Diffuse Reflection Spectroscopy is a widely used technique to analyze the optical characteristics of biological tissues. In this paper we show that by using diffuse reflectance spectra and a new mathematical model we can retrieve the optical parameters by applying an adjustment of the data with nonlinear least squares. In our model we represent the spectra using a Fourier series expansion finding mathematical relations between the polynomial coefficients and the optical parameters. In this first paper we use spectra generated by the Monte Carlo Multilayered Technique to simulate the propagation of photons in turbid media. Using these spectra we determine the behavior of Fourier series coefficients when varying the optical parameters of the medium under study. With this procedure we find mathematical relations between Fourier series coefficients and optical parameters. Finally, the results show that our method can retrieve the optical parameters of biological tissues with accuracy that is adequate for medical applications.

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data.

PubMed

Zhang, L; Liu, X J

2016-06-03

With the rapid development of next-generation high-throughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

Step by Step: Biology Undergraduates' Problem-Solving Procedures during Multiple-Choice Assessment

ERIC Educational Resources Information Center

Prevost, Luanna B.; Lemons, Paula P.

2016-01-01

This study uses the theoretical framework of domain-specific problem solving to explore the procedures students use to solve multiple-choice problems about biology concepts. We designed several multiple-choice problems and administered them on four exams. We trained students to produce written descriptions of how they solved the problem, and this…

Application of separable parameter space techniques to multi-tracer PET compartment modeling

PubMed Central

Zhang, Jeff L; Morey, A Michael; Kadrmas, Dan J

2016-01-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models. PMID:26788888

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons.

PubMed

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons.

The use of information theory for the evaluation of biomarkers of aging and physiological age.

PubMed

Blokh, David; Stambler, Ilia

2017-04-01

The present work explores the application of information theoretical measures, such as entropy and normalized mutual information, for research of biomarkers of aging. The use of information theory affords unique methodological advantages for the study of aging processes, as it allows evaluating non-linear relations between biological parameters, providing the precise quantitative strength of those relations, both for individual and multiple parameters, showing cumulative or synergistic effect. Here we illustrate those capabilities utilizing a dataset on heart disease, including diagnostic parameters routinely available to physicians. The use of information-theoretical methods, utilizing normalized mutual information, revealed the exact amount of information that various diagnostic parameters or their combinations contained about the persons' age. Based on those exact informative values for the correlation of measured parameters with age, we constructed a diagnostic rule (a decision tree) to evaluate physiological age, as compared to chronological age. The present data illustrated that younger subjects suffering from heart disease showed characteristics of people of higher age (higher physiological age). Utilizing information-theoretical measures, with additional data, it may be possible to create further clinically applicable information-theory-based markers and models for the evaluation of physiological age, its relation to age-related diseases and its potential modifications by therapeutic interventions. Copyright © 2017 Elsevier B.V. All rights reserved.

Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons

PubMed Central

Nicola, Wilten; Campbell, Sue Ann

2013-01-01

We analytically derive mean-field models for all-to-all coupled networks of heterogeneous, adapting, two-dimensional integrate and fire neurons. The class of models we consider includes the Izhikevich, adaptive exponential and quartic integrate and fire models. The heterogeneity in the parameters leads to different moment closure assumptions that can be made in the derivation of the mean-field model from the population density equation for the large network. Three different moment closure assumptions lead to three different mean-field systems. These systems can be used for distinct purposes such as bifurcation analysis of the large networks, prediction of steady state firing rate distributions, parameter estimation for actual neurons and faster exploration of the parameter space. We use the mean-field systems to analyze adaptation induced bursting under realistic sources of heterogeneity in multiple parameters. Our analysis demonstrates that the presence of heterogeneity causes the Hopf bifurcation associated with the emergence of bursting to change from sub-critical to super-critical. This is confirmed with numerical simulations of the full network for biologically reasonable parameter values. This change decreases the plausibility of adaptation being the cause of bursting in hippocampal area CA3, an area with a sizable population of heavily coupled, strongly adapting neurons. PMID:24416013

Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer.

PubMed

Okimoto, Gordon; Zeinalzadeh, Ashkan; Wenska, Tom; Loomis, Michael; Nation, James B; Fabre, Tiphaine; Tiirikainen, Maarit; Hernandez, Brenda; Chan, Owen; Wong, Linda; Kwee, Sandi

2016-01-01

Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint analysis of the data matrices associated with the different data types of a MMDS should provide a more focused view of the biology underlying complex diseases such as cancer that would not be apparent from the analysis of a single data type alone. As multi-modal data rapidly accumulate in research laboratories and public databases such as The Cancer Genome Atlas (TCGA), the translation of such data into clinically actionable knowledge has been slowed by the lack of computational tools capable of analyzing MMDSs. Here, we describe the Joint Analysis of Many Matrices by ITeration (JAMMIT) algorithm that jointly analyzes the data matrices of a MMDS using sparse matrix approximations of rank-1. The JAMMIT algorithm jointly approximates an arbitrary number of data matrices by rank-1 outer-products composed of "sparse" left-singular vectors (eigen-arrays) that are unique to each matrix and a right-singular vector (eigen-signal) that is common to all the matrices. The non-zero coefficients of the eigen-arrays identify small subsets of variables for each data type (i.e., signatures) that in aggregate, or individually, best explain a dominant eigen-signal defined on the columns of the data matrices. The approximation is specified by a single "sparsity" parameter that is selected based on false discovery rate estimated by permutation testing. Multiple signals of interest in a given MDDS are sequentially detected and modeled by iterating JAMMIT on "residual" data matrices that result from a given sparse approximation. We show that JAMMIT outperforms other joint analysis algorithms in the detection of multiple signatures embedded in simulated MDDS. On real multimodal data for ovarian and liver cancer we show that JAMMIT identified multi-modal signatures that were clinically informative and enriched for cancer-related biology. Sparse matrix approximations of rank-1 provide a simple yet effective means of jointly reducing multiple, big data types to a small subset of variables that characterize important clinical and/or biological attributes of the bio-samples from which the data were acquired.

Modeling multisystem biological risk in young adults: The Coronary Artery Risk Development in Young Adults Study.

PubMed

Seeman, Teresa; Gruenewald, Tara; Karlamangla, Arun; Sidney, Steve; Liu, Kiang; McEwen, Bruce; Schwartz, Joseph

2010-01-01

Although much prior research has focused on identifying the roles of major regulatory systems in health risks, the concept of allostatic load (AL) focuses on the importance of a more multisystems view of health risks. How best to operationalize allostatic load, however, remains the subject of some debate. We sought to test a hypothesized metafactor model of allostatic load composed of a number of biological system factors, and to investigate model invariance across sex and ethnicity. Biological data from 782 men and women, aged 32-47, from the Oakland, CA and Chicago, IL sites of the Coronary Artery Risk Development in Young Adults Study (CARDIA) were collected as part of the Year 15exam in 2000. These include measures of blood pressure, metabolic parameters (glucose, insulin, lipid profiles, and waist circumference), markers of inflammation (interleukin-6, C-reactive protein, and fibrinogen), heart rate variability, sympathetic nervous system activity (12-hr urinary norepinephrine and epinephrine) and hypothalamic-pituitary-adrenal axis activity (diurnal salivary free cortisol). A "metafactor" model of AL as an aggregate measure of six underlying latent biological subfactors was found to fit the data, with the metafactor structure capturing 84% of variance of all pairwise associations among biological subsystems. There was little evidence of model variance across sex and/or ethnicity. These analyses extend work operationalizing AL as a multisystems index of biological dysregulation, providing initial support for a model of AL as a metaconstruct of inter-relationships among multiple biological regulatory systems, that varies little across sex or ethnicity.

Method and apparatus for determining nutrient stimulation of biological processes

DOEpatents

Colwell, F.S.; Geesey, G.G.; Gillis, R.J.; Lehman, R.M.

1997-11-11

A method and apparatus is described for determining the nutrients to stimulate microorganisms in a particular environment. A representative sample of microorganisms from a particular environment are contacted with multiple support means wherein each support means has intimately associated with the surface of the support means a different nutrient composition for said microorganisms in said sample. The multiple support means is allowed to remain in contact with the microorganisms in the sample for a time period sufficient to measure differences in microorganism effects for the multiple support means. Microorganism effects for the multiple support means are then measured and compared. The invention is particularly adaptable to being conducted in situ. The additional steps of regulating nutrients added to the particular environment of microorganisms can enhance the desired results. Biological systems particularly suitable for this invention are bioremediation, biologically enhanced oil recovery, biological leaching of metals, and agricultural bioprocesses. 5 figs.

Method and apparatus for determining nutrient stimulation of biological processes

DOEpatents

Colwell, Frederick S.; Geesey, Gill G.; Gillis, Richard J.; Lehman, R. Michael

1999-01-01

A method and apparatus for determining the nutrients to stimulate microorganisms in a particular environment. A representative sample of microorganisms from a particular environment are contacted with multiple support means wherein each support means has intimately associated with the surface of the support means a different nutrient composition for said microorganisms in said sample. The multiple support means is allowed to remain in contact with the microorganisms in the sample for a time period sufficient to measure difference in microorganism effects for the multiple support means. Microorganism effects for the multiple support means are then measured and compared. The invention is particularly adaptable to being conducted in situ. The additional steps of regulating nutrients added to the particular environment of microorganisms can enhance the desired results. Biological systems particularly suitable for this invention are bioremediation, biologically enhanced oil recovery, biological leaching of metals, and agricultural bioprocesses.

Method and apparatus for determining nutrient stimulation of biological processes

DOEpatents

Colwell, F.S.; Geesey, G.G.; Gillis, R.J.; Lehman, R.M.

1999-07-13

A method and apparatus are disclosed for determining the nutrients to stimulate microorganisms in a particular environment. A representative sample of microorganisms from a particular environment are contacted with multiple support means wherein each support means has intimately associated with the surface of the support means a different nutrient composition for microorganisms in the sample. The multiple support means is allowed to remain in contact with the microorganisms in the sample for a time period sufficient to measure difference in microorganism effects for the multiple support means. Microorganism effects for the multiple support means are then measured and compared. The invention is particularly adaptable to being conducted in situ. The additional steps of regulating nutrients added to the particular environment of microorganisms can enhance the desired results. Biological systems particularly suitable for this invention are bioremediation, biologically enhanced oil recovery, biological leaching of metals, and agricultural bioprocesses. 5 figs.

Method and apparatus for determining nutrient stimulation of biological processes

DOEpatents

Colwell, Frederick S.; Geesey, Gill G.; Gillis, Richard J.; Lehman, R. Michael

1997-01-01

A method and apparatus for determining the nutrients to stimulate microorganisms in a particular environment. A representative sample of microorganisms from a particular environment are contacted with multiple support means wherein each support means has intimately associated with the surface of the support means a different nutrient composition for said microorganisms in said sample. The multiple support means is allowed to remain in contact with the microorganisms in the sample for a time period sufficient to measure differences in microorganism effects for the multiple support means. Microorganism effects for the multiple support means are then measured and compared. The invention is particularly adaptable to being conducted in situ. The additional steps of regulating nutrients added to the particular environment of microorganisms can enhance the desired results. Biological systems particularly suitable for this invention are bioremediation, biologically enhanced oil recovery, biological leaching of metals, and agricultural bioprocesses.

Calculating background levels for ecological risk parameters in toxic harbor sediment

USGS Publications Warehouse

Leadon, C.J.; McDonnell, T.R.; Lear, J.; Barclift, D.

2007-01-01

Establishing background levels for biological parameters is necessary in assessing the ecological risks from harbor sediment contaminated with toxic chemicals. For chemicals in sediment, the term contaminated is defined as having concentrations above background and significant human health or ecological risk levels. For biological parameters, a site could be considered contaminated if levels of the parameter are either more or less than the background level, depending on the specific parameter. Biological parameters can include tissue chemical concentrations in ecological receptors, bioassay responses, bioaccumulation levels, and benthic community metrics. Chemical parameters can include sediment concentrations of a variety of potentially toxic chemicals. Indirectly, contaminated harbor sediment can impact shellfish, fish, birds, and marine mammals, and human populations. This paper summarizes the methods used to define background levels for chemical and biological parameters from a survey of ecological risk investigations of marine harbor sediment at California Navy bases. Background levels for regional biological indices used to quantify ecological risks for benthic communities are also described. Generally, background stations are positioned in relatively clean areas exhibiting the same physical and general chemical characteristics as nearby areas with contaminated harbor sediment. The number of background stations and the number of sample replicates per background station depend on the statistical design of the sediment ecological risk investigation, developed through the data quality objective (DQO) process. Biological data from the background stations can be compared to data from a contaminated site by using minimum or maximum background levels or comparative statistics. In Navy ecological risk assessments (ERA's), calculated background levels and appropriate ecological risk screening criteria are used to identify sampling stations and sites with contaminated sediments.

Parameter Estimation and Model Selection in Computational Biology

PubMed Central

Lillacci, Gabriele; Khammash, Mustafa

2010-01-01

A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262

Multiphysics and Thermal Response Models to Improve Accuracy of Local Temperature Estimation in Rat Cortex under Microwave Exposure

PubMed Central

Kodera, Sachiko; Gomez-Tames, Jose; Hirata, Akimasa; Masuda, Hiroshi; Arima, Takuji; Watanabe, Soichi

2017-01-01

The rapid development of wireless technology has led to widespread concerns regarding adverse human health effects caused by exposure to electromagnetic fields. Temperature elevation in biological bodies is an important factor that can adversely affect health. A thermophysiological model is desired to quantify microwave (MW) induced temperature elevations. In this study, parameters related to thermophysiological responses for MW exposures were estimated using an electromagnetic-thermodynamics simulation technique. To the authors’ knowledge, this is the first study in which parameters related to regional cerebral blood flow in a rat model were extracted at a high degree of accuracy through experimental measurements for localized MW exposure at frequencies exceeding 6 GHz. The findings indicate that the improved modeling parameters yield computed results that match well with the measured quantities during and after exposure in rats. It is expected that the computational model will be helpful in estimating the temperature elevation in the rat brain at multiple observation points (that are difficult to measure simultaneously) and in explaining the physiological changes in the local cortex region. PMID:28358345

[Mechanisms and applications of transcutaneous electrical nerve stimulation in analgesia].

PubMed

Tang, Zheng-Yu; Wang, Hui-Quan; Xia, Xiao-Lei; Tang, Yi; Peng, Wei-Wei; Hu, Li

2017-06-25

Transcutaneous electrical nerve stimulation (TENS), as a non-pharmacological and non-invasive analgesic therapy with low-cost, has been widely used to relieve pain in various clinical applications, by delivering current pulses to the skin area to activate the peripheral nerve fibers. Nevertheless, analgesia induced by TENS varied in the clinical practice, which could be caused by the fact that TENS with different stimulus parameters has different biological mechanisms in relieving pain. Therefore, to advance our understanding of TENS in various basic and clinical studies, we discussed (1) neurophysiological and biochemical mechanisms of TENS-induced analgesia; (2) relevant factors that may influence analgesic effects of TENS from the perspectives of stimulus parameters, including stimulated position, pulse parameters (current intensity, frequency, and pulse width), stimulus duration and used times in each day; and (3) applications of TENS in relieving clinical pain, including post-operative pain, chronic low back pain and labor pain. Finally, we propose that TENS may involve multiple and complex psychological neurophysiological mechanisms, and suggest that different analgesic effects of TENS with different stimulus parameters should be taken into consideration in clinical applications. In addition, to optimize analgesic effect, we recommend that individual-based TENS stimulation parameters should be designed by considering individual differences among patients, e.g., adaptively adjusting the stimulation parameters based on the dynamic ratings of patients' pain.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Combining fungal biopesticides and insecticide-treated bednets to enhance malaria control.

PubMed

Hancock, Penelope A

2009-10-01

In developing strategies to control malaria vectors, there is increased interest in biological methods that do not cause instant vector mortality, but have sublethal and lethal effects at different ages and stages in the mosquito life cycle. These techniques, particularly if integrated with other vector control interventions, may produce substantial reductions in malaria transmission due to the total effect of alterations to multiple life history parameters at relevant points in the life-cycle and transmission-cycle of the vector. To quantify this effect, an analytically tractable gonotrophic cycle model of mosquito-malaria interactions is developed that unites existing continuous and discrete feeding cycle approaches. As a case study, the combined use of fungal biopesticides and insecticide treated bednets (ITNs) is considered. Low values of the equilibrium EIR and human prevalence were obtained when fungal biopesticides and ITNs were combined, even for scenarios where each intervention acting alone had relatively little impact. The effect of the combined interventions on the equilibrium EIR was at least as strong as the multiplicative effect of both interventions. For scenarios representing difficult conditions for malaria control, due to high transmission intensity and widespread insecticide resistance, the effect of the combined interventions on the equilibrium EIR was greater than the multiplicative effect, as a result of synergistic interactions between the interventions. Fungal biopesticide application was found to be most effective when ITN coverage was high, producing significant reductions in equilibrium prevalence for low levels of biopesticide coverage. By incorporating biological mechanisms relevant to vectorial capacity, continuous-time vector population models can increase their applicability to integrated vector management.

Sleep and biological parameters in professional burnout: A psychophysiological characterization

PubMed Central

Sauvet, Fabien; Gomez-Merino, Danielle; Boucher, Thierry; Elbaz, Maxime; Delafosse, Jean Yves; Leger, Damien; Chennaoui, Mounir

2018-01-01

Professional burnout syndrome has been described in association with insomnia and metabolic, inflammatory and immune correlates. We investigated the interest of exploring biological parameters and sleep disturbances in relation to burnout symptoms among white-collar workers. Fifty-four participants with burnout were compared to 86 healthy control participants in terms of professional rank level, sleep, job strain (Karasek questionnaire), social support, anxiety and depression (HAD scale). Fasting concentrations of glycaemia, glycosylated hemoglobin (HbA1C), total-cholesterol, triglycerides, C-reactive protein (CRP), thyroid stimulating hormone (TSH), 25-hydroxyvitamin D (25[OH]D), and white blood cell (WBC) counts were assessed. Analysis of variance and a forward Stepwise Multiple Logistic Regression were made to identify predictive factors of burnout. Besides reporting more job strain (in particular job control p = 0.02), higher levels of anxiety (p<0.001), and sleep disorders related to insomnia (OR = 21.5, 95%CI = 8.8–52.3), participants with burnout presented higher levels of HbA1C, glycaemia, CRP, lower levels of 25(OH)D, higher number of leukocytes, neutrophils and monocytes (P<0.001 for all) and higher total-cholesterol (P = 0.01). In particular, when HbA1c is > 3.5%, the prevalence of burnout increases from 16.6% to 60.0% (OR = 4.3, 95%CI = 2.8–6.9). Strong significant positive correlation existed between HbA1C and the two dimensions (emotional exhaustion and depersonalization (r = 0.79 and r = 0.71, p<0.01)) of burnout. Models including job strain, job satisfaction, anxiety and insomnia did not predict burnout (p = 0.30 and p = 0.50). However, when HbA1C levels is included, the prediction of burnout became significant (P = 0.03). Our findings demonstrated the interest of sleep and biological parameters, in particular HbA1C levels, in the characterization of professional burnout. PMID:29385150

Sleep and biological parameters in professional burnout: A psychophysiological characterization.

PubMed

Metlaine, Arnaud; Sauvet, Fabien; Gomez-Merino, Danielle; Boucher, Thierry; Elbaz, Maxime; Delafosse, Jean Yves; Leger, Damien; Chennaoui, Mounir

2018-01-01

Professional burnout syndrome has been described in association with insomnia and metabolic, inflammatory and immune correlates. We investigated the interest of exploring biological parameters and sleep disturbances in relation to burnout symptoms among white-collar workers. Fifty-four participants with burnout were compared to 86 healthy control participants in terms of professional rank level, sleep, job strain (Karasek questionnaire), social support, anxiety and depression (HAD scale). Fasting concentrations of glycaemia, glycosylated hemoglobin (HbA1C), total-cholesterol, triglycerides, C-reactive protein (CRP), thyroid stimulating hormone (TSH), 25-hydroxyvitamin D (25[OH]D), and white blood cell (WBC) counts were assessed. Analysis of variance and a forward Stepwise Multiple Logistic Regression were made to identify predictive factors of burnout. Besides reporting more job strain (in particular job control p = 0.02), higher levels of anxiety (p<0.001), and sleep disorders related to insomnia (OR = 21.5, 95%CI = 8.8-52.3), participants with burnout presented higher levels of HbA1C, glycaemia, CRP, lower levels of 25(OH)D, higher number of leukocytes, neutrophils and monocytes (P<0.001 for all) and higher total-cholesterol (P = 0.01). In particular, when HbA1c is > 3.5%, the prevalence of burnout increases from 16.6% to 60.0% (OR = 4.3, 95%CI = 2.8-6.9). Strong significant positive correlation existed between HbA1C and the two dimensions (emotional exhaustion and depersonalization (r = 0.79 and r = 0.71, p<0.01)) of burnout. Models including job strain, job satisfaction, anxiety and insomnia did not predict burnout (p = 0.30 and p = 0.50). However, when HbA1C levels is included, the prediction of burnout became significant (P = 0.03). Our findings demonstrated the interest of sleep and biological parameters, in particular HbA1C levels, in the characterization of professional burnout.

Examining the Effect of Multiple Writing Tasks on Year 10 Biology Students' Understandings of Cell and Molecular Biology Concepts

ERIC Educational Resources Information Center

Hand, Brian; Hohenshell, Liesl; Prain, Vaughan

2007-01-01

This paper reports on a study that examined the cumulative effects on students' learning of science, and perceptions of the role of writing in learning, when the students engaged in multiple writing tasks with planning strategy support. The study was conducted with Year 10 biology students who completed two consecutive units on Cells and Molecular…

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

PubMed

Chiang, Austin W T; Liu, Wei-Chung; Charusanti, Pep; Hwang, Ming-Jing

2014-01-15

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

Mosaicing of single plane illumination microscopy images using groupwise registration and fast content-based image fusion

NASA Astrophysics Data System (ADS)

Preibisch, Stephan; Rohlfing, Torsten; Hasak, Michael P.; Tomancak, Pavel

2008-03-01

Single Plane Illumination Microscopy (SPIM; Huisken et al., Nature 305(5686):1007-1009, 2004) is an emerging microscopic technique that enables live imaging of large biological specimens in their entirety. By imaging the living biological sample from multiple angles SPIM has the potential to achieve isotropic resolution throughout even relatively large biological specimens. For every angle, however, only a relatively shallow section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. In order to produce a single, uniformly high resolution image, we propose here an image mosaicing algorithm that combines state of the art groupwise image registration for alignment with content-based image fusion to prevent degrading of the fused image due to regional blurring of the input images. For the registration stage, we introduce an application-specific groupwise transformation model that incorporates per-image as well as groupwise transformation parameters. We also propose a new fusion algorithm based on Gaussian filters, which is substantially faster than fusion based on local image entropy. We demonstrate the performance of our mosaicing method on data acquired from living embryos of the fruit fly, Drosophila, using four and eight angle acquisitions.

Effects of prolonged exposure of lettuce seeds to HZE particles on orbital stations

NASA Astrophysics Data System (ADS)

Nevzgodina, L. V.; Maksimova, E. N.; Kaminskaya, E. V.

In a study of the biological effects of cosmic HZE particles, lettuce (Lactuca sativa) seeds were flown on the orbital stations Salyut 6 and 7 for varying periods of time (from 40 to 457 days). The dependence of the biological damage on flight duration, physical parameters and the fact of passage of an HZE particle through the seed was estimated using the criterion of the frequency of aberrant cells. The arrangement of the flight biological container Biobloc made it possible to trace the location of tracks of individual HZE particles with Z>=6 and LET 200 keV/um. In seeds hit by HZE particles, for all exposure times, a statistically significant much higher yield of aberrant cells and also of cells containing multiple chromosome aberrations was observed than in the control material. The frequency of aberrant cells is markedly higher (by a factor of 1,5) in seeds hit than in non-hit ones. The changes of the yield of aberrant cells as a function of the absorbed dose (3.2-63.4 mGy) and the fluence (4.8-44.2 particles/cm2) are linear for the exposure duration ranging from 40 to 457 days.

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation.

PubMed

Zimmer, Christoph

2016-01-01

Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models. The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique. The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models.

DNA Assembly in 3D Printed Fluidics

PubMed Central

Patrick, William G.; Nielsen, Alec A. K.; Keating, Steven J.; Levy, Taylor J.; Wang, Che-Wei; Rivera, Jaime J.; Mondragón-Palomino, Octavio; Carr, Peter A.; Voigt, Christopher A.; Oxman, Neri; Kong, David S.

2015-01-01

The process of connecting genetic parts—DNA assembly—is a foundational technology for synthetic biology. Microfluidics present an attractive solution for minimizing use of costly reagents, enabling multiplexed reactions, and automating protocols by integrating multiple protocol steps. However, microfluidics fabrication and operation can be expensive and requires expertise, limiting access to the technology. With advances in commodity digital fabrication tools, it is now possible to directly print fluidic devices and supporting hardware. 3D printed micro- and millifluidic devices are inexpensive, easy to make and quick to produce. We demonstrate Golden Gate DNA assembly in 3D-printed fluidics with reaction volumes as small as 490 nL, channel widths as fine as 220 microns, and per unit part costs ranging from $0.61 to $5.71. A 3D-printed syringe pump with an accompanying programmable software interface was designed and fabricated to operate the devices. Quick turnaround and inexpensive materials allowed for rapid exploration of device parameters, demonstrating a manufacturing paradigm for designing and fabricating hardware for synthetic biology. PMID:26716448

Fate of Engineered Nanoparticles: Implications in the ...

EPA Pesticide Factsheets

The increased flux of the engineered nanoparticles (ENPs) in consumer and commercial products has become a viable threat, particularly if their release affects the environment. The aim of this paper is to review the recent literature results pertaining to the underlying mechanisms initiating the transformations of ENPs for both, the biotic and abiotic processes. The transformation of ENPs is necessarily interrelated to multiple environmental aspects and many concepts overlap. Physicochemical, macromolecular, and biological pathways contribute to assessing the impact of the altered activities of ENPs on the surrounding environmental matrices. Transformations involving both organic and inorganic ligands are vital in soil and water systems. Energy-efficient biocatalytic pathways can easily facilitate biotransformation involving enzymatic reactions and biomolecules. The relationship between physicochemical and biological parameters triggers transformation, greatly affecting the bioavailability and aging of ENPs to various extents. Therefore, the interaction of ENPs in environmental matrices is significant in understanding the risk of potential exposure and/or uptake by biota. Submitted to Elsevier journal, Coordination Chemistry Reviews

Effects of radio frequency identification-related radiation on in vitro biologics.

PubMed

Uysal, Ismail; Hohberger, Clive; Rasmussen, R Scott; Ulrich, David A; Emond, Jean-Pierre; Gutierrez, Alfonso

2012-01-01

The recent developments on the use of e-pedigree to identify the chain of custody of drugs suggests the use of advanced track and trace technologies such as two-dimensional barcodes and radio frequency identification (RFID) tags. RFID technology is used mainly for valuable commodities such as pharmaceutical products while incorporating additional functionalities like monitoring environmental variables to ensure product safety and quality. In its guidance for the use of RFID technologies for drugs (Compliance Policy Guide Section 400.210), the Food and Drug Administration outlined multiple parameters that would apply to any study or application using RFID. However, drugs approved under a Biologics License Application or protein drugs covered by a New Drug Application were excluded mainly due to concerns about the effects of radio frequency radiation (thermal and/or non-thermal) on biologics. Even though the thermal effects of radio frequency on biologics are relatively well understood, there are few studies in the literature about the non-thermal effects of radio frequency with regards to the protein structure integrity. In this paper, we analyze the non-thermal effects of radio frequency radiation by exposing a wide variety of biologics including biopharmaceuticals with vaccines, hormones, and immunoglobulins, as well as cellular blood products such as red blood cells and whole blood-derived platelets as well as fresh frozen plasma. In order to represent the majority of the frequency spectrum used in RFID applications, five different frequencies (13.56 MHz, 433 MHz, 868 MHz, 915 MHz, and 2.4 GHz) are used to account for the most commonly used international frequency bands for RFID. With the help of specialized radio frequency signal-generating hardware, magnetic and electromagnetic fields are created around the exposed products with power levels greater than Federal Communications Commission-regulated limits. The in vitro test results on more than 100 biopharmaceutical products from eight major pharmaceutical companies as well, as different blood products, show no non-thermal effect by radio frequency radiation. Forthcoming requirements, such as the California Board of Pharmacy Track and Trace initiative regarding the use of e-pedigree to identify the chain of custody of drugs, suggest the use of advanced track and trace technologies such as two-dimensional barcodes and radio frequency identification (RFID) tags. When used for pharmaceuticals, RFID technology can support additional functionalities like monitoring temperature to ensure product safety. In its guidance for the use of RFID technologies for drugs, the Food and Drug Administration outlined multiple parameters that would apply to pilot studies using RFID while excluding drugs approved under a Biologics License Application or protein drugs covered by a New Drug Application due to concerns about the effects of radio frequency radiation on biologics. Even though the effects of radio frequency on biologics due to temperature changes are relatively well understood, there are few studies in the literature about other effects of radio frequency that can occur without a noticeable change in temperature. In this paper, we expose a wide variety of biologics including biopharmaceuticals to radio frequency radiation at different frequencies, as well as cellular blood products and plasma to high frequency radiation. The in vitro test results show no detectable effect due to radio frequency radiation.

Membrane tension: A challenging but universal physical parameter in cell biology.

PubMed

Pontes, Bruno; Monzo, Pascale; Gauthier, Nils C

2017-11-01

The plasma membrane separates the interior of cells from the outside environment. The membrane tension, defined as the force per unit length acting on a cross-section of membrane, regulates many vital biological processes. In this review, we summarize the first historical findings and the latest advances, showing membrane tension as an important physical parameter in cell biology. We also discuss how this parameter must be better integrated and we propose experimental approaches for key unanswered questions. Copyright © 2017 Elsevier Ltd. All rights reserved.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Environmental parameters of the Tennessee River in Alabama. 2: Physical, chemical, and biological parameters. [biological and chemical effects of thermal pollution from nuclear power plants on water quality

NASA Technical Reports Server (NTRS)

Rosing, L. M.

1976-01-01

Physical, chemical and biological water quality data from five sites in the Tennessee River, two in Guntersville Reservoir and three in Wheeler Reservoir were correlated with climatological data for three annual cycles. Two of the annual cycles are for the years prior to the Browns Ferry Nuclear Power Plant operations and one is for the first 14 months of Plant operations. A comparison of the results of the annual cycles indicates that two distinct physical conditions in the reservoirs occur, one during the warm months when the reservoirs are at capacity and one during the colder winter months when the reservoirs have been drawn-down for water storage during the rainy months and for weed control. The wide variations of physical and chemical parameters to which the biological organisms are subjected on an annual basis control the biological organisms and their population levels. A comparison of the parameters of the site below the Power plant indicates that the heated effluent from the plant operating with two of the three reactors has not had any effect on the organisms at this site. Recommendations given include the development of prediction mathematical models (statistical analysis) for the physical and chemical parameters under specific climatological conditions which affect biological organisms. Tabulated data of chemical analysis of water and organism populations studied is given.

Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study

PubMed Central

Andrzejewski, Piotr; Baltzer, Pascal; Polanec, Stephan H.; Sturdza, Alina; Georg, Dietmar; Helbich, Thomas H.; Karanikas, Georgios; Grimm, Christoph; Polterauer, Stephan; Poetter, Richard; Wadsak, Wolfgang; Mitterhauser, Markus; Georg, Petra

2016-01-01

Objectives To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). Materials and Methods Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. Results All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05–0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. Conclusion MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT. PMID:27167829

Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study.

PubMed

Pinker, Katja; Andrzejewski, Piotr; Baltzer, Pascal; Polanec, Stephan H; Sturdza, Alina; Georg, Dietmar; Helbich, Thomas H; Karanikas, Georgios; Grimm, Christoph; Polterauer, Stephan; Poetter, Richard; Wadsak, Wolfgang; Mitterhauser, Markus; Georg, Petra

2016-01-01

To investigate fused multiparametric positron emission tomography/magnetic resonance imaging (MP PET/MRI) at 3T in patients with locally advanced cervical cancer, using high-resolution T2-weighted, contrast-enhanced MRI (CE-MRI), diffusion-weighted imaging (DWI), and the radiotracers [18F]fluorodeoxyglucose ([18F]FDG) and [18F]fluoromisonidazol ([18F]FMISO) for the non-invasive detection of tumor heterogeneity for an improved planning of chemo-radiation therapy (CRT). Sixteen patients with locally advanced cervix were enrolled in this IRB approved and were examined with fused MP [18F]FDG/ [18F]FMISO PET/MRI and in eleven patients complete data sets were acquired. MP PET/MRI was assessed for tumor volume, enhancement (EH)-kinetics, diffusivity, and [18F]FDG/ [18F]FMISO-avidity. Descriptive statistics and voxel-by-voxel analysis of MRI and PET parameters were performed. Correlations were assessed using multiple correlation analysis. All tumors displayed imaging parameters concordant with cervix cancer, i.e. type II/III EH-kinetics, restricted diffusivity (median ADC 0.80x10-3mm2/sec), [18F]FDG- (median SUVmax16.2) and [18F]FMISO-avidity (median SUVmax3.1). In all patients, [18F]FMISO PET identified the hypoxic tumor subvolume, which was independent of tumor volume. A voxel-by-voxel analysis revealed only weak correlations between the MRI and PET parameters (0.05-0.22), indicating that each individual parameter yields independent information and the presence of tumor heterogeneity. MP [18F]FDG/ [18F]FMISO PET/MRI in patients with cervical cancer facilitates the acquisition of independent predictive and prognostic imaging parameters. MP [18F]FDG/ [18F]FMISO PET/MRI enables insights into tumor biology on multiple levels and provides information on tumor heterogeneity, which has the potential to improve the planning of CRT.

DAMBE7: New and Improved Tools for Data Analysis in Molecular Biology and Evolution.

PubMed

Xia, Xuhua

2018-06-01

DAMBE is a comprehensive software package for genomic and phylogenetic data analysis on Windows, Linux, and Macintosh computers. New functions include imputing missing distances and phylogeny simultaneously (paving the way to build large phage and transposon trees), new bootstrapping/jackknifing methods for PhyPA (phylogenetics from pairwise alignments), and an improved function for fast and accurate estimation of the shape parameter of the gamma distribution for fitting rate heterogeneity over sites. Previous method corrects multiple hits for each site independently. DAMBE's new method uses all sites simultaneously for correction. DAMBE, featuring a user-friendly graphic interface, is freely available from http://dambe.bio.uottawa.ca (last accessed, April 17, 2018).

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

PubMed

Villaverde, Alejandro F; Banga, Julio R

2017-11-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.

A biological quality index for volcanic Andisols and Aridisols (Canary Islands, Spain): variations related to the ecosystem degradation.

PubMed

Armas, Cecilia María; Santana, Bayanor; Mora, Juan Luis; Notario, Jesús Santiago; Arbelo, Carmen Dolores; Rodríguez-Rodríguez, Antonio

2007-05-25

The aim of this work is to identify indicators of biological activity in soils from the Canary Islands, by studying the variation of selected biological parameters related to the processes of deforestation and accelerated soil degradation affecting the Canarian natural ecosystems. Ten plots with different degrees of maturity/degradation have been selected in three typical habitats in the Canary Islands: laurel forest, pine forest and xerophytic scrub with Andisols and Aridisols as the most common soils. The studied characteristics in each case include total organic carbon, field soil respiration, mineralized carbon after laboratory incubation, microbial biomass carbon, hot water-extractable carbon and carboxymethylcellulase, beta-d-glucosidase and dehydrogenase activities. A Biological Quality Index (BQI) has been designed on the basis of a regression model using these variables, assuming that the total soil organic carbon content is quite stable in nearly mature ecosystems. Total carbon in mature ecosystems has been related to significant biological variables (hot water-extractable carbon, soil respiration and carboxymethylcellulase, beta-d-glucosidase and dehydrogenase activities), accounting for nearly 100% of the total variance by a multiple regression analysis. The index has been calculated as the ratio of the value calculated from the regression model and the actual measured value. The obtained results show that soils in nearly mature ecosystems have BQI values close to unit, whereas those in degraded ecosystems range between 0.24 and 0.97, depending on the degradation degree.

Neuregulin in Cardiovascular Development and Disease

PubMed Central

Odiete, Oghenerukevwe; Hill, Michael F.; Sawyer, Douglas B.

2013-01-01

Studies in genetically modified mice have demonstrated that neuregulin-1 (NRG-1), along with the erythroblastic leukemia viral oncogene homolog (ErbB) 2, 3, and 4 receptor tyrosine kinases, is necessary for multiple aspects of cardiovascular development. These observations stimulated in vitro and in vivo animal studies, implicating NRG-1/ErbB signaling in the regulation of cardiac cell biology throughout life. Cardiovascular effects of ErbB2-targeted cancer therapies provide evidence in humans that ErbB signaling plays a role in the maintenance of cardiac function. These and other studies suggest a conceptual model in which a key function of NRG-1/ErbB signaling is to mediate adaptations of the heart to physiological and pathological stimuli through activation of intracellular kinase cascades that regulate tissue plasticity. Recent work implicates NRG-1/ErbB signaling in the regulation of multiple aspects of cardiovascular biology, including angiogenesis, blood pressure, and skeletal muscle responses to exercise. The therapeutic potential of recombinant NRG-1 as a potential treatment for heart failure has been demonstrated in animal models and is now being explored in clinical studies. NRG-1 is found in human serum and plasma, and it correlates with some clinical parameters, suggesting that it may have value as an indicator of prognosis. In this review, we bring together this growing literature on NRG-1 and its significance in cardiovascular development and disease. PMID:23104879

Monitoring corneal crosslinking using phase-decorrelation OCT (Conference Presentation)

NASA Astrophysics Data System (ADS)

Blackburn, Brecken J.; Gu, Shi; Jenkins, Michael W.; Rollins, Andrew M.

2017-02-01

Viscosity is often a critical characteristic of biological fluids such as blood and mucus. However, traditional rheology is often inadequate when only small quantities of sample are available. A robust method to measure viscosity of microquantities of biological samples could lead to a better understanding and diagnosis of diseases. Here, we present a method to measure viscosity by observing particle Brownian motion within a sample. M-mode optical coherence tomography (OCT) imaging, obtained with a phase-sensitive 47 kHz spectral domain system, yields a viscosity measurement from multiple 200-1000 microsecond frames. This very short period of continuous acquisition, as compared to laser speckle decorrelation, decreases sensitivity to bulk motion, thereby potentially enabling in vivo and in situ applications. The theory linking g(1) first-order image autocorrelation to viscosity is derived from first principles of Brownian motion and the Stokes-Einstein relation. To improve precision, multiple windows acquired over 500 milliseconds are analyzed and the resulting linear fit parameters are averaged. Verification experiments were performed with 200 µL samples of glycerol and water with polystyrene microbeads. Lateral bulk motion up to 2 mm/s was tolerated and accurate viscosity measurements were obtained to a depth of 400 µm or more. Additionally, the method measured a significant decrease of the apparent diffusion constant of soft tissue after formalin fixation, suggesting potential for mapping tissue stiffness over a volume.

Clinical Pharmacokinetics and Pharmacodynamics of Biologic Therapeutics for Treatment of Systemic Lupus Erythematosus

PubMed Central

Yu, Tian; Enioutina, Elena Y.; Brunner, Hermine I.; Vinks, Alexander A.

2017-01-01

Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease with potentially severe clinical manifestation that mainly affects women of childbearing age. Patients who do not respond to standard-of-care therapies, such as corticosteroids and immunosuppressants, require biologic therapeutics that specifically target a single or multiple SLE pathogenesis pathways. This review summarizes the clinical pharmacokinetic and pharmacodynamic characteristics of biologic agents that are approved, used off-label, or in the active pipeline of drug development for SLE patients. Depending on the type of target, the interacting biologics may exhibit linear (non-specific) or nonlinear (target-mediated) disposition profiles, with terminal half-lives varying from approximately 1 week to 1 month. Biologics given by subcutaneous administration, which offers dosing flexibility over intravenous administration, demonstrated a relatively slow absorption with a time to maximum concentration of approximately 1 day to 2 weeks and a variable bioavailability of 30–82 %. The population pharmacokinetics of monoclonal antibodies were best described by a two-compartment model with central clearance and steady-state volume of distribution ranging from 0.176 to 0.215 L/day and 3.60–5.29 L, respectively. The between-subject variability in pharmacokinetic parameters were moderate (20–79 %) and could be partially explained by body size. The development of linked pharmacokinetic-pharmacodynamic models incorporating SLE disease biomarkers are an attractive strategy for use in dosing regimen simulation and optimization. The relationship between efficacy/adverse events and biologic concentration should be evaluated to improve clinical trial outcomes, especially for biologics in the advanced phase of drug development. New strategies, such as model-based precision dosing dashboards, could be utilized to incorporate information collected from therapeutic drug monitoring into pharmacokinetic/pharmacodynamic models to enable individualized dosing in real time. PMID:27384528

Clinical Pharmacokinetics and Pharmacodynamics of Biologic Therapeutics for Treatment of Systemic Lupus Erythematosus.

PubMed

Yu, Tian; Enioutina, Elena Y; Brunner, Hermine I; Vinks, Alexander A; Sherwin, Catherine M

2017-02-01

Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease with potentially severe clinical manifestation that mainly affects women of child-bearing age. Patients who do not respond to standard-of-care therapies, such as corticosteroids and immunosuppressants, require biologic therapeutics that specifically target a single or multiple SLE pathogenesis pathways. This review summarizes the clinical pharmacokinetic and pharmacodynamic characteristics of biologic agents that are approved, used off-label, or in the active pipeline of drug development for SLE patients. Depending on the type of target, the interacting biologics may exhibit linear (non-specific) or non-linear (target-mediated) disposition profiles, with terminal half-lives varying from approximately 1 week to 1 month. Biologics given by subcutaneous administration, which offers dosing flexibility over intravenous administration, demonstrated a relatively slow absorption with a time to maximum concentration of approximately 1 day to 2 weeks and a variable bioavailability of 30-82 %. The population pharmacokinetics of monoclonal antibodies were best described by a two-compartment model with central clearance and steady-state volume of distribution ranging from 0.176 to 0.215 L/day and 3.60-5.29 L, respectively. The between-subject variability in pharmacokinetic parameters were moderate (20-79 %) and could be partially explained by body size. The development of linked pharmacokinetic-pharmacodynamic models incorporating SLE disease biomarkers are an attractive strategy for use in dosing regimen simulation and optimization. The relationship between efficacy/adverse events and biologic concentration should be evaluated to improve clinical trial outcomes, especially for biologics in the advanced phase of drug development. New strategies, such as model-based precision dosing dashboards, could be utilized to incorporate information collected from therapeutic drug monitoring into pharmacokinetic/pharmacodynamic models to enable individualized dosing in real time.

Combining Costs and Benefits of Animal Activities to Assess Net Yield Outcomes in Apple Orchards

PubMed Central

Luck, Gary W.

2016-01-01

Diverse animal communities influence ecosystem function in agroecosystems through positive and negative plant-animal interactions. Yet, past research has largely failed to examine multiple interactions that can have opposing impacts on agricultural production in a given context. We collected data on arthropod communities and yield quality and quantity parameters (fruit set, yield loss and net outcomes) in three major apple-growing regions in south-eastern Australia. We quantified the net yield outcome (accounting for positive and negative interactions) of multiple animal activities (pollination, fruit damage, biological control) across the entire growing season on netted branches, which excluded vertebrate predators of arthropods, and open branches. Net outcome was calculated as the number of undamaged fruit at harvest as a proportion of the number of blossoms (i.e., potential fruit yield). Vertebrate exclusion resulted in lower levels of fruit set and higher levels of arthropod damage to apples, but did not affect net outcomes. Yield quality and quantity parameters (fruit set, yield loss, net outcomes) were not directly associated with arthropod functional groups. Model variance and significant differences between the ratio of pest to beneficial arthropods between regions indicated that complex relationships between environmental factors and multiple animal interactions have a combined effect on yield. Our results show that focusing on a single crop stage, species group or ecosystem function/service can overlook important complexity in ecological processes within the system. Accounting for this complexity and quantifying the net outcome of ecological interactions within the system, is more informative for research and management of biodiversity and ecosystem services in agricultural landscapes. PMID:27391022

Combining Costs and Benefits of Animal Activities to Assess Net Yield Outcomes in Apple Orchards.

PubMed

Saunders, Manu E; Luck, Gary W

2016-01-01

Diverse animal communities influence ecosystem function in agroecosystems through positive and negative plant-animal interactions. Yet, past research has largely failed to examine multiple interactions that can have opposing impacts on agricultural production in a given context. We collected data on arthropod communities and yield quality and quantity parameters (fruit set, yield loss and net outcomes) in three major apple-growing regions in south-eastern Australia. We quantified the net yield outcome (accounting for positive and negative interactions) of multiple animal activities (pollination, fruit damage, biological control) across the entire growing season on netted branches, which excluded vertebrate predators of arthropods, and open branches. Net outcome was calculated as the number of undamaged fruit at harvest as a proportion of the number of blossoms (i.e., potential fruit yield). Vertebrate exclusion resulted in lower levels of fruit set and higher levels of arthropod damage to apples, but did not affect net outcomes. Yield quality and quantity parameters (fruit set, yield loss, net outcomes) were not directly associated with arthropod functional groups. Model variance and significant differences between the ratio of pest to beneficial arthropods between regions indicated that complex relationships between environmental factors and multiple animal interactions have a combined effect on yield. Our results show that focusing on a single crop stage, species group or ecosystem function/service can overlook important complexity in ecological processes within the system. Accounting for this complexity and quantifying the net outcome of ecological interactions within the system, is more informative for research and management of biodiversity and ecosystem services in agricultural landscapes.

Resilience of Key Biological Parameters of the Senegalese Flat Sardinella to Overfishing and Climate Change.

PubMed

Ba, Kamarel; Thiaw, Modou; Lazar, Najih; Sarr, Alassane; Brochier, Timothée; Ndiaye, Ismaïla; Faye, Alioune; Sadio, Oumar; Panfili, Jacques; Thiaw, Omar Thiom; Brehmer, Patrice

2016-01-01

The stock of the Senegalese flat sardinella, Sardinella maderensis, is highly exploited in Senegal, West Africa. Its growth and reproduction parameters are key biological indicators for improving fisheries management. This study reviewed these parameters using landing data from small-scale fisheries in Senegal and literature information dated back more than 25 years. Age was estimated using length-frequency data to calculate growth parameters and assess the growth performance index. With global climate change there has been an increase in the average sea surface temperature along the Senegalese coast but the length-weight parameters, sex ratio, size at first sexual maturity, period of reproduction and condition factor of S. maderensis have not changed significantly. The above parameters of S. maderensis have hardly changed, despite high exploitation and fluctuations in environmental conditions that affect the early development phases of small pelagic fish in West Africa. This lack of plasticity of the species regarding of the biological parameters studied should be considered when planning relevant fishery management plans.

A Hierarchical Bayesian Model for Calibrating Estimates of Species Divergence Times

PubMed Central

Heath, Tracy A.

2012-01-01

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to quantify his or her prior knowledge of the age of the ancestral node relative to the age of its calibrating fossil. The values of these parameters can, potentially, result in biased estimates of node ages if they lead to overly informative prior distributions. Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses with multiple fossil age constraints. This approach applies a Dirichlet process prior as a hyperprior on the parameters of calibration prior densities. Specifically, this model assumes that the rate parameters of exponential prior distributions on calibrated nodes are distributed according to a Dirichlet process, whereby the rate parameters are clustered into distinct parameter categories. Both simulated and biological data are analyzed to evaluate the performance of the Dirichlet process hyperprior. Compared with fixed exponential prior densities, the hierarchical Bayesian approach results in more accurate and precise estimates of internal node ages. When this hyperprior is applied using Markov chain Monte Carlo methods, the ages of calibrated nodes are sampled from mixtures of exponential distributions and uncertainty in the values of calibration density parameters is taken into account. PMID:22334343

Development of automated high throughput single molecular microfluidic detection platform for signal transduction analysis

NASA Astrophysics Data System (ADS)

Huang, Po-Jung; Baghbani Kordmahale, Sina; Chou, Chao-Kai; Yamaguchi, Hirohito; Hung, Mien-Chie; Kameoka, Jun

2016-03-01

Signal transductions including multiple protein post-translational modifications (PTM), protein-protein interactions (PPI), and protein-nucleic acid interaction (PNI) play critical roles for cell proliferation and differentiation that are directly related to the cancer biology. Traditional methods, like mass spectrometry, immunoprecipitation, fluorescence resonance energy transfer, and fluorescence correlation spectroscopy require a large amount of sample and long processing time. "microchannel for multiple-parameter analysis of proteins in single-complex (mMAPS)"we proposed can reduce the process time and sample volume because this system is composed by microfluidic channels, fluorescence microscopy, and computerized data analysis. In this paper, we will present an automated mMAPS including integrated microfluidic device, automated stage and electrical relay for high-throughput clinical screening. Based on this result, we estimated that this automated detection system will be able to screen approximately 150 patient samples in a 24-hour period, providing a practical application to analyze tissue samples in a clinical setting.

Aerated biofilters with multiple-level air injection configurations to enhance biological treatment of methane emissions.

PubMed

Farrokhzadeh, Hasti; Hettiaratchi, J Patrick A; Jayasinghe, Poornima; Kumar, Sunil

2017-09-01

Aiming to improve conventional methane biofilter performance, a multiple-level aeration biofilter design is proposed. Laboratory flow-through column experiments were conducted to evaluate three actively-aerated methane biofilter configurations. Columns were aerated at one, two, and three levels of the bed depth, with air introduced at flow rates calculated from methane oxidation reaction stoichiometry. Inlet methane loading rates were increased in five stages between 6 and 18mL/min. The effects of methane feeding rate, levels of aeration, and residence time on methane oxidation rates were determined. Samples collected after completion of flow-through experiments were used to determine methane oxidation kinetic parameters, V max , K m , and methanotrophic community distribution across biofilter columns. Results obtained from mixed variances analysis and response surfaces, as well as methanotrophic activity data, suggested that, biofilter column with two aeration levels has the most even performance over time, maintaining 85.1% average oxidation efficiency over 95days of experiments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Lidar inelastic multiple-scattering parameters of cirrus particle ensembles determined with geometrical-optics crystal phase functions.

PubMed

Reichardt, J; Hess, M; Macke, A

2000-04-20

Multiple-scattering correction factors for cirrus particle extinction coefficients measured with Raman and high spectral resolution lidars are calculated with a radiative-transfer model. Cirrus particle-ensemble phase functions are computed from single-crystal phase functions derived in a geometrical-optics approximation. Seven crystal types are considered. In cirrus clouds with height-independent particle extinction coefficients the general pattern of the multiple-scattering parameters has a steep onset at cloud base with values of 0.5-0.7 followed by a gradual and monotonic decrease to 0.1-0.2 at cloud top. The larger the scattering particles are, the more gradual is the rate of decrease. Multiple-scattering parameters of complex crystals and of imperfect hexagonal columns and plates can be well approximated by those of projected-area equivalent ice spheres, whereas perfect hexagonal crystals show values as much as 70% higher than those of spheres. The dependencies of the multiple-scattering parameters on cirrus particle spectrum, base height, and geometric depth and on the lidar parameters laser wavelength and receiver field of view, are discussed, and a set of multiple-scattering parameter profiles for the correction of extinction measurements in homogeneous cirrus is provided.

Quantitative Förster resonance energy transfer analysis for kinetic determinations of SUMO-specific protease.

PubMed

Liu, Yan; Song, Yang; Madahar, Vipul; Liao, Jiayu

2012-03-01

Förster resonance energy transfer (FRET) technology has been widely used in biological and biomedical research, and it is a very powerful tool for elucidating protein interactions in either dynamic or steady state. SUMOylation (the process of SUMO [small ubiquitin-like modifier] conjugation to substrates) is an important posttranslational protein modification with critical roles in multiple biological processes. Conjugating SUMO to substrates requires an enzymatic cascade. Sentrin/SUMO-specific proteases (SENPs) act as an endopeptidase to process the pre-SUMO or as an isopeptidase to deconjugate SUMO from its substrate. To fully understand the roles of SENPs in the SUMOylation cycle, it is critical to understand their kinetics. Here, we report a novel development of a quantitative FRET-based protease assay for SENP1 kinetic parameter determination. The assay is based on the quantitative analysis of the FRET signal from the total fluorescent signal at acceptor emission wavelength, which consists of three components: donor (CyPet-SUMO1) emission, acceptor (YPet) emission, and FRET signal during the digestion process. Subsequently, we developed novel theoretical and experimental procedures to determine the kinetic parameters, k(cat), K(M), and catalytic efficiency (k(cat)/K(M)) of catalytic domain SENP1 toward pre-SUMO1. Importantly, the general principles of this quantitative FRET-based protease kinetic determination can be applied to other proteases. Copyright © 2011 Elsevier Inc. All rights reserved.

Contact nanomechanical measurements with the AFM

NASA Astrophysics Data System (ADS)

Geisse, Nicholas

2013-03-01

The atomic force microscope (AFM) has found broad use in the biological sciences largely due to its ability to make measurements on unfixed and unstained samples under liquid. In addition to imaging at multiple spatial scales ranging from micro- to nanometer, AFMs are commonly used as nanomechanical probes. This is pertinent for cell biology, as it has been demonstrated that the geometrical and mechanical properties of the extracellular microenvironment are important in such processes as cancer, cardiovascular disease, muscular dystrophy, and even the control of cell life and death. Indeed, the ability to control and quantify these external geometrical and mechanical parameters arises as a key issue in the field. Because AFM can quantitatively measure the mechanical properties of various biological samples, novel insights to cell function and to cell-substrate interactions are now possible. As the application of AFM to these types of problems is widened, it is important to understand the performance envelope of the technique and its associated data analyses. This talk will discuss the important issues that must be considered when mechanical models are applied to real-world data. Examples of the effect of different model assumptions on our understanding of the measured material properties will be shown. Furthermore, specific examples of the importance of mechanical stimuli and the micromechanical environment to the structure and function of biological materials will be presented.

Changes in hormone and stress-inducing activities of municipal wastewater in a conventional activated sludge wastewater treatment plant.

PubMed

Wojnarowicz, Pola; Yang, Wenbo; Zhou, Hongde; Parker, Wayne J; Helbing, Caren C

2014-12-01

Conventional municipal wastewater treatment plants do not efficiently remove contaminants of emerging concern, and so are primary sources for contaminant release into the aquatic environment. Although these contaminants are present in effluents at ng-μg/L concentrations (i.e. microcontaminants), many compounds can act as endocrine disrupting compounds or stress-inducing agents at these levels. Chemical fate analyses indicate that additional levels of wastewater treatment reduce but do not always completely remove all microcontaminants. The removal of microcontaminants from wastewater does not necessarily correspond to a reduction in biological activity, as contaminant metabolites or byproducts may still be biologically active. To evaluate the efficacy of conventional municipal wastewater treatment plants to remove biological activity, we examined the performance of a full scale conventional activated sludge municipal wastewater treatment plant located in Guelph, Ontario, Canada. We assessed reductions in levels of conventional wastewater parameters and thyroid hormone disrupting and stress-inducing activities in wastewater at three phases along the treatment train using a C-fin assay. Wastewater treatment was effective at reducing total suspended solids, chemical and biochemical oxygen demand, and stress-inducing bioactivity. However, only minimal reduction was observed in thyroid hormone disrupting activities. The present study underscores the importance of examining multiple chemical and biological endpoints in evaluating and monitoring the effectiveness of wastewater treatment for removal of microcontaminants. Copyright © 2014 Elsevier Ltd. All rights reserved.

Homogenization Theory for the Prediction of Obstructed Solute Diffusivity in Macromolecular Solutions.

PubMed

Donovan, Preston; Chehreghanianzabi, Yasaman; Rathinam, Muruhan; Zustiak, Silviya Petrova

2016-01-01

The study of diffusion in macromolecular solutions is important in many biomedical applications such as separations, drug delivery, and cell encapsulation, and key for many biological processes such as protein assembly and interstitial transport. Not surprisingly, multiple models for the a-priori prediction of diffusion in macromolecular environments have been proposed. However, most models include parameters that are not readily measurable, are specific to the polymer-solute-solvent system, or are fitted and do not have a physical meaning. Here, for the first time, we develop a homogenization theory framework for the prediction of effective solute diffusivity in macromolecular environments based on physical parameters that are easily measurable and not specific to the macromolecule-solute-solvent system. Homogenization theory is useful for situations where knowledge of fine-scale parameters is used to predict bulk system behavior. As a first approximation, we focus on a model where the solute is subjected to obstructed diffusion via stationary spherical obstacles. We find that the homogenization theory results agree well with computationally more expensive Monte Carlo simulations. Moreover, the homogenization theory agrees with effective diffusivities of a solute in dilute and semi-dilute polymer solutions measured using fluorescence correlation spectroscopy. Lastly, we provide a mathematical formula for the effective diffusivity in terms of a non-dimensional and easily measurable geometric system parameter.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

PubMed

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

PubMed

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

PubMed Central

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology. PMID:23555205

Acquisition of Cooperative Small Unmanned Aerial Systems for Advancing Man Machine Interface Research

DTIC Science & Technology

2016-08-24

global sensor field of views (FOVs), mimicking biological systems such as an insect fly eye , but allowing multiple aperture configurations. Due to...synthetic, global sensor field of views (FOVs), mimicking biological systems such as an insect fly eye , but allowing multiple aperture configurations. Due to...such as an insect fly eye , but allowing multiple aperture configurations. Due to the desired nature of distributed networked aerial vehicles (for the

Single neuron modeling and data assimilation in BNST neurons

NASA Astrophysics Data System (ADS)

Farsian, Reza

Neurons, although tiny in size, are vastly complicated systems, which are responsible for the most basic yet essential functions of any nervous system. Even the most simple models of single neurons are usually high dimensional, nonlinear, and contain many parameters and states which are unobservable in a typical neurophysiological experiment. One of the most fundamental problems in experimental neurophysiology is the estimation of these parameters and states, since knowing their values is essential in identification, model construction, and forward prediction of biological neurons. Common methods of parameter and state estimation do not perform well for neural models due to their high dimensionality and nonlinearity. In this dissertation, two alternative approaches for parameters and state estimation of biological neurons have been demonstrated: dynamical parameter estimation (DPE) and a Markov Chain Monte Carlo (MCMC) method. The first method uses elements of chaos control and synchronization theory for parameter and state estimation. MCMC is a statistical approach which uses a path integral formulation to evaluate a mean and an error bound for these unobserved parameters and states. These methods have been applied to biological system of neurons in Bed Nucleus of Stria Termialis neurons (BNST) of rats. State and parameters of neurons in both systems were estimated, and their value were used for recreating a realistic model and predicting the behavior of the neurons successfully. The knowledge of biological parameters can ultimately provide a better understanding of the internal dynamics of a neuron in order to build robust models of neuron networks.

Cellular signaling identifiability analysis: a case study.

PubMed

Roper, Ryan T; Pia Saccomani, Maria; Vicini, Paolo

2010-05-21

Two primary purposes for mathematical modeling in cell biology are (1) simulation for making predictions of experimental outcomes and (2) parameter estimation for drawing inferences from experimental data about unobserved aspects of biological systems. While the former purpose has become common in the biological sciences, the latter is less common, particularly when studying cellular and subcellular phenomena such as signaling-the focus of the current study. Data are difficult to obtain at this level. Therefore, even models of only modest complexity can contain parameters for which the available data are insufficient for estimation. In the present study, we use a set of published cellular signaling models to address issues related to global parameter identifiability. That is, we address the following question: assuming known time courses for some model variables, which parameters is it theoretically impossible to estimate, even with continuous, noise-free data? Following an introduction to this problem and its relevance, we perform a full identifiability analysis on a set of cellular signaling models using DAISY (Differential Algebra for the Identifiability of SYstems). We use our analysis to bring to light important issues related to parameter identifiability in ordinary differential equation (ODE) models. We contend that this is, as of yet, an under-appreciated issue in biological modeling and, more particularly, cell biology. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Integrated Analysis of Pharmacologic, Clinical, and SNP Microarray Data using Projection onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing

PubMed Central

Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun; Campana, Dario; Evans, William E.; Pui, Ching-Hon; Relling, Mary V.

2010-01-01

Powerful methods for integrated analysis of multiple biological data sets are needed to maximize interpretation capacity and acquire meaningful knowledge. We recently developed Projection Onto the Most Interesting Statistical Evidence (PROMISE). PROMISE is a statistical procedure that incorporates prior knowledge about the biological relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical, and genome-wide genotype data that incorporating knowledge about the biological relationships among pharmacologic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE is a powerful statistical tool for identifying genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables. PMID:21516175

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

PubMed Central

Chen, Min; Hashimoto, Koichi

2017-01-01

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses. PMID:29206189

Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

NASA Astrophysics Data System (ADS)

Ojha, Lokendra Kumar

2017-07-01

The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

Stochastic and superharmonic stochastic resonances of a confined overdamped harmonic oscillator

NASA Astrophysics Data System (ADS)

Zhang, Lu; Lai, Li; Peng, Hao; Tu, Zhe; Zhong, Suchuan

2018-01-01

The dynamics of many soft condensed matter and biological systems is affected by space limitations, which produce some peculiar effects on the systems' stochastic resonance (SR) behavior. In this study, we propose a model where SR can be observed: a confined overdamped harmonic oscillator that is subjected to a sinusoidal driving force and is under the influence of a multiplicative white noise. The output response of the system is a periodic signal with harmonic frequencies that are odd multiples of the driving frequency. We verify the amplitude resonances at the driving frequencies and superharmonic frequencies that are equal to three, five, and seven times the driving frequency, using a numerical method based on the stochastic Taylor expansion. The synergistic effect of the multiplicative white noise, constant boundaries, and periodic driving force that can induce a SR in the output amplitude at the driving and superharmonic frequencies is found. The SR phenomenon found in this paper is sensitive to the driving amplitude and frequency, inherent potential parameter, and boundary width, thus leading to various resonance conditions. Therefore, the mechanism found could be beneficial for the characterization of these confined systems and could constitute an important tool for controlling their basic properties.

Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification.

PubMed

Harrington, S; Reeder, T W

2017-02-01

The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth-death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Biosimilarity and Interchangeability: Principles and Evidence: A Systematic Review.

PubMed

McKinnon, Ross A; Cook, Matthew; Liauw, Winston; Marabani, Mona; Marschner, Ian C; Packer, Nicolle H; Prins, Johannes B

2018-02-01

The efficacy, safety and immunogenicity risk of switching between an originator biologic and a biosimilar or from one biosimilar to another are of potential concern. The aim was to conduct a systematic literature review of the outcomes of switching between biologics and their biosimilars and identify any evidence gaps. A systematic literature search was conducted in PubMed, EMBASE and Cochrane Library from inception to June 2017. Relevant societal meetings were also checked. Peer-reviewed studies reporting efficacy and/or safety data on switching between originator and biosimilar products or from one biosimilar to another were selected. Studies with fewer than 20 switched patients were excluded. Data were extracted on interventions, study population, reason for treatment switching, efficacy outcomes, safety and anti-drug antibodies. The systematic literature search identified 63 primary publications covering 57 switching studies. The reason for switching was reported as non-medical in 50 studies (23 clinical, 27 observational). Seven studies (all observational) did not report whether the reasons for switching were medical or non-medical. In 38 of the 57 studies, fewer than 100 patients were switched. Follow-up after switching went beyond 1 year in eight of the 57 studies. Of the 57 studies, 33 included statistical analysis of disease activity or patient outcomes; the majority of these studies found no statistically significant differences between groups for main efficacy parameters (based on P < 0.05 or predefined acceptance ranges), although some studies observed changes for some parameters. Most studies reported similar safety profiles between groups. There are important evidence gaps around the safety of switching between biologics and their biosimilars. Sufficiently powered and appropriately statistically analysed clinical trials and pharmacovigilance studies, with long-term follow-ups and multiple switches, are needed to support decision-making around biosimilar switching.

Model-Based Analysis for Qualitative Data: An Application in Drosophila Germline Stem Cell Regulation

PubMed Central

Pargett, Michael; Rundell, Ann E.; Buzzard, Gregery T.; Umulis, David M.

2014-01-01

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques. This is the case for stem cell regulation mechanisms explored in the Drosophila germarium through fluorescent immunohistochemistry. To enable better integration of biological data with modeling in this and similar situations, we have developed a general parameter estimation process to quantitatively optimize models with qualitative data. The process employs a modified version of the Optimal Scaling method from social and behavioral sciences, and multi-objective optimization to evaluate the trade-off between fitting different datasets (e.g. wild type vs. mutant). Using only published imaging data in the germarium, we first evaluated support for a published intracellular regulatory network by considering alternative connections of the same regulatory players. Simply screening networks against wild type data identified hundreds of feasible alternatives. Of these, five parsimonious variants were found and compared by multi-objective analysis including mutant data and dynamic constraints. With these data, the current model is supported over the alternatives, but support for a biochemically observed feedback element is weak (i.e. these data do not measure the feedback effect well). When also comparing new hypothetical models, the available data do not discriminate. To begin addressing the limitations in data, we performed a model-based experiment design and provide recommendations for experiments to refine model parameters and discriminate increasingly complex hypotheses. PMID:24626201

A direct method for computing extreme value (Gumbel) parameters for gapped biological sequence alignments.

PubMed

Quinn, Terrance; Sinkala, Zachariah

2014-01-01

We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.

Pacific Circulation and the Resilience of its Equatorial Reefs

NASA Astrophysics Data System (ADS)

Cohen, A. L.; Drenkard, E.

2012-12-01

High rates of calcification by tropical reef-building corals are paramount to the maintenance of healthy reefs. Investigations of the impact of ocean acidification in both laboratory and field studies demonstrate unequivocally the dependence of coral and coral reef calcification on the carbonate ion concentration of seawater, a dependence predicted by fundamental laws of physical chemistry. Nevertheless, results from a new generation of experiments that exploit the biology of coral calcification, suggest that effects of ocean acidification can - in some instances - be mitigated with simultaneous manipulation of multiple factors. These laboratory results imply that coral reefs in regions projected to experience changes in, for example, nutrient delivery, light and flow, in addition to pH and carbonate ion concentration, may be more resilient (or vulnerable) to the effects of ocean acidification alone. If demonstrated to be true, these observations have profound implications for the conservation and management of coral reefs in the 21st century. We quantified spatial and temporal variability in rates of calcification of a dominant Indo-Pacific reef building coral across sites where changes in ocean circulation patterns drive variability in multiple physical, chemical and biological parameters. Such changes are occurring against a background of variability and trends in carbonate system chemistry. Our field data provide support for hypotheses based on laboratory observations, and show that impacts of ocean acidification on coral calcification can be partially and in some cases, fully, offset by simultaneous changes in multiple factors. Our results imply that projected changes in oceanic and atmospheric circulation patterns, driven by global warming, must be considered when predicting coral reef resilience, or vulnerability, to 21st century ocean acidification.

When things don't add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing.

PubMed

Galic, Nika; Sullivan, Lauren L; Grimm, Volker; Forbes, Valery E

2018-04-01

Ecosystems are exposed to multiple stressors which can compromise functioning and service delivery. These stressors often co-occur and interact in different ways which are not yet fully understood. Here, we applied a population model representing a freshwater amphipod feeding on leaf litter in forested streams. We simulated impacts of hypothetical stressors, individually and in pairwise combinations that target the individuals' feeding, maintenance, growth and reproduction. Impacts were quantified by examining responses at three levels of biological organisation: individual-level body sizes and cumulative reproduction, population-level abundance and biomass and ecosystem-level leaf litter decomposition. Interactive effects of multiple stressors at the individual level were mostly antagonistic, that is, less negative than expected. Most population- and ecosystem-level responses to multiple stressors were stronger than expected from an additive model, that is, synergistic. Our results suggest that across levels of biological organisation responses to multiple stressors are rarely only additive. We suggest methods for efficiently quantifying impacts of multiple stressors at different levels of biological organisation. © 2018 John Wiley & Sons Ltd/CNRS.

Influential Parameters for the Analysis of Intracellular Parasite Metabolomics.

PubMed

Carey, Maureen A; Covelli, Vincent; Brown, Audrey; Medlock, Gregory L; Haaren, Mareike; Cooper, Jessica G; Papin, Jason A; Guler, Jennifer L

2018-04-25

Metabolomics is increasingly popular for the study of pathogens. For the malaria parasite Plasmodium falciparum , both targeted and untargeted metabolomics have improved our understanding of pathogenesis, host-parasite interactions, and antimalarial drug treatment and resistance. However, purification and analysis procedures for performing metabolomics on intracellular pathogens have not been explored. Here, we purified in vitro -grown ring-stage intraerythrocytic P. falciparum parasites for untargeted metabolomics studies; the small size of this developmental stage amplifies the challenges associated with metabolomics studies as the ratio between host and parasite biomass is maximized. Following metabolite identification and data preprocessing, we explored multiple confounding factors that influence data interpretation, including host contamination and normalization approaches (including double-stranded DNA, total protein, and parasite numbers). We conclude that normalization parameters have large effects on differential abundance analysis and recommend the thoughtful selection of these parameters. However, normalization does not remove the contribution from the parasite's extracellular environment (culture media and host erythrocyte). In fact, we found that extraparasite material is as influential on the metabolome as treatment with a potent antimalarial drug with known metabolic effects (artemisinin). Because of this influence, we could not detect significant changes associated with drug treatment. Instead, we identified metabolites predictive of host and medium contamination that could be used to assess sample purification. Our analysis provides the first quantitative exploration of the effects of these factors on metabolomics data analysis; these findings provide a basis for development of improved experimental and analytical methods for future metabolomics studies of intracellular organisms. IMPORTANCE Molecular characterization of pathogens such as the malaria parasite can lead to improved biological understanding and novel treatment strategies. However, the distinctive biology of the Plasmodium parasite, including its repetitive genome and the requirement for growth within a host cell, hinders progress toward these goals. Untargeted metabolomics is a promising approach to learn about pathogen biology. By measuring many small molecules in the parasite at once, we gain a better understanding of important pathways that contribute to the parasite's response to perturbations such as drug treatment. Although increasingly popular, approaches for intracellular parasite metabolomics and subsequent analysis are not well explored. The findings presented in this report emphasize the critical need for improvements in these areas to limit misinterpretation due to host metabolites and to standardize biological interpretation. Such improvements will aid both basic biological investigations and clinical efforts to understand important pathogens. Copyright © 2018 Carey et al.

Mathematical and numerical challenges in living biological materials

NASA Astrophysics Data System (ADS)

Forest, M. Gregory; Vasquez, Paula A.

2013-10-01

The proclaimed Century of Biology is rapidly leading to the realization of how starkly different and more complex biological materials are than the materials that underpinned the industrial and technological revolution. These differences arise, in part, because biological matter exhibits both viscous and elastic behavior. Moreover, this behavior varies across the frequency, wavelength and amplitude spectrum of forcing. This broadclass of responsesin biological matter requires multiple frequency-dependent functions to specify material behavior, instead of a discrete set of parameters that relate to either viscosity or elasticity. This complexity prevails even if the biological matter is assumed to be spatially homogeneous, which is rarely true. However, very little progress has been made on the characterization of heterogeneity and how to build that information into constitutive laws and predictive models. In addition, most biological matter is non-stationary, which motivates the term "living". Biomaterials typically are in an active state in order to perform certain functions, and they often are modified or replenished on the basis of external stimuli. It has become popular in materials engineering to try to duplicate some of the functionality of biomaterials, e.g., a lot of effort has gone into the design of self-assembling, self-healing and shape shifting materials. These distinguishing features of biomaterials require significantly more degrees of freedom than traditional composites and many of the molecular species and their roles in functionality have yet to be determined. A typical biological material includes small molecule biochemical species that react and diffuse within larger species. These large molecular weightspecies provide the primary structural and biophysical properties of the material. The small molecule binding and unbinding kinetics serves to modulate material properties, and typical small molecule production and release are governed by external stimuli (e.g., stress). The bottom line is that the mathematical and numerical tools of 20th Century materials science are often insufficient for describing biological materials and for predicting their behavior both in vitro and in vivo.

Perspectives on the Use of Algae as Biological Indicators for Monitoring and Protecting Aquatic Environments, with Special Reference to Malaysian Freshwater Ecosystems

PubMed Central

Omar, Wan Maznah Wan

2010-01-01

Algal communities possess many attributes as biological indicators of spatial and temporal environmental changes. Algal parameters, especially the community structural and functional variables that have been used in biological monitoring programs, are highlighted in this document. Biological indicators like algae have only recently been included in water quality assessments in some areas of Malaysia. The use of algal parameters in identifying various types of water degradation is essential and complementary to other environmental indicators. PMID:24575199

Self-Organization in Coordination-Driven Self-Assembly

PubMed Central

Northrop, Brian H.; Zheng, Yao-Rong; Chi, Ki-Whan; Stang, Peter J.

2009-01-01

Conspectus Self-assembly allows for the preparation of highly complex molecular and supramolecular systems from relatively simple starting materials. Typically, self-assembled supramolecules are constructed by combining complementary pairs of two highly symmetric molecular components, thus limiting the chances of forming unwanted side products. Combining asymmetric molecular components or multiple complementary sets of molecules in one complex mixture can produce myriad different ordered and disordered supramolecular assemblies. Alternatively, spontaneous self-organization phenomena can promote the formation of specific product(s) out of a collection of multiple possibilities. Self-organization processes are common throughout much of nature and are especially common in biological systems. Recently, researchers have studied self-organized self-assembly in purely synthetic systems. This Account describes our investigations of self-organization in the coordination-driven self-assembly of platinum(II)-based metallosupramolecules. The modularity of the coordination-driven approach to self-assembly has allowed us to systematically study a wide variety of different factors that can control the extent of supramolecular self-organization. In particular, we have evaluated the effects of the symmetry and polarity of ambidentate donor subunits, differences in geometrical parameters (e.g. the size, angularity, and dimensionality) of Pt(II)-based acceptors and organic donors, the influence of temperature and solvent, and the effects of intermolecular steric interactions and hydrophobic interactions on self-organization. Our studies have shown that the extent of self-organization in the coordination-driven self-assembly of both 2D polygons and 3D polyhedra ranges from no organization (a statistical mixture of multiple products), to amplified organization (wherein a particular product or products are favored over others), and all the way to the absolute self-organization of discrete supramolecular assemblies. In many cases, inputs such as dipolar interactions, steric interactions, and differences in the geometric parameters of subunits—used either alone or as multiple factors simultaneously—can achieve absolute self-organization of discrete supramolecules. We have also observed instances where self-organization is not absolute and varies in its deviation from statistical results. Steric interactions are particularly useful control factors for driving such amplified self-organization because they can be subtly tuned through small structural variations. Having the ability to fully understand and control the self-organization of complex mixtures into specific synthetic supramolecules can provide a better understanding of analogous processes in biological systems. Furthermore, self-organization may allow for the facile synthesis of complex multifunctional, multicomponent systems from simply mixing a collection of much simpler, judiciously designed individual molecular components. PMID:19555073

Characterizing Uncertainty and Variability in PBPK Models ...

EPA Pesticide Factsheets

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability. The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological impro

Auto-FPFA: An Automated Microscope for Characterizing Genetically Encoded Biosensors.

PubMed

Nguyen, Tuan A; Puhl, Henry L; Pham, An K; Vogel, Steven S

2018-05-09

Genetically encoded biosensors function by linking structural change in a protein construct, typically tagged with one or more fluorescent proteins, to changes in a biological parameter of interest (such as calcium concentration, pH, phosphorylation-state, etc.). Typically, the structural change triggered by alterations in the bio-parameter is monitored as a change in either fluorescent intensity, or lifetime. Potentially, other photo-physical properties of fluorophores, such as fluorescence anisotropy, molecular brightness, concentration, and lateral and/or rotational diffusion could also be used. Furthermore, while it is likely that multiple photo-physical attributes of a biosensor might be altered as a function of the bio-parameter, standard measurements monitor only a single photo-physical trait. This limits how biosensors are designed, as well as the accuracy and interpretation of biosensor measurements. Here we describe the design and construction of an automated multimodal-microscope. This system can autonomously analyze 96 samples in a micro-titer dish and for each sample simultaneously measure intensity (photon count), fluorescence lifetime, time-resolved anisotropy, molecular brightness, lateral diffusion time, and concentration. We characterize the accuracy and precision of this instrument, and then demonstrate its utility by characterizing three types of genetically encoded calcium sensors as well as a negative control.

Quantitative tissue polarimetry using polar decomposition of 3 x 3 Mueller matrix

NASA Astrophysics Data System (ADS)

Swami, M. K.; Manhas, S.; Buddhiwant, P.; Ghosh, N.; Uppal, A.; Gupta, P. K.

2007-05-01

Polarization properties of any optical system are completely described by a sixteen-element (4 x 4) matrix called Mueller matrix, which transform the Stokes vector describing the polarization properties of incident light to the stokes vector of scattered light. Measurement of all the elements of the matrix requires a minimum of sixteen measurements involving both linear and circularly polarized light. However, for many diagnostic applications, it would be useful if all the polarization parameters of the medium (depolarization (Δ), differential attenuation of two orthogonal polarizations, that is, diattenuation (d), and differential phase retardance of two orthogonal polarizations, i.e., retardance (δ )) can be quantified with linear polarization measurements alone. In this paper we show that for a turbid medium, like biological tissue, where the depolarization of linearly polarized light arises primarily due to the randomization of the field vector's direction by multiple scattering, the polarization parameters of the medium can be obtained from the nine Mueller matrix elements involving linear polarization measurements only. Use of the approach for measurement of polarization parameters (Δ, d and δ) of normal and malignant (squamous cell carcinoma) tissues resected from human oral cavity are presented.

Assessing the drug-likeness of lamellarins, a marine-derived natural product class with diverse oncological activities.

PubMed

Chittchang, Montakarn; Gleeson, M Paul; Ploypradith, Poonsakdi; Ruchirawat, Somsak

2010-06-01

Natural products currently represent an underutilized source of leads for the pharmaceutical industry, especially when one considers that almost 50% of all drugs were either derived from such sources or are very closely related. Lamellarins are a class of natural products with diverse biological activities and have entered into preclinical development for the treatment of multidrug-resistant tumors. Although these compounds demonstrated good cell penetration, as observed by their low microM activity in whole cell models, they have not been extensively profiled from a physicochemical point of view, and this is the goal of this study. For this study, we have determined the experimental logP values of a set of 25 lamellarins, given it is the single most important parameter in determining multiple ADMET parameters. We also discuss the relationship between this natural product class, natural product derivatives in development and on the market, oral marketed drugs, as well as drug molecules in development, using a range of physicochemical parameters in conjunction with principal components analysis (PCA). The impact of this systematic analysis on our ongoing medicinal chemistry strategy is also discussed. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

Tunable Collagen I Hydrogels for Engineered Physiological Tissue Micro-Environments

PubMed Central

Antoine, Elizabeth E.; Vlachos, Pavlos P.; Rylander, Marissa N.

2015-01-01

Collagen I hydrogels are commonly used to mimic the extracellular matrix (ECM) for tissue engineering applications. However, the ability to design collagen I hydrogels similar to the properties of physiological tissues has been elusive. This is primarily due to the lack of quantitative correlations between multiple fabrication parameters and resulting material properties. This study aims to enable informed design and fabrication of collagen hydrogels in order to reliably and reproducibly mimic a variety of soft tissues. We developed empirical predictive models relating fabrication parameters with material and transport properties. These models were obtained through extensive experimental characterization of these properties, which include compression modulus, pore and fiber diameter, and diffusivity. Fabrication parameters were varied within biologically relevant ranges and included collagen concentration, polymerization pH, and polymerization temperature. The data obtained from this study elucidates previously unknown fabrication-property relationships, while the resulting equations facilitate informed a priori design of collagen hydrogels with prescribed properties. By enabling hydrogel fabrication by design, this study has the potential to greatly enhance the utility and relevance of collagen hydrogels in order to develop physiological tissue microenvironments for a wide range of tissue engineering applications. PMID:25822731

Do Houseflies Think? Patterns of Induction and Biological Beliefs in Development.

ERIC Educational Resources Information Center

Gutheil, Grant; Vera, Alonzo; Keil, Frank C.

1998-01-01

Examined preschoolers' inductive inferences across biological and non-biological kinds. Found support for gradual-enrichment model of conceptual change. Four-year-olds had a limited, coherent, independent biological theory which may form the basis of mature understanding of biological kinds. Explored results in terms of multiple explanatory…

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation

PubMed Central

2017-01-01

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability. PMID:29186132

Biological and nonbiological complex drugs for multiple sclerosis in Latin America: regulations and risk management.

PubMed

Carrá, Adriana; Macías Islas, Miguel Angel; Tarulla, Adriana; Bichuetti, Denis Bernardi; Finkelsztejn, Alessandro; Fragoso, Yara Dadalti; Árcega-Revilla, Raul; Cárcamo Rodríguez, Claudia; Durán, Juan Carlos; Bonitto, Juan García; León, Rosalba; Oehninger Gatti, Carlos; Orozco, Geraldine; Vizcarra Escobar, Darwin

2015-06-01

Biological drugs and nonbiological complex drugs with expired patents are followed by biosimilars and follow-on drugs that are supposedly similar and comparable with the reference product in terms of quality, safety and efficacy. Unlike simple molecules that can be copied and reproduced, biosimilars and follow-on complex drugs are heterogeneous and need specific regulations from health and pharmacovigilance agencies. A panel of 14 Latin American experts on multiple sclerosis from nine different countries met to discuss the recommendations regarding biosimilars and follow-on complex drugs for treating multiple sclerosis. Specific measures relating to manufacturing, therapeutic equivalence assessment and pharmacovigilance reports need to be implemented before commercialization. Physical, chemical, biological and immunogenic characterizations of the new product need to be available before clinical trials start. The new product must maintain the same immunogenicity as the original. Automatic substitution of biological and complex drugs poses unacceptable risks to the patient.

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

A global method for identifying dependences between helio-geophysical and biological series by filtering the precedents (outliers)

NASA Astrophysics Data System (ADS)

Ozheredov, V. A.; Breus, T. K.; Gurfinkel, Yu. I.; Matveeva, T. A.

2014-12-01

A new approach to finding the dependence between heliophysical and meteorological factors and physiological parameters is considered that is based on the preliminary filtering of precedents (outliers). The sought-after dependence is masked by extraneous influences which cannot be taken into account. Therefore, the typically calculated correlation between the external-influence ( x) and physiology ( y) parameters is extremely low and does not allow their interdependence to be conclusively proved. A robust method for removing the precedents (outliers) from the database is proposed that is based on the intelligent sorting of the polynomial curves of possible dependences y( x), followed by filtering out the precedents which are far away from y( x) and optimizing the coefficient of nonlinear correlation between the regular, i.e., remaining, precedents. This optimization problem is shown to be a search for a maximum in the absence of the concept of gradient and requires the use of a genetic algorithm based on the Gray code. The relationships between the various medical and biological parameters and characteristics of the space and terrestrial weather are obtained and verified using the cross-validation method. It is proven that, by filtering out no more than 20% of precedents, it is possible to obtain a nonlinear correlation coefficient of no less than 0.5. A juxtaposition of the proposed method for filtering precedents (outliers) and the least-square method (LSM) for determining the optimal polynomial using multiple independent tests (Monte Carlo method) of models, which are as close as possible to real dependences, has shown that the LSM determination loses much in comparison to the proposed method.

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation

PubMed Central

Zimmer, Christoph

2016-01-01

Background Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models. Methods The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique. Results The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models. PMID:27583802

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

Study of potential health effects of electromagnetic fields of telephony and Wi-Fi, using chicken embryo development as animal model.

PubMed

Woelders, Henri; de Wit, Agnes; Lourens, Alexander; Stockhofe, Norbert; Engel, Bas; Hulsegge, Ina; Schokker, Dirkjan; van Heijningen, Paula; Vossen, Stefan; Bekers, Dave; Zwamborn, Peter

2017-04-01

The objective of this study is to investigate possible biological effects of radiofrequency electromagnetic fields (RF-EMF) as used in modern wireless telecommunication in a well-controlled experimental environment using chicken embryo development as animal model. Chicken eggs were incubated under continuous experimental exposure to GSM (1.8 GHz), DECT (1.88 GHz), UMTS (2.1 GHz), and WLAN (5.6 GHz) radiation, with the appropriate modulation protocol, using a homogeneous field distribution at a field strength of approximately 3 V/m, representing the maximum field level in a normal living environment. Radiation-shielded exposure units/egg incubators were operating in parallel for exposed and control eggs in a climatized homogeneous environment, using 450 eggs per treatment in three successive rounds per treatment. Dosimetry of the exposure (field characteristics and specific absorption rate) were studied. Biological parameters studied included embryo death during incubation, hatching percentage, and various morphological and histological parameters of embryos and chicks and their organs, and gene expression profiles of embryos on day 7 and day 18 of incubation by microarray and qPCR. No conclusive evidence was found for induced embryonic mortality or malformations by exposure to the used EMFs, or for effects on the other measured parameters. Estimated differences between treatment groups were always small and the effect of treatment was not significant. In a statistical model that ignored possible interaction between rounds and exposure units, some of the many pairwise comparisons of exposed versus control had P-values lower than 0.05, but were not significant after correction for multiple testing. Bioelectromagnetics. 38:186-203, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour.

PubMed

Evans, Stephanie; Alden, Kieran; Cucurull-Sanchez, Lourdes; Larminie, Christopher; Coles, Mark C; Kullberg, Marika C; Timmis, Jon

2017-02-01

A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model's sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor RORγt, is sufficient to drive switching of Th17 cells towards an IFN-γ-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from http://www.york.ac.uk/ycil/software.

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. PMID:23593445

Universally Sloppy Parameter Sensitivities in Systems Biology Models

PubMed Central

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-01-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a “sloppy” spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters. PMID:17922568

Universally sloppy parameter sensitivities in systems biology models.

PubMed

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-10-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

The Multiple-Choice Model: Some Solutions for Estimation of Parameters in the Presence of Omitted Responses

ERIC Educational Resources Information Center

Abad, Francisco J.; Olea, Julio; Ponsoda, Vicente

2009-01-01

This article deals with some of the problems that have hindered the application of Samejima's and Thissen and Steinberg's multiple-choice models: (a) parameter estimation difficulties owing to the large number of parameters involved, (b) parameter identifiability problems in the Thissen and Steinberg model, and (c) their treatment of omitted…

[Effects of biologically active pectin-containing dietary supplement on gastroduodenal motility in patients with a functional dyspepsia].

PubMed

Loranskaia, T I; Kabanova, I N; Klykova, E V

2002-01-01

For 21 patients with a functional dyspepsia the influencing biologically active additives to nutrition "Pekcecom" on dynamics of clinical symptoms and parameters gastroduodenal motility under the data gastroduodenoscintigraphy was studied. The usage of biologically active additives during 4 weeks was accompanied by deboosting of accelerated gastric emptying for want of statistically significant influencing on a normal and delayed gastric emptying and parameters of duodenal transit.

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework

PubMed Central

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation. PMID:28225811

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework.

PubMed

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation.

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

PubMed Central

2012-01-01

Background An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge. PMID:22578440

Promoting Inquiry-Based Teaching in Laboratory Courses: Are We Meeting the Grade?

PubMed Central

Butler, Amy; Burke da Silva, Karen

2014-01-01

Over the past decade, repeated calls have been made to incorporate more active teaching and learning in undergraduate biology courses. The emphasis on inquiry-based teaching is especially important in laboratory courses, as these are the courses in which students are applying the process of science. To determine the current state of research on inquiry-based teaching in undergraduate biology laboratory courses, we reviewed the recent published literature on inquiry-based exercises. The majority of studies in our data set were in the subdisciplines of biochemistry, cell biology, developmental biology, genetics, and molecular biology. In addition, most exercises were guided inquiry, rather than open ended or research based. Almost 75% of the studies included assessment data, with two-thirds of these studies including multiple types of assessment data. However, few exercises were assessed in multiple courses or at multiple institutions. Furthermore, assessments were rarely based on published instruments. Although the results of the studies in our data set show a positive effect of inquiry-based teaching in biology laboratory courses on student learning gains, research that uses the same instrument across a range of courses and institutions is needed to determine whether these results can be generalized. PMID:25185228

Online model checking approach based parameter estimation to a neuronal fate decision simulation model in Caenorhabditis elegans with hybrid functional Petri net with extension.

PubMed

Li, Chen; Nagasaki, Masao; Koh, Chuan Hock; Miyano, Satoru

2011-05-01

Mathematical modeling and simulation studies are playing an increasingly important role in helping researchers elucidate how living organisms function in cells. In systems biology, researchers typically tune many parameters manually to achieve simulation results that are consistent with biological knowledge. This severely limits the size and complexity of simulation models built. In order to break this limitation, we propose a computational framework to automatically estimate kinetic parameters for a given network structure. We utilized an online (on-the-fly) model checking technique (which saves resources compared to the offline approach), with a quantitative modeling and simulation architecture named hybrid functional Petri net with extension (HFPNe). We demonstrate the applicability of this framework by the analysis of the underlying model for the neuronal cell fate decision model (ASE fate model) in Caenorhabditis elegans. First, we built a quantitative ASE fate model containing 3327 components emulating nine genetic conditions. Then, using our developed efficient online model checker, MIRACH 1.0, together with parameter estimation, we ran 20-million simulation runs, and were able to locate 57 parameter sets for 23 parameters in the model that are consistent with 45 biological rules extracted from published biological articles without much manual intervention. To evaluate the robustness of these 57 parameter sets, we run another 20 million simulation runs using different magnitudes of noise. Our simulation results concluded that among these models, one model is the most reasonable and robust simulation model owing to the high stability against these stochastic noises. Our simulation results provide interesting biological findings which could be used for future wet-lab experiments.

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

NASA Astrophysics Data System (ADS)

Hoshiba, Yasuhiro; Hirata, Takafumi; Shigemitsu, Masahito; Nakano, Hideyuki; Hashioka, Taketo; Masuda, Yoshio; Yamanaka, Yasuhiro

2018-06-01

Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a one-dimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

Chemical mixtures and environmental effects: a pilot study to assess ecological exposure and effects in streams

USGS Publications Warehouse

Buxton, Herbert T.; Reilly, Timothy J.; Kuivila, Kathryn; Kolpin, Dana W.; Bradley, Paul M.; Villeneuve, Daniel L.; Mills, Marc A.

2015-01-01

Assessment and management of the risks of exposure to complex chemical mixtures in streams are priorities for human and environmental health organizations around the world. The current lack of information on the composition and variability of environmental mixtures and a limited understanding of their combined effects are fundamental obstacles to timely identification and prevention of adverse human and ecological effects of exposure. This report describes the design of a field-based study of the composition and biological activity of chemical mixtures in U.S. stream waters affected by a wide range of human activities and contaminant sources. The study is a collaborative effort by the U.S. Geological Survey and the U.S. Environmental Protection Agency. Scientists sampled 38 streams spanning 24 States and Puerto Rico. Thirty-four of the sites were located in watersheds impacted by multiple contaminant sources, including industrial and municipal wastewater discharges, crop and animal agricultural runoff, urban runoff, and other point and nonpoint contaminant sources. The remaining four sites were minimally development reference watersheds. All samples underwent comprehensive chemical and biological characterization, including sensitive and specific direct analysis for over 700 dissolved organic and inorganic chemicals and field parameters, identification of unknown contaminants (environmental diagnostics), and a variety of bioassays to evaluate biological activity and toxicity.

Using circulating tumor cells to inform on prostate cancer biology and clinical utility

PubMed Central

Li, Jing; Gregory, Simon G.; Garcia-Blanco, Mariano A.; Armstrong, Andrew J.

2016-01-01

Substantial advances in the molecular biology of prostate cancer have led to the approval of multiple new systemic agents to treat men with metastatic castration-resistant prostate cancer (mCRPC). These treatments encompass androgen receptor directed therapies, immunotherapies, bone targeting radiopharmaceuticals and cytotoxic chemotherapies. There is, however, great heterogeneity in the degree of patient benefit with these agents, thus fueling the need to develop predictive biomarkers that are able to rationally guide therapy. Circulating tumor cells (CTCs) have the potential to provide an assessment of tumor-specific biomarkers through a non-invasive, repeatable “liquid biopsy” of a patient’s cancer at a given point in time. CTCs have been extensively studied in men with mCRPC, where CTC enumeration using the Cellsearch® method has been validated and FDA approved to be used in conjunction with other clinical parameters as a prognostic biomarker in metastatic prostate cancer. In addition to enumeration, more sophisticated molecular profiling of CTCs is now feasible and may provide more clinical utility as it may reflect tumor evolution within an individual particularly under the pressure of systemic therapies. Here, we review technologies used to detect and characterize CTCs, and the potential biological and clinical utility of CTC molecular profiling in men with metastatic prostate cancer. PMID:26079252

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

PubMed

Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; MacNamara, Aidan; Danciu, Diana-Patricia; Banga, Julio R; Saez-Rodriguez, Julio

2014-05-10

Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

PubMed Central

2014-01-01

Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957

Using multi-criteria analysis of simulation models to understand complex biological systems

Treesearch

Maureen C. Kennedy; E. David Ford

2011-01-01

Scientists frequently use computer-simulation models to help solve complex biological problems. Typically, such models are highly integrated, they produce multiple outputs, and standard methods of model analysis are ill suited for evaluating them. We show how multi-criteria optimization with Pareto optimality allows for model outputs to be compared to multiple system...

Practice Makes Pretty Good: Assessment of Primary Literature Reading Abilities across Multiple Large-Enrollment Biology Laboratory Courses

ERIC Educational Resources Information Center

Sato, Brian K.; Kadandale, Pavan; He, Wenliang; Murata, Paige M. N.; Latif, Yama; Warschauer, Mark

2014-01-01

Primary literature is essential for scientific communication and is commonly utilized in undergraduate biology education. Despite this, there is often little time spent "training" our students how to critically analyze a paper. To address this, we introduced a primary literature module in multiple upper-division laboratory courses. In…

Inspiring Integration in College Students Reading Multiple Biology Texts

ERIC Educational Resources Information Center

Firetto, Carla

2013-01-01

Introductory biology courses typically present topics on related biological systems across separate chapters and lectures. A complete foundational understanding requires that students understand how these biological systems are related. Unfortunately, spontaneous generation of these connections is rare for novice learners. These experiments focus…

Design and utilisation of protocols to characterise dynamic PET uptake of two tracers using basis pursuit.

PubMed

Bell, Christopher; Puttick, Simon; Rose, Stephen; Smith, Jye; Thomas, Paul; Dowson, Nicholas

2017-06-21

Imaging using more than one biological process using PET could be of great utility, but despite previously proposed approaches to dual-tracer imaging, it is seldom performed. The alternative of performing multiple scans is often infeasible for clinical practice or even in research studies. Dual-tracer PET scanning allows for multiple PET radiotracers to be imaged within the same imaging session. In this paper we describe our approach to utilise the basis pursuit method to aid in the design of dual-tracer PET imaging experiments, and later in separation of the signals. The advantage of this approach is that it does not require a compartment model architecture to be specified or even that both signals are distinguishable in all cases. This means the method for separating dual-tracer signals can be used for many feasible and useful combinations of biology or radiotracer, once an appropriate scanning protocol has been decided upon. Following a demonstration in separating the signals from two consecutively injected radionuclides in a controlled experiment, phantom and list-mode mouse experiments demonstrated the ability to test the feasibility of dual-tracer imaging protocols for multiple injection delays. Increases in variances predicted for kinetic macro-parameters V D and K I in brain and tumoral tissue were obtained when separating the synthetically combined data. These experiments confirmed previous work using other approaches that injections delays of 10-20 min ensured increases in variance were kept minimal for the test tracers used. On this basis, an actual dual-tracer experiment using a 20 min delay was performed using these radio tracers, with the kinetic parameters (V D and K I ) extracted for each tracer in agreement with the literature. This study supports previous work that dual-tracer PET imaging can be accomplished provided certain constraints are adhered to. The utilisation of basis pursuit techniques, with its removed need to specify a model architecture, allows the feasibility of a range of imaging protocols to be investigated via simulation in a straight-forward manner for a wide range of possible scenarios. The hope is that the ease of utilising this approach during feasibility studies and in practice removes any perceived technical barrier to performing dual-tracer imaging.

Design and utilisation of protocols to characterise dynamic PET uptake of two tracers using basis pursuit

NASA Astrophysics Data System (ADS)

Bell, Christopher; Puttick, Simon; Rose, Stephen; Smith, Jye; Thomas, Paul; Dowson, Nicholas

2017-06-01

Imaging using more than one biological process using PET could be of great utility, but despite previously proposed approaches to dual-tracer imaging, it is seldom performed. The alternative of performing multiple scans is often infeasible for clinical practice or even in research studies. Dual-tracer PET scanning allows for multiple PET radiotracers to be imaged within the same imaging session. In this paper we describe our approach to utilise the basis pursuit method to aid in the design of dual-tracer PET imaging experiments, and later in separation of the signals. The advantage of this approach is that it does not require a compartment model architecture to be specified or even that both signals are distinguishable in all cases. This means the method for separating dual-tracer signals can be used for many feasible and useful combinations of biology or radiotracer, once an appropriate scanning protocol has been decided upon. Following a demonstration in separating the signals from two consecutively injected radionuclides in a controlled experiment, phantom and list-mode mouse experiments demonstrated the ability to test the feasibility of dual-tracer imaging protocols for multiple injection delays. Increases in variances predicted for kinetic macro-parameters V D and K I in brain and tumoral tissue were obtained when separating the synthetically combined data. These experiments confirmed previous work using other approaches that injections delays of 10-20 min ensured increases in variance were kept minimal for the test tracers used. On this basis, an actual dual-tracer experiment using a 20 min delay was performed using these radio tracers, with the kinetic parameters (V D and K I) extracted for each tracer in agreement with the literature. This study supports previous work that dual-tracer PET imaging can be accomplished provided certain constraints are adhered to. The utilisation of basis pursuit techniques, with its removed need to specify a model architecture, allows the feasibility of a range of imaging protocols to be investigated via simulation in a straight-forward manner for a wide range of possible scenarios. The hope is that the ease of utilising this approach during feasibility studies and in practice removes any perceived technical barrier to performing dual-tracer imaging.

The application of multiple biophysical cues to engineer functional neocartilage for treatment of osteoarthritis. Part I: cellular response.

PubMed

Brady, Mariea A; Waldman, Stephen D; Ethier, C Ross

2015-02-01

Osteoarthritis (OA) is a complex disease of the joint for which current treatments are unsatisfactory, thus motivating development of tissue engineering (TE)-based therapies. To date, TE strategies have had some success, developing replacement tissue constructs with biochemical properties approaching that of native cartilage. However, poor biomechanical properties and limited postimplantation integration with surrounding tissue are major shortcomings that need to be addressed. Functional tissue engineering strategies that apply physiologically relevant biophysical cues provide a platform to improve TE constructs before implantation. In the previous decade, new experimental and theoretical findings in cartilage biomechanics and electromechanics have emerged, resulting in an increased understanding of the complex interplay of multiple biophysical cues in the extracellular matrix of the tissue. The effect of biophysical stimulation on cartilage, and the resulting chondrocyte-mediated biosynthesis, remodeling, degradation, and repair, has, therefore, been extensively explored by the TE community. This article compares and contrasts the cellular response of chondrocytes to multiple biophysical stimuli, and may be read in conjunction with its companion paper that compares and contrasts the subsequent intracellular signal transduction cascades. Mechanical, magnetic, and electrical stimuli promote proliferation, differentiation, and maturation of chondrocytes within established dose parameters or "biological windows." This knowledge will provide a framework for ongoing studies incorporating multiple biophysical cues in TE functional neocartilage for treatment of OA.

[Study on differences between pharmacokinetics and chromatopharmacodynamics for Chinese materia medica formulae].

PubMed

He, Fuyuan; Deng, Kaiwen; Zou, Huan; Qiu, Yun; Chen, Feng; Zhou, Honghao

2011-01-01

To study on the differences between chromatopharmacokinetics (pharmacokinetics with fingerprint chromatography) and chromatopharmacodynamics (pharmacodynamics with fingerprint chromatography) of Chinese materia medica formulae to answer the question whether the pharmacokinetic parameters of multiple composites can be utilized to guide the medication of multiple composites. On the base of established four chromatopharmacology (pharmacology with chromatographic fingerprint), the pharmacokinetics, and pharmacodynamics were analyzed comparably on their mathematical model and parameter definition. On the basis of quantitative pharmacology, the function expressions and total statistical parameters, such as total zero moment, total first moment, total second moment of the pharmacokinetics, and pharmacodynamics were analyzed to the common expressions and elucidated results for single and multiple components in Chinese materia medica formulae. Total quantitative pharmacokinetic, i.e., chromatopharmacokinetic parameter were decided by each component pharmacokinetic parameters, whereas the total quantitative pharmacodynamic, i.e., chromatopharmacodynamic parameter were decided by both of pharmacokinetic and pharmacodynamic parameters of each components. The pharmacokinetic parameters were corresponded to pharmacodynamic parameters with an existing stable effective coefficient when the constitutive ratio of each composite was a constant. The effects of Chinese materia medica were all controlled by pharmacokinetic and pharmacodynamic coefficient. It is a special case that the pharmacokinetic parameter could independently guide the clinical medication for single component whereas the chromatopharmacokinetic parameters are not applied to the multiple drug combination system, and not be used to solve problems of chromatopharmacokinetic of Chinese materia medica formulae.

Nonadiabatic dynamics of electron transfer in solution: Explicit and implicit solvent treatments that include multiple relaxation time scales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwerdtfeger, Christine A.; Soudackov, Alexander V.; Hammes-Schiffer, Sharon, E-mail: shs3@illinois.edu

2014-01-21

The development of efficient theoretical methods for describing electron transfer (ET) reactions in condensed phases is important for a variety of chemical and biological applications. Previously, dynamical dielectric continuum theory was used to derive Langevin equations for a single collective solvent coordinate describing ET in a polar solvent. In this theory, the parameters are directly related to the physical properties of the system and can be determined from experimental data or explicit molecular dynamics simulations. Herein, we combine these Langevin equations with surface hopping nonadiabatic dynamics methods to calculate the rate constants for thermal ET reactions in polar solvents formore » a wide range of electronic couplings and reaction free energies. Comparison of explicit and implicit solvent calculations illustrates that the mapping from explicit to implicit solvent models is valid even for solvents exhibiting complex relaxation behavior with multiple relaxation time scales and a short-time inertial response. The rate constants calculated for implicit solvent models with a single solvent relaxation time scale corresponding to water, acetonitrile, and methanol agree well with analytical theories in the Golden rule and solvent-controlled regimes, as well as in the intermediate regime. The implicit solvent models with two relaxation time scales are in qualitative agreement with the analytical theories but quantitatively overestimate the rate constants compared to these theories. Analysis of these simulations elucidates the importance of multiple relaxation time scales and the inertial component of the solvent response, as well as potential shortcomings of the analytical theories based on single time scale solvent relaxation models. This implicit solvent approach will enable the simulation of a wide range of ET reactions via the stochastic dynamics of a single collective solvent coordinate with parameters that are relevant to experimentally accessible systems.« less

"SABER": A new software tool for radiotherapy treatment plan evaluation.

PubMed

Zhao, Bo; Joiner, Michael C; Orton, Colin G; Burmeister, Jay

2010-11-01

Both spatial and biological information are necessary in order to perform true optimization of a treatment plan and for predicting clinical outcome. The goal of this work is to develop an enhanced treatment plan evaluation tool which incorporates biological parameters and retains spatial dose information. A software system is developed which provides biological plan evaluation with a novel combination of features. It incorporates hyper-radiosensitivity using the induced-repair model and applies the new concept of dose convolution filter (DCF) to simulate dose wash-out effects due to cell migration, bystander effect, and/or tissue motion during treatment. Further, the concept of spatial DVH (sDVH) is introduced to evaluate and potentially optimize the spatial dose distribution in the target volume. Finally, generalized equivalent uniform dose is derived from both the physical dose distribution (gEUD) and the distribution of equivalent dose in 2 Gy fractions (gEUD2) and the software provides three separate models for calculation of tumor control probability (TCP), normal tissue complication probability (NTCP), and probability of uncomplicated tumor control (P+). TCP, NTCP, and P+ are provided as a function of prescribed dose and multivariable TCP, NTCP, and P+ plots are provided to illustrate the dependence on individual parameters used to calculate these quantities. Ten plans from two clinical treatment sites are selected to test the three calculation models provided by this software. By retaining both spatial and biological information about the dose distribution, the software is able to distinguish features of radiotherapy treatment plans not discernible using commercial systems. Plans that have similar DVHs may have different spatial and biological characteristics and the application of novel tools such as sDVH and DCF within the software may substantially change the apparent plan quality or predicted plan metrics such as TCP and NTCP. For the cases examined, both the calculation method and the application of DCF can change the ranking order of competing plans. The voxel-by-voxel TCP model makes it feasible to incorporate spatial variations of clonogen densities (n), radiosensitivities (SF2), and fractionation sensitivities (alpha/beta) as those data become available. The new software incorporates both spatial and biological information into the treatment planning process. The application of multiple methods for the incorporation of biological and spatial information has demonstrated that the order of application of biological models can change the order of plan ranking. Thus, the results of plan evaluation and optimization are dependent not only on the models used but also on the order in which they are applied. This software can help the planner choose more biologically optimal treatment plans and potentially predict treatment outcome more accurately.

SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment

PubMed Central

Coan, Heather B.; Youker, Robert T.

2017-01-01

Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are commonly used because they provide a standard, predefined way of modeling substitutions. However, researchers often do not know if a given matrix family or any individual matrix within a family is the most suitable. Furthermore, predefined matrix families may inaccurately reflect a particular hypothesis that a researcher wishes to model or otherwise result in unsatisfactory alignments. In these cases, the ability to compare the effects of one or more custom matrices may be needed. This laborious process is often performed manually because the ability to simultaneously load multiple matrices and then compare their effects on alignments is not readily available in current software tools. This paper presents SubVis, an interactive R package for loading and applying multiple substitution matrices to pairwise alignments. Users can simultaneously explore alignments resulting from multiple predefined and custom substitution matrices. SubVis utilizes several of the alignment functions found in R, a common language among protein scientists. Functions are tied together with the Shiny platform which allows the modification of input parameters. Information regarding alignment quality and individual amino acid substitutions is displayed with the JavaScript language which provides interactive visualizations for revealing both high-level and low-level alignment information. PMID:28674656

Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis.

PubMed

Yi, Ming; Mudunuri, Uma; Che, Anney; Stephens, Robert M

2009-06-29

One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php.

Do Sophisticated Epistemic Beliefs Predict Meaningful Learning? Findings from a Structural Equation Model of Undergraduate Biology Learning

ERIC Educational Resources Information Center

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-01-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely "multiple-source," "uncertainty," "development," and "justification." COLB is further…

Genetics in endocrinology: genetic variation in deiodinases: a systematic review of potential clinical effects in humans.

PubMed

Verloop, Herman; Dekkers, Olaf M; Peeters, Robin P; Schoones, Jan W; Smit, Johannes W A

2014-09-01

Iodothyronine deiodinases represent a family of selenoproteins involved in peripheral and local homeostasis of thyroid hormone action. Deiodinases are expressed in multiple organs and thyroid hormone affects numerous biological systems, thus genetic variation in deiodinases may affect multiple clinical endpoints. Interest in clinical effects of genetic variation in deiodinases has clearly increased. We aimed to provide an overview for the role of deiodinase polymorphisms in human physiology and morbidity. In this systematic review, studies evaluating the relationship between deiodinase polymorphisms and clinical parameters in humans were eligible. No restrictions on publication date were imposed. The following databases were searched up to August 2013: Pubmed, EMBASE (OVID-version), Web of Science, COCHRANE Library, CINAHL (EbscoHOST-version), Academic Search Premier (EbscoHOST-version), and ScienceDirect. Deiodinase physiology at molecular and tissue level is described, and finally the role of these polymorphisms in pathophysiological conditions is reviewed. Deiodinase type 1 (D1) polymorphisms particularly show moderate-to-strong relationships with thyroid hormone parameters, IGF1 production, and risk for depression. D2 variants correlate with thyroid hormone levels, insulin resistance, bipolar mood disorder, psychological well-being, mental retardation, hypertension, and risk for osteoarthritis. D3 polymorphisms showed no relationship with inter-individual variation in serum thyroid hormone parameters. One D3 polymorphism was associated with risk for osteoarthritis. Genetic deiodinase profiles only explain a small proportion of inter-individual variations in serum thyroid hormone levels. Evidence suggests a role of genetic deiodinase variants in certain pathophysiological conditions. The value for determination of deiodinase polymorphism in clinical practice needs further investigation. © 2014 European Society of Endocrinology.

Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2016-12-01

Surrogate construction has become a routine procedure when facing computationally intensive studies requiring multiple evaluations of complex models. In particular, surrogate models, otherwise called emulators or response surfaces, replace complex models in uncertainty quantification (UQ) studies, including uncertainty propagation (forward UQ) and parameter estimation (inverse UQ). Further, surrogates based on Polynomial Chaos (PC) expansions are especially convenient for forward UQ and global sensitivity analysis, also known as variance-based decomposition. However, the PC surrogate construction strongly suffers from the curse of dimensionality. With a large number of input parameters, the number of model simulations required for accurate surrogate construction is prohibitively large. Relatedly, non-adaptive PC expansions typically include infeasibly large number of basis terms far exceeding the number of available model evaluations. We develop Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth and PC surrogate construction leading to a sparse, high-dimensional PC surrogate with a very few model evaluations. The surrogate is then readily employed for global sensitivity analysis leading to further dimensionality reduction. Besides numerical tests, we demonstrate the construction on the example of Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Visualizing the 3D Architecture of Multiple Erythrocytes Infected with Plasmodium at Nanoscale by Focused Ion Beam-Scanning Electron Microscopy

PubMed Central

Soares Medeiros, Lia Carolina; De Souza, Wanderley; Jiao, Chengge; Barrabin, Hector; Miranda, Kildare

2012-01-01

Different methods for three-dimensional visualization of biological structures have been developed and extensively applied by different research groups. In the field of electron microscopy, a new technique that has emerged is the use of a focused ion beam and scanning electron microscopy for 3D reconstruction at nanoscale resolution. The higher extent of volume that can be reconstructed with this instrument represent one of the main benefits of this technique, which can provide statistically relevant 3D morphometrical data. As the life cycle of Plasmodium species is a process that involves several structurally complex developmental stages that are responsible for a series of modifications in the erythrocyte surface and cytoplasm, a high number of features within the parasites and the host cells has to be sampled for the correct interpretation of their 3D organization. Here, we used FIB-SEM to visualize the 3D architecture of multiple erythrocytes infected with Plasmodium chabaudi and analyzed their morphometrical parameters in a 3D space. We analyzed and quantified alterations on the host cells, such as the variety of shapes and sizes of their membrane profiles and parasite internal structures such as a polymorphic organization of hemoglobin-filled tubules. The results show the complex 3D organization of Plasmodium and infected erythrocyte, and demonstrate the contribution of FIB-SEM for the obtainment of statistical data for an accurate interpretation of complex biological structures. PMID:22432024

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-09-10

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Oxidative DNA damage background estimated by a system model of base excision repair

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sokhansanj, B A; Wilson, III, D M

Human DNA can be damaged by natural metabolism through free radical production. It has been suggested that the equilibrium between innate damage and cellular DNA repair results in an oxidative DNA damage background that potentially contributes to disease and aging. Efforts to quantitatively characterize the human oxidative DNA damage background level based on measuring 8-oxoguanine lesions as a biomarker have led to estimates varying over 3-4 orders of magnitude, depending on the method of measurement. We applied a previously developed and validated quantitative pathway model of human DNA base excision repair, integrating experimentally determined endogenous damage rates and model parametersmore » from multiple sources. Our estimates of at most 100 8-oxoguanine lesions per cell are consistent with the low end of data from biochemical and cell biology experiments, a result robust to model limitations and parameter variation. Our results show the power of quantitative system modeling to interpret composite experimental data and make biologically and physiologically relevant predictions for complex human DNA repair pathway mechanisms and capacity.« less

Effects of multiple levels of social organization on survival and abundance.

PubMed

Ward, Eric J; Semmens, Brice X; Holmes, Elizabeth E; Balcomb Iii, Ken C

2011-04-01

Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group-level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age-specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities. Conservation Biology ©2010 Society for Conservation Biology. No claim to original US government works.

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.

PubMed

Erguler, Kamil; Stumpf, Michael P H

2011-05-01

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.

Poroelastic Parameters of Peru Margin Sediments: Implications for Flow and Transport at Multiple Scales in the Marine Biosphere

NASA Astrophysics Data System (ADS)

Gettemy, G. L.; Cikoski, C.; Tobin, H. J.

2004-12-01

As part of a broader investigation of the deep marine subsurface environment, the first biosphere-focused drilling expedition, Leg 201, of the Ocean Drilling Program (ODP) occupied five unique sites in the Peru Margin (in a 1200 km2 region centered at 10 S, 80E). These sites represent the entire range of shallow biogeological conditions associated with this convergent margin:deep-water, mixed clay-pelagic sediments ocean-ward of the trench; slope-apron and prism toe sediments at the deformation front; and several distinct lithostratigraphic sequences on the continental shelf. Microbial enumeration and pore-water geochemistry results show that each particular site is both consistent and unique--consistent in terms of general biotic quantity and activity as predicted by energy flux and redox potential given the depositional environment and sedimentary record, but unique at key biogeological boundaries such as lithologic and/or physical property interfaces. This research addresses questions related to our understanding of how and why these boundaries form by looking at poroelastic and hydrologic parameters measured at multiple scales, from sub-millimeter to several centimeters. The issue of measurement scale, especially in regard to permeability and diffusivity characterization, is vital to interpreting observations of biologically-mediated diagenetic fronts (e.g., dolomitic lenses, depth- or time-varying barite fronts). These parameters are derived from (i) hydrologic and wave propagation experiments, (ii) SEM images, and (iii) shipboard split-core measurements, and structured in a modified Biot poroelasticity framework. This approach also allows quantification of the local heterogeneity of these parameters at the scale applicable to (and controlled by) microbial life; these results can then be used to formulate predictive models of the impact of biogeochemical processes. Ultimately, these models could then be used in interpretation of new remote-sensed data (e.g., from borehole tools, high-frequency backscatter devices), a fundamental challenge for all types of biospheric imaging everywhere.

Incorporation of multiple cloud layers for ultraviolet radiation modeling studies

NASA Technical Reports Server (NTRS)

Charache, Darryl H.; Abreu, Vincent J.; Kuhn, William R.; Skinner, Wilbert R.

1994-01-01

Cloud data sets compiled from surface observations were used to develop an algorithm for incorporating multiple cloud layers into a multiple-scattering radiative transfer model. Aerosol extinction and ozone data sets were also incorporated to estimate the seasonally averaged ultraviolet (UV) flux reaching the surface of the Earth in the Detroit, Michigan, region for the years 1979-1991, corresponding to Total Ozone Mapping Spectrometer (TOMS) version 6 ozone observations. The calculated UV spectrum was convolved with an erythema action spectrum to estimate the effective biological exposure for erythema. Calculations show that decreasing the total column density of ozone by 1% leads to an increase in erythemal exposure by approximately 1.1-1.3%, in good agreement with previous studies. A comparison of the UV radiation budget at the surface between a single cloud layer method and a multiple cloud layer method presented here is discussed, along with limitations of each technique. With improved parameterization of cloud properties, and as knowledge of biological effects of UV exposure increase, inclusion of multiple cloud layers may be important in accurately determining the biologically effective UV budget at the surface of the Earth.

Bayesian approach to MSD-based analysis of particle motion in live cells.

PubMed

Monnier, Nilah; Guo, Syuan-Ming; Mori, Masashi; He, Jun; Lénárt, Péter; Bathe, Mark

2012-08-08

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Differentiating low-molecular-weight heparins based on chemical, biological, and pharmacologic properties: implications for the development of generic versions of low-molecular-weight heparins.

PubMed

Jeske, Walter P; Walenga, Jeanine M; Hoppensteadt, Debra A; Vandenberg, Curtis; Brubaker, Aleah; Adiguzel, Cafer; Bakhos, Mamdouh; Fareed, Jawed

2008-02-01

Low-molecular-weight heparins (LMWHs) are polypharmacologic drugs used to treat thrombotic and cardiovascular disorders. These drugs are manufactured using different chemical and enzymatic methods, resulting in products with distinct chemical and pharmacologic profiles. Generic LMWHs have been introduced in Asia and South America, and several generic suppliers are seeking regulatory approval in the United States and the European Union. For simple small-molecule drugs, generic drugs have the same chemical structure, potency, and bioavailability as the innovator drug. Applying this definition to complex biological products such as the LMWHs has proved difficult. One major issue is defining appropriate criteria to demonstrate bioequivalence; pharmacopoeial specifications alone appear to be inadequate. Whereas available generic versions of LMWHs exhibit similar molecular and pharmacopoeial profiles, marked differences in their biological and pharmacologic behavior have been noted. Preliminary studies have demonstrated differences in terms of anti-Xa activity and tissue factor pathway inhibitor release after subcutaneous administration, as well as antiplatelet and profibrinolytic effects. The current data emphasize the need to consider multiple functional parameters when defining bioequivalence of biologic drugs with complex structures and activities and also underscore the importance of further pharmacologic studies involving animal models and human clinical trials. The U.S. Food and Drug Administration and the European Medicine Evaluation Agency are currently developing guidelines for the acceptance of biosimilar agents including LMWHs. Until such guidelines are complete, generic interchange may not be feasible.

Mini-review of the geotechnical parameters of municipal solid waste: Mechanical and biological pre-treated versus raw untreated waste.

PubMed

Petrovic, Igor

2016-09-01

The most viable option for biostabilisation of old sanitary landfills, filled with raw municipal solid waste, is the so-called bioreactor landfill. Even today, bioreactor landfills are viable options in many economically developing countries. However, in order to reduce the biodegradable component of landfilled waste, mechanical and biological treatment has become a widely accepted waste treatment technology, especially in more prosperous countries. Given that mechanical and biological treatment alters the geotechnical properties of raw waste material, the design of sanitary landfills which accepts mechanically and biologically treated waste, should be carried out with a distinct set of geotechnical parameters. However, under the assumption that 'waste is waste', some design engineers might be tempted to use geotechnical parameters of untreated raw municipal solid waste and mechanical and biological pre-treated municipal solid waste interchangeably. Therefore, to provide guidelines for use and to provide an aggregated source of this information, this mini-review provides comparisons of geotechnical parameters of mechanical and biological pre-treated waste and raw untreated waste at various decomposition stages. This comparison reveals reasonable correlations between the hydraulic conductivity values of untreated and mechanical and biological pre-treated municipal solid waste. It is recognised that particle size might have a significant influence on the hydraulic conductivity of both municipal solid waste types. However, the compression ratios and shear strengths of untreated and pre-treated municipal solid waste do not show such strong correlations. Furthermore, another emerging topic that requires appropriate attention is the recovery of resources that are embedded in old landfills. Therefore, the presented results provide a valuable tool for engineers designing landfills for mechanical and biological pre-treated waste or bioreactor landfills for untreated raw waste as well as planning landfill mining projects. © The Author(s) 2016.

Upper Secondary Students' Understanding of the Use of Multiple Models in Biology Textbooks--The Importance of Conceptual Variation and Incommensurability

ERIC Educational Resources Information Center

Gericke, Niklas; Hagberg, Mariana; Jorde, Doris

2013-01-01

In this study we investigate students' ability to discern conceptual variation and the use of multiple models in genetics when reading content-specific excerpts from biology textbooks. Using the history and philosophy of science as our reference, we were able to develop a research instrument allowing students themselves to investigate the…

Network-based drug discovery by integrating systems biology and computational technologies

PubMed Central

Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

2013-01-01

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

Study of Carrying Capacity Assesment for Natural Fisheries in Jatibarang Reservoir In Semarang City

NASA Astrophysics Data System (ADS)

Sujono, Bambang; Anggoro, Sutrisno

2018-02-01

Jatibarang reservoir serves as water supply in dry season and controlling flood in Semarang City. This reservoir is stem Kreo River which cathment areas of 54 km2, pool of area 110 ha and volume is 20 billion m3. This reservoir is potential to develop as natural fisheries area. The goals of this research were to explore existing condition of physical, biological as well as chemical parameter; carrying capacity assessment for natural fisheries; determining appropriate fish species to be developed in Jatibarang reservoir. This research was done in descriptive explorative scheme. Field survey and laboratory analyses were conducted to identify physical, chemical and biological parameters of the water. Physical parameters measured were temperature and water brightness. Chemical parameters measured were pH, DO, phosphate, Ammonia, nitrites and nitrate, while biological parameter measured were chlorophyll-a concentration. Carrying capacity analyses was done referred to the Government Regulation Number 82, 2001 that regulate the management of water quality and water pollution control. Based on the research, it showed that the existing condition of physical, chemical and biological parameters were still good to be used for natural fisheries. Based on TSI index, it classified as eutrofic water. Furthermore, tilapia fish (Oreochromis mossambicus), nile tilapia (Oreochromis niloticus) tawes (Barbonymus gonionotus) and carper fish (Cyprinus carpio) were considered as best species for natural fisheries in Jatibarang Reservoir.

Validity of simple clinical and biological parameters as screening tool for sickle cell anemia for referral to tertiary center in highly resource constraints.

PubMed

Kadima, Bertin Tshimanga; Gini-Ehungu, Jean Lambert; Mbutiwi, Fiston Ikwa Ndol; Bahati, John Tunda; Aloni, Michel Ntetani

2017-11-01

In the Democratic Republic of Congo, the incidence of sickle cell anemia (SCA) is estimated around 40 000 neonates per year. However, it is notoriously difficult to perform conventional electrophoresis in all hospitals and laboratories, especially at peripheral levels and rural area. A panel of multiple clinical and laboratory features that would enhance sickle cell disease were assessed for the detection of the disease in highly resource-scarce settings. A prospective study was conducted in Kinshasa. Venous blood samples were drawn from each study participant in order to determine the hematologic parameters, the peripheral smears, and the hemoglobin electrophoresis. We used Cohen's κ statistic to examine the agreement of each variable and diagnosis of sickle cell disease. A total of 807 patients were screened for sickle cell disease. Among these 807 children, 36 (4.5%) were homozygous for Hb S disease. The presence of at least 8% erythroblasts (PPV: 91%, NPV: 99%, sensitivity: 83.3%, specificity: 99.6%, κ value: .86) and sickle cells (PPV:100%, NPV: 98%, sensitivity: 50%, specificity: 100%, κ value: .66) in the peripheral blood smear had an acceptable agreement for sickle cell disease. These two biological markers may guide the clinician in the decision-making to initiate the management of the children as a sickle cell patient, pending confirmation of the disease by electrophoresis techniques. © 2017 Wiley Periodicals, Inc.

MINE: Module Identification in Networks

PubMed Central

2011-01-01

Background Graphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks. Results MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties. Conclusions MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans. PMID:21605434

Multiscale digital Arabidopsis predicts individual organ and whole-organism growth.

PubMed

Chew, Yin Hoon; Wenden, Bénédicte; Flis, Anna; Mengin, Virginie; Taylor, Jasper; Davey, Christopher L; Tindal, Christopher; Thomas, Howard; Ougham, Helen J; de Reffye, Philippe; Stitt, Mark; Williams, Mathew; Muetzelfeldt, Robert; Halliday, Karen J; Millar, Andrew J

2014-09-30

Understanding how dynamic molecular networks affect whole-organism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field.

Functional mapping of quantitative trait loci associated with rice tillering.

PubMed

Liu, G F; Li, M; Wen, J; Du, Y; Zhang, Y-M

2010-10-01

Several biologically significant parameters that are related to rice tillering are closely associated with rice grain yield. Although identification of the genes that control rice tillering and therefore influence crop yield would be valuable for rice production management and genetic improvement, these genes remain largely unidentified. In this study, we carried out functional mapping of quantitative trait loci (QTLs) for rice tillering in 129 doubled haploid lines, which were derived from a cross between IR64 and Azucena. We measured the average number of tillers in each plot at seven developmental stages and fit the growth trajectory of rice tillering with the Wang-Lan-Ding mathematical model. Four biologically meaningful parameters in this model--the potential maximum for tiller number (K), the optimum tiller time (t(0)), and the increased rate (r), or the reduced rate (c) at the time of deviation from t(0)--were our defined variables for multi-marker joint analysis under the framework of penalized maximum likelihood, as well as composite interval mapping. We detected a total of 27 QTLs that accounted for 2.49-8.54% of the total phenotypic variance. Nine common QTLs across multi-marker joint analysis and composite interval mapping showed high stability, while one QTL was environment-specific and three were epistatic. We also identified several genomic segments that are associated with multiple traits. Our results describe the genetic basis of rice tiller development, enable further marker-assisted selection in rice cultivar development, and provide useful information for rice production management.

Impact of processing parameters on the haemocompatibility of Bombyx mori silk films.

PubMed

Seib, F Philipp; Maitz, Manfred F; Hu, Xiao; Werner, Carsten; Kaplan, David L

2012-02-01

Silk has traditionally been used for surgical sutures due to its lasting strength and durability; however, the use of purified silk proteins as a scaffold material for vascular tissue engineering goes beyond traditional use and requires application-orientated biocompatibility testing. For this study, a library of Bombyx mori silk films was generated and exposed to various solvents and treatment conditions to reflect current silk processing techniques. The films, along with clinically relevant reference materials, were exposed to human whole blood to determine silk blood compatibility. All substrates showed an initial inflammatory response comparable to polylactide-co-glycolide (PLGA), and a low to moderate haemostasis response similar to polytetrafluoroethylene (PTFE) substrates. In particular, samples that were water annealed at 25 °C for 6 h demonstrated the best blood compatibility based on haemostasis parameters (e.g. platelet decay, thrombin-antithrombin complex, platelet factor 4, granulocytes-platelet conjugates) and inflammatory parameters (e.g. C3b, C5a, CD11b, surface-associated leukocytes). Multiple factors such as treatment temperature and solvent influenced the biological response, though no single physical parameter such as β-sheet content, isoelectric point or contact angle accurately predicted blood compatibility. These findings, when combined with prior in vivo data on silk, support a viable future for silk-based vascular grafts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Homogenization Theory for the Prediction of Obstructed Solute Diffusivity in Macromolecular Solutions

PubMed Central

Donovan, Preston; Chehreghanianzabi, Yasaman; Rathinam, Muruhan; Zustiak, Silviya Petrova

2016-01-01

The study of diffusion in macromolecular solutions is important in many biomedical applications such as separations, drug delivery, and cell encapsulation, and key for many biological processes such as protein assembly and interstitial transport. Not surprisingly, multiple models for the a-priori prediction of diffusion in macromolecular environments have been proposed. However, most models include parameters that are not readily measurable, are specific to the polymer-solute-solvent system, or are fitted and do not have a physical meaning. Here, for the first time, we develop a homogenization theory framework for the prediction of effective solute diffusivity in macromolecular environments based on physical parameters that are easily measurable and not specific to the macromolecule-solute-solvent system. Homogenization theory is useful for situations where knowledge of fine-scale parameters is used to predict bulk system behavior. As a first approximation, we focus on a model where the solute is subjected to obstructed diffusion via stationary spherical obstacles. We find that the homogenization theory results agree well with computationally more expensive Monte Carlo simulations. Moreover, the homogenization theory agrees with effective diffusivities of a solute in dilute and semi-dilute polymer solutions measured using fluorescence correlation spectroscopy. Lastly, we provide a mathematical formula for the effective diffusivity in terms of a non-dimensional and easily measurable geometric system parameter. PMID:26731550

Apparatus and Methods for Manipulation and Optimization of Biological Systems

NASA Technical Reports Server (NTRS)

Sun, Ren (Inventor); Ho, Chih-Ming (Inventor); Wong, Pak Kin (Inventor); Yu, Fuqu (Inventor)

2014-01-01

The invention provides systems and methods for manipulating biological systems, for example to elicit a more desired biological response from a biological sample, such as a tissue, organ, and/or a cell. In one aspect, the invention operates by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The invention can be used, e.g., to optimize any biological system, e.g., bioreactors for proteins, and the like, small molecules, polysaccharides, lipids, and the like. Another use of the apparatus and methods includes is for the discovery of key parameters in complex biological systems.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y.; Liu, Z.; Zhang, S.

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Use of Graph Database for the Integration of Heterogeneous Biological Data.

PubMed

Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young

2017-03-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data.

Use of Graph Database for the Integration of Heterogeneous Biological Data

PubMed Central

Yoon, Byoung-Ha; Kim, Seon-Kyu

2017-01-01

Understanding complex relationships among heterogeneous biological data is one of the fundamental goals in biology. In most cases, diverse biological data are stored in relational databases, such as MySQL and Oracle, which store data in multiple tables and then infer relationships by multiple-join statements. Recently, a new type of database, called the graph-based database, was developed to natively represent various kinds of complex relationships, and it is widely used among computer science communities and IT industries. Here, we demonstrate the feasibility of using a graph-based database for complex biological relationships by comparing the performance between MySQL and Neo4j, one of the most widely used graph databases. We collected various biological data (protein-protein interaction, drug-target, gene-disease, etc.) from several existing sources, removed duplicate and redundant data, and finally constructed a graph database containing 114,550 nodes and 82,674,321 relationships. When we tested the query execution performance of MySQL versus Neo4j, we found that Neo4j outperformed MySQL in all cases. While Neo4j exhibited a very fast response for various queries, MySQL exhibited latent or unfinished responses for complex queries with multiple-join statements. These results show that using graph-based databases, such as Neo4j, is an efficient way to store complex biological relationships. Moreover, querying a graph database in diverse ways has the potential to reveal novel relationships among heterogeneous biological data. PMID:28416946

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

2014-01-01

Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

Determination of the dynamical behaviour of biological materials during impact using a pendulum device

NASA Astrophysics Data System (ADS)

Van Zeebroeck, M.; Tijskens, E.; Van Liedekerke, P.; Deli, V.; De Baerdemaeker, J.; Ramon, H.

2003-09-01

A pendulum device has been developed to measure contact force, displacement and displacement rate of an impactor during its impact on the sample. Displacement, classically measured by double integration of an accelerometer, was determined in an alternative way using a more accurate incremental optical encoder. The parameters of the Kuwabara-Kono contact force model for impact of spheres have been estimated using an optimization method, taking the experimentally measured displacement, displacement rate and contact force into account. The accuracy of the method was verified using a rubber ball. Contact force parameters for the Kuwabara-Kono model have been estimated with success for three biological materials, i.e., apples, tomatoes and potatoes. The variability in the parameter estimations for the biological materials was quite high and can be explained by geometric differences (radius of curvature) and by biological variation of mechanical tissue properties.

Clinico-pathological and biological prognostic variables in squamous cell carcinoma of the vulva.

PubMed

Gadducci, Angiolo; Tana, Roberta; Barsotti, Cecilia; Guerrieri, Maria Elena; Genazzani, Andrea Riccardo

2012-07-01

Several clinical-pathological parameters have been related to survival of patients with invasive squamous cell carcinoma of the vulva, whereas few studies have investigated the ability of biological variables to predict the clinical outcome of these patients. The present paper reviews the literature data on the prognostic relevance of lymph node-related parameters, primary tumor-related parameters, FIGO stage, blood variables, and tissue biological variables. Regarding these latter, the paper takes into account the analysis of DNA content, cell cycle-regulatory proteins, apoptosis-related proteins, epidermal growth factor receptor [EGFR], and proteins that are involved in tumor invasiveness, metastasis and angiogenesis. At present, the lymph node status and FIGO stage according to the new 2009 classification system are the main predictors for vulvar squamous cell carcinoma, whereas biological variables do not have yet a clinical relevance and their role is still investigational. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Bio-logging of physiological parameters in higher marine vertebrates

NASA Astrophysics Data System (ADS)

Ponganis, Paul J.

2007-02-01

Bio-logging of physiological parameters in higher marine vertebrates had its origins in the field of bio-telemetry in the 1960s and 1970s. The development of microprocessor technology allowed its first application to bio-logging investigations of Weddell seal diving physiology in the early 1980s. Since that time, with the use of increased memory capacity, new sensor technology, and novel data processing techniques, investigators have examined heart rate, temperature, swim speed, stroke frequency, stomach function (gastric pH and motility), heat flux, muscle oxygenation, respiratory rate, diving air volume, and oxygen partial pressure (P) during diving. Swim speed, heart rate, and body temperature have been the most commonly studied parameters. Bio-logging investigation of pressure effects has only been conducted with the use of blood samplers and nitrogen analyses on animals diving at isolated dive holes. The advantages/disadvantages and limitations of recording techniques, probe placement, calibration techniques, and study conditions are reviewed.

[Assessment of the pain patients with the multiple sclerosis after applying the physiotherapy treatment].

PubMed

Kubsik, Anna; Klimkiewicz, Robert; Klimkiewicz, Paulina; Janczewska, Katarzyna; Jankowska, Agnieszka; Łukasiak, Adam; Woldańska-Okońska, Marta

2016-04-01

Multiple sclerosis is one of the most common demyelinating disease of the CNS connected with the autoimmune action. The effect of the disease is progressive disability, and one of the symptoms is pain. In relieving pain in the course of MS physical procedures and exercises of physiotherapy are used. The aim of the study was assessment of the pain in patients with the multiple sclerosis after applying laser radiation, magnetostimulation and kinesiotherapy. The studied material was consisted of 120 patients with multiple sclerosis of both sexes (82 women and 38 men) aged 21-81 years. Patients were randomly divided into 4 treatment groups and the assesment was performed three times. In the first group laser therapy, in the group II laser and magnetostimulation, in the third group kinesiotherapy, in the fourth group magnetostimulation was used. The same program of physiotherapy in all groups was used. All patients were performed the following tests to assess of the pain: The Laitinen Modified Questionnaire Indicators of Pain of and the Visual- Analogue Scale (VAS). In all treatment groups was observed tends to decrease a result of a point in The Laitinen Modified Questionnaire Indicators of Pain and the Visual-Analogue Scale (VAS). Correlation between groups demonstrated statistically significant result on the level p<0.05 in the group where the laser treatment was applied towards group II assessed with parameter of the Questionnaire of Pain according to Laitinen, as well as towards group II and III assessed with parameter - of the Visual Analogue Scale (VAS). The good result, i.e. the reduction of the spot value, after the III examination towards the preliminary examination were got in the group II. Laser radiation is an effective method which has an analgesisc action. The combination of laser radiation and magnetostimulation reduces pain in patients with multiple sclerosis, and also allows to maintain a therapeutic effect even after the cessation of the application of these procedures, which indicates the possibility to elicitation the biological phenomenon of hysteresis in these methods. © 2016 MEDPRESS.

Designed multiple ligands in metabolic disease research: from concept to platform.

PubMed

Gattrell, W; Johnstone, C; Patel, S; Smith, C Sambrook; Scheel, A; Schindler, M

2013-08-01

Type 2 diabetes mellitus (T2DM) is a multifactorial disease, and drug monotherapy typically results in unsatisfactory treatment outcomes for patients. Even when used in combination, existing therapies lack efficacy in the long term. Designed multiple ligands (DMLs) are compounds developed to modulate multiple targets relevant to a disease. DMLs offer the potential to yield greater efficacy over monotherapies, either by modulating different biological pathways, or by boosting a single one. However, examples of DMLs progressing into clinical trials, or onto the market are rare; DML drug discovery is challenging, and perceived by some to be almost impossible. Nevertheless, with the judicious selection of biological targets, both from a biological and chemical perspective, it is possible to develop drug-like DMLs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Determination of morphological parameters of biological cells by analysis of scattered-light distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burger, D.E.

1979-11-01

The extraction of morphological parameters from biological cells by analysis of light-scatter patterns is described. A light-scattering measurement system has been designed and constructed that allows one to visually examine and photographically record biological cells or cell models and measure the light-scatter pattern of an individual cell or cell model. Using a laser or conventional illumination, the imaging system consists of a modified microscope with a 35 mm camera attached to record the cell image or light-scatter pattern. Models of biological cells were fabricated. The dynamic range and angular distributions of light scattered from these models was compared to calculatedmore » distributions. Spectrum analysis techniques applied on the light-scatter data give the sought after morphological cell parameters. These results compared favorably to shape parameters of the fabricated cell models confirming the mathematical model procedure. For nucleated biological material, correct nuclear and cell eccentricity as well as the nuclear and cytoplasmic diameters were determined. A method for comparing the flow equivalent of nuclear and cytoplasmic size to the actual dimensions is shown. This light-scattering experiment provides baseline information for automated cytology. In its present application, it involves correlating average size as measured in flow cytology to the actual dimensions determined from this technique. (ERB)« less

Impact of biology knowledge on the conservation and management of large pelagic sharks.

PubMed

Yokoi, Hiroki; Ijima, Hirotaka; Ohshimo, Seiji; Yokawa, Kotaro

2017-09-06

Population growth rate, which depends on several biological parameters, is valuable information for the conservation and management of pelagic sharks, such as blue and shortfin mako sharks. However, reported biological parameters for estimating the population growth rates of these sharks differ by sex and display large variability. To estimate the appropriate population growth rate and clarify relationships between growth rate and relevant biological parameters, we developed a two-sex age-structured matrix population model and estimated the population growth rate using combinations of biological parameters. We addressed elasticity analysis and clarified the population growth rate sensitivity. For the blue shark, the estimated median population growth rate was 0.384 with a range of minimum and maximum values of 0.195-0.533, whereas those values of the shortfin mako shark were 0.102 and 0.007-0.318, respectively. The maturity age of male sharks had the largest impact for blue sharks, whereas that of female sharks had the largest impact for shortfin mako sharks. Hypotheses for the survival process of sharks also had a large impact on the population growth rate estimation. Both shark maturity age and survival rate were based on ageing validation data, indicating the importance of validating the quality of these data for the conservation and management of large pelagic sharks.

Effects of imputation on correlation: implications for analysis of mass spectrometry data from multiple biological matrices.

PubMed

Taylor, Sandra L; Ruhaak, L Renee; Kelly, Karen; Weiss, Robert H; Kim, Kyoungmi

2017-03-01

With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly. © The Author 2016. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables.

PubMed

Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C; Downing, James R; Lamba, Jatinder

2009-08-15

In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org.

Cooperativity to increase Turing pattern space for synthetic biology.

PubMed

Diambra, Luis; Senthivel, Vivek Raj; Menendez, Diego Barcena; Isalan, Mark

2015-02-20

It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction-diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose-response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction-diffusion systems and will help to guide projects to engineer synthetic Turing patterns.

Multiplication and Presence of Shielding Material from Time-Correlated Pulse-Height Measurements of Subcritical Plutonium Assemblies

DOE PAGES

Monterial, Mateusz; Marleau, Peter; Paff, Marc; ...

2017-01-20

Here, we present the results from the first measurements of the Time-Correlated Pulse-Height (TCPH) distributions from 4.5 kg sphere of α-phase weapons-grade plutonium metal in five configurations: bare, reflected by 1.27 cm and 2.54 cm of tungsten, and 2.54 cm and 7.62 cm of polyethylene. A new method for characterizing source multiplication and shielding configuration is also demonstrated. The method relies on solving for the underlying fission chain timing distribution that drives the spreading of the measured TCPH distribution. We found that a gamma distribution fits the fission chain timing distribution well and that the fit parameters correlate with bothmore » multiplication (rate parameter) and shielding material types (shape parameter). The source-to-detector distance was another free parameter that we were able to optimize, and proved to be the most well constrained parameter. MCNPX-PoliMi simulations were used to complement the measurements and help illustrate trends in these parameters and their relation to multiplication and the amount and type of material coupled to the subcritical assembly.« less

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

Multiplication and Presence of Shielding Material from Time-Correlated Pulse-Height Measurements of Subcritical Plutonium Assemblies

NASA Astrophysics Data System (ADS)

Monterial, Mateusz; Marleau, Peter; Paff, Marc; Clarke, Shaun; Pozzi, Sara

2017-04-01

We present the results from the first measurements of the Time-Correlated Pulse-Height (TCPH) distributions from 4.5 kg sphere of α-phase weapons-grade plutonium metal in five configurations: bare, reflected by 1.27 cm and 2.54 cm of tungsten, and 2.54 cm and 7.62 cm of polyethylene. A new method for characterizing source multiplication and shielding configuration is also demonstrated. The method relies on solving for the underlying fission chain timing distribution that drives the spreading of the measured TCPH distribution. We found that a gamma distribution fits the fission chain timing distribution well and that the fit parameters correlate with both multiplication (rate parameter) and shielding material types (shape parameter). The source-to-detector distance was another free parameter that we were able to optimize, and proved to be the most well constrained parameter. MCNPX-PoliMi simulations were used to complement the measurements and help illustrate trends in these parameters and their relation to multiplication and the amount and type of material coupled to the subcritical assembly.

Evolutionary optimization with data collocation for reverse engineering of biological networks.

PubMed

Tsai, Kuan-Yao; Wang, Feng-Sheng

2005-04-01

Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.

Assessing the effect of selection with deltamethrin on biological parameters and detoxifying enzymes in Aedes aegypti (L.).

PubMed

Alvarez-Gonzalez, Leslie C; Briceño, Arelis; Ponce-Garcia, Gustavo; Villanueva-Segura, O Karina; Davila-Barboza, Jesus A; Lopez-Monroy, Beatriz; Gutierrez-Rodriguez, Selene M; Contreras-Perera, Yamili; Rodriguez-Sanchez, Iram P; Flores, Adriana E

2017-11-01

Resistance to insecticides through one or several mechanisms has a cost for an insect in various parameters of its biological cycle. The present study evaluated the effect of deltamethrin on detoxifying enzymes and biological parameters in a population of Aedes aegypti selected for 15 generations. The enzyme activities of alpha- and beta-esterases, mixed-function oxidases and glutathione-S-transferases were determined during selection, along with biological parameters. Overexpression of mixed-function oxidases as a mechanism of metabolic resistance to deltamethrin was found. There were decreases in percentages of eggs hatching, pupation and age-specific survival and in total survival at the end of the selection (F 16 ). Although age-specific fecundity was not affected by selection with deltamethrin, total fertility, together with lower survival, significantly affected gross reproduction rate, gradually decreasing due to deltamethrin selection. Similarly, net reproductive rate and intrinsic growth rate were affected by selection. Alterations in life parameters could be due to the accumulation of noxious effects or deleterious genes related to detoxifying enzymes, specifically those coding for mixed-function oxidases, along with the presence of recessive alleles of the V1016I and F1534C mutations, associating deltamethrin resistance with fitness cost in Ae. aegypti. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamp, F.; Brueningk, S.C.; Wilkens, J.J.

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g.more » RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less

Bottom-up synthetic biology: modular design for making artificial platelets

NASA Astrophysics Data System (ADS)

Majumder, Sagardip; Liu, Allen P.

2018-01-01

Engineering artificial cells to mimic one or multiple fundamental cell biological functions is an emerging area of synthetic biology. Reconstituting functional modules from biological components in vitro is a challenging yet an important essence of bottom-up synthetic biology. Here we describe the concept of building artificial platelets using bottom-up synthetic biology and the four functional modules that together could enable such an ambitious effort.

Controlled Release Strategies for Bone, Cartilage, and Osteochondral Engineering—Part II: Challenges on the Evolution from Single to Multiple Bioactive Factor Delivery

PubMed Central

Santo, Vítor E.; Mano, João F.; Reis, Rui L.

2013-01-01

The development of controlled release systems for the regeneration of bone, cartilage, and osteochondral interface is one of the hot topics in the field of tissue engineering and regenerative medicine. However, the majority of the developed systems consider only the release of a single growth factor, which is a limiting step for the success of the therapy. More recent studies have been focused on the design and tailoring of appropriate combinations of bioactive factors to match the desired goals regarding tissue regeneration. In fact, considering the complexity of extracellular matrix and the diversity of growth factors and cytokines involved in each biological response, it is expected that an appropriate combination of bioactive factors could lead to more successful outcomes in tissue regeneration. In this review, the evolution on the development of dual and multiple bioactive factor release systems for bone, cartilage, and osteochondral interface is overviewed, specifically the relevance of parameters such as dosage and spatiotemporal distribution of bioactive factors. A comprehensive collection of studies focused on the delivery of bioactive factors is also presented while highlighting the increasing impact of platelet-rich plasma as an autologous source of multiple growth factors. PMID:23249320

Scaling in cognitive performance reflects multiplicative multifractal cascade dynamics

PubMed Central

Stephen, Damian G.; Anastas, Jason R.; Dixon, James A.

2012-01-01

Self-organized criticality purports to build multi-scaled structures out of local interactions. Evidence of scaling in various domains of biology may be more generally understood to reflect multiplicative interactions weaving together many disparate scales. The self-similarity of power-law scaling entails homogeneity: fluctuations distribute themselves similarly across many spatial and temporal scales. However, this apparent homogeneity can be misleading, especially as it spans more scales. Reducing biological processes to one power-law relationship neglects rich cascade dynamics. We review recent research into multifractality in executive-function cognitive tasks and propose that scaling reflects not criticality but instead interactions across multiple scales and among fluctuations of multiple sizes. PMID:22529819

Magnetic steering control of multi-cellular bio-hybrid microswimmers.

PubMed

Carlsen, Rika Wright; Edwards, Matthew R; Zhuang, Jiang; Pacoret, Cecile; Sitti, Metin

2014-10-07

Bio-hybrid devices, which integrate biological cells with synthetic components, have opened a new path in miniaturized systems with the potential to provide actuation and control for systems down to a few microns in size. Here, we address the challenge of remotely controlling bio-hybrid microswimmers propelled by multiple bacterial cells. These devices have been proposed as a viable method for targeted drug delivery but have also been shown to exhibit stochastic motion. We demonstrate a method of remote magnetic control that significantly reduces the stochasticity of the motion, enabling steering control. The demonstrated microswimmers consist of multiple Serratia marcescens (S. marcescens) bacteria attached to a 6 μm-diameter superparamagnetic bead. We characterize their motion and define the parameters governing their controllability. We show that the microswimmers can be controlled along two-dimensional (2-D) trajectories using weak magnetic fields (≤10 mT) and can achieve 2-D swimming speeds up to 7.3 μm s(-1). This magnetic steering approach can be integrated with sensory-based steering in future work, enabling new control strategies for bio-hybrid microsystems.

The Genome Portal of the Department of Energy Joint Genome Institute

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nordberg, Henrik; Cantor, Michael; Dushekyo, Serge

2014-03-14

The JGI Genome Portal (http://genome.jgi.doe.gov) provides unified access to all JGI genomic databases and analytical tools. A user can search, download and explore multiple data sets available for all DOE JGI sequencing projects including their status, assemblies and annotations of sequenced genomes. Genome Portal in the past 2 years was significantly updated, with a specific emphasis on efficient handling of the rapidly growing amount of diverse genomic data accumulated in JGI. A critical aspect of handling big data in genomics is the development of visualization and analysis tools that allow scientists to derive meaning from what are otherwise terrabases ofmore » inert sequence. An interactive visualization tool developed in the group allows us to explore contigs resulting from a single metagenome assembly. Implemented with modern web technologies that take advantage of the power of the computer's graphical processing unit (gpu), the tool allows the user to easily navigate over a 100,000 data points in multiple dimensions, among many biologically meaningful parameters of a dataset such as relative abundance, contig length, and G+C content.« less

The Role of C-Peptide as Marker of Cardiometabolic Risk in Women With Polycystic Ovary Syndrome: A Controlled Study

PubMed Central

de Medeiros, Sebastiao Freitas; Angelo, Laura Camila Antunes; de Medeiros, Matheus Antonio Souto; Banhara, Camila Regis; Barbosa, Bruna Barcelo; Yamamoto, Marcia Marly Winck

2018-01-01

Background The aim of this study was to examine the role of C-peptide as a biological marker of cardiometabolic risk in polycystic ovary syndrome (PCOS). Methods This case-control study enrolled 385 PCOS patients and 240 normal cycling women. Anthropometric and clinical variables were taken at first visit. Fasting C-peptide, glucose, lipids, and hormone measurements were performed. Simple and multiple correlations between C-peptide and other variables associated with dysmetabolism and cardiovascular disease were examined. Results C-peptide was well correlated with several anthropometric, metabolic, and endocrine parameters. In PCOS patients, stepwise multiple regression including C-peptide as the criterion variable and other predictors of cardiovascular disease risk provided a significant model in which the fasting C-peptide/glucose ratio, glucose, body weight, and free estrogen index (FEI) were retained (adjusted R2 = 0.988, F = 7.161, P = 0.008). Conclusion C-peptide levels alone or combined with C-peptide/glucose ratio, glucose, body weight, and FEI provided a significant model to identify PCOS patients with higher risk of future cardiometabolic diseases. PMID:29416587

Transbulbar B-Mode Sonography in Multiple Sclerosis: Clinical and Biological Relevance.

PubMed

De Masi, Roberto; Orlando, Stefania; Conte, Aldo; Pasca, Sergio; Scarpello, Rocco; Spagnolo, Pantaleo; Muscella, Antonella; De Donno, Antonella

2016-12-01

Optic nerve sheath diameter quantification by transbulbar B-mode sonography is a recently validated technique, but its clinical relevance in relapse-free multiple sclerosis patients remains unexplored. In an open-label, comparative, cross-sectional study, we aimed to assess possible differences between patients and healthy controls in terms of optic nerve sheath diameter and its correlation with clinical/paraclinical parameters in this disease. Sixty unselected relapse-free patients and 35 matched healthy controls underwent transbulbar B-mode sonography. Patients underwent routine neurologic examination, brain magnetic resonance imaging and visual evoked potential tests. The mean optic nerve sheath diameter 3 and 5 mm from the eyeball was 22-25% lower in patients than controls and correlated with the Expanded Disability Status Scale (r = -0.34, p = 0.048, and r = -0.32, p = 0.042, respectively). We suggest that optic nerve sheath diameter quantified by transbulbar B-mode sonography should be included in routine assessment of the disease as an extension of the neurologic examination. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Feynman formulas for semigroups generated by an iterated Laplace operator

NASA Astrophysics Data System (ADS)

Buzinov, M. S.

2017-04-01

In the present paper, we find representations of a one-parameter semigroup generated by a finite sum of iterated Laplace operators and an additive perturbation (the potential). Such semigroups and the evolution equations corresponding to them find applications in the field of physics, chemistry, biology, and pattern recognition. The representations mentioned above are obtained in the form of Feynman formulas, i.e., in the form of a limit of multiple integrals as the multiplicity tends to infinity. The term "Feynman formula" was proposed by Smolyanov. Smolyanov's approach uses Chernoff's theorems. A simple form of representations thus obtained enables one to use them for numerical modeling the dynamics of the evolution system as a method for the approximation of solutions of equations. The problems considered in this note can be treated using the approach suggested by Remizov (see also the monograph of Smolyanov and Shavgulidze on path integrals). The representations (of semigroups) obtained in this way are more complicated than those given by the Feynman formulas; however, it is possible to bypass some analytical difficulties.

Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.

PubMed

Hüffner, Falk; Komusiewicz, Christian; Niedermeier, Rolf; Wernicke, Sebastian

2017-01-01

Fixed-parameter algorithms are designed to efficiently find optimal solutions to some computationally hard (NP-hard) problems by identifying and exploiting "small" problem-specific parameters. We survey practical techniques to develop such algorithms. Each technique is introduced and supported by case studies of applications to biological problems, with additional pointers to experimental results.

Ordinary differential equations with applications in molecular biology.

PubMed

Ilea, M; Turnea, M; Rotariu, M

2012-01-01

Differential equations are of basic importance in molecular biology mathematics because many biological laws and relations appear mathematically in the form of a differential equation. In this article we presented some applications of mathematical models represented by ordinary differential equations in molecular biology. The vast majority of quantitative models in cell and molecular biology are formulated in terms of ordinary differential equations for the time evolution of concentrations of molecular species. Assuming that the diffusion in the cell is high enough to make the spatial distribution of molecules homogenous, these equations describe systems with many participating molecules of each kind. We propose an original mathematical model with small parameter for biological phospholipid pathway. All the equations system includes small parameter epsilon. The smallness of epsilon is relative to the size of the solution domain. If we reduce the size of the solution region the same small epsilon will result in a different condition number. It is clear that the solution for a smaller region is less difficult. We introduce the mathematical technique known as boundary function method for singular perturbation system. In this system, the small parameter is an asymptotic variable, different from the independent variable. In general, the solutions of such equations exhibit multiscale phenomena. Singularly perturbed problems form a special class of problems containing a small parameter which may tend to zero. Many molecular biology processes can be quantitatively characterized by ordinary differential equations. Mathematical cell biology is a very active and fast growing interdisciplinary area in which mathematical concepts, techniques, and models are applied to a variety of problems in developmental medicine and bioengineering. Among the different modeling approaches, ordinary differential equations (ODE) are particularly important and have led to significant advances. Ordinary differential equations are used to model biological processes on various levels ranging from DNA molecules or biosynthesis phospholipids on the cellular level.

On determining firing delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2010-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

On determining firing delay time of transitions for petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2011-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

Variation in skin biology to climate in Shanghai, China.

PubMed

Liu, Xiaoping; Gao, Yanrui; Zhang, Yiyi; Wang, Xuemin

2017-09-01

To explore the relationship between climate and skin condition, and to investigate the variation of skin biology to climatic change. In total, 2005 healthy Chinese volunteers living in Shanghai (aged 13-69 years) were recruited. Transepidermal water loss (TEWL) and SCH were tested on six sites (forehead, cheek, nasolabial, inner forearm, dorsal hand, and palm) by noninvasive devices between January 2005 and December 2012. The corresponding climate data were recorded by local Weather Bureau. TEWL was increased with atmospheric pressure and decreased with temperature, steam pressure, and relative humidity (p < 0.05). SCH was increased with steam pressure and decreased with atmospheric pressure (p < 0.05); there was no obvious trend between SCH and temperature and SCH and relative humidity. To investigate the climate parameters together, we introduced these correlated factors into the multivariate linear regression model which demonstrated that temperature and steam pressure were main factors related to skin biological parameters. At different sites, the effect of climatic factors on skin biology was diverse. Skin biological parameters are associated with climatic factors. Different sites have different sensitivity to climate factors.

Biomine: predicting links between biological entities using network models of heterogeneous databases.

PubMed

Eronen, Lauri; Toivonen, Hannu

2012-06-06

Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.

Automated carbon dioxide cleaning system

NASA Technical Reports Server (NTRS)

Hoppe, David T.

1991-01-01

Solidified CO2 pellets are an effective blast media for the cleaning of a variety of materials. CO2 is obtained from the waste gas streams generated from other manufacturing processes and therefore does not contribute to the greenhouse effect, depletion of the ozone layer, or the environmental burden of hazardous waste disposal. The system is capable of removing as much as 90 percent of the contamination from a surface in one pass or to a high cleanliness level after multiple passes. Although the system is packaged and designed for manual hand held cleaning processes, the nozzle can easily be attached to the end effector of a robot for automated cleaning of predefined and known geometries. Specific tailoring of cleaning parameters are required to optimize the process for each individual geometry. Using optimum cleaning parameters the CO2 systems were shown to be capable of cleaning to molecular levels below 0.7 mg/sq ft. The systems were effective for removing a variety of contaminants such as lubricating oils, cutting oils, grease, alcohol residue, biological films, and silicone. The system was effective on steel, aluminum, and carbon phenolic substrates.

Distribution uniformity of laser-accelerated proton beams

NASA Astrophysics Data System (ADS)

Zhu, Jun-Gao; Zhu, Kun; Tao, Li; Xu, Xiao-Han; Lin, Chen; Ma, Wen-Jun; Lu, Hai-Yang; Zhao, Yan-Ying; Lu, Yuan-Rong; Chen, Jia-Er; Yan, Xue-Qing

2017-09-01

Compared with conventional accelerators, laser plasma accelerators can generate high energy ions at a greatly reduced scale, due to their TV/m acceleration gradient. A compact laser plasma accelerator (CLAPA) has been built at the Institute of Heavy Ion Physics at Peking University. It will be used for applied research like biological irradiation, astrophysics simulations, etc. A beamline system with multiple quadrupoles and an analyzing magnet for laser-accelerated ions is proposed here. Since laser-accelerated ion beams have broad energy spectra and large angular divergence, the parameters (beam waist position in the Y direction, beam line layout, drift distance, magnet angles etc.) of the beamline system are carefully designed and optimised to obtain a radially symmetric proton distribution at the irradiation platform. Requirements of energy selection and differences in focusing or defocusing in application systems greatly influence the evolution of proton distributions. With optimal parameters, radially symmetric proton distributions can be achieved and protons with different energy spread within ±5% have similar transverse areas at the experiment target. Supported by National Natural Science Foundation of China (11575011, 61631001) and National Grand Instrument Project (2012YQ030142)

Heart rate variability in adolescents with functional hypothalamic amenorrhea and anorexia nervosa.

PubMed

Bomba, Monica; Corbetta, Fabiola; Gambera, Alessandro; Nicosia, Franco; Bonini, Luisa; Neri, Francesca; Tremolizzo, Lucio; Nacinovich, Renata

2014-02-28

Aim of this study consisted in assessing the 24-h heart rate variability (HRV), a measure of autonomic nervous system (ANS) imbalance, in 21 adolescents with functional hypothalamic amenorrhea (FHA, 11 normogonadotropic, N-FHA, and 10 hypogonadotropic, Hy-FHA) compared to 21 patients with anorexia nervosa (AN) and 21 controls. As expected, subjects with AN showed a significant dysregulation in multiple HRV parameters, while Hy-FHA patients presented with a dysregulation in a few domains (SDNN, HFr), which was not present in girls with N-FHA, who showed values largely similar to controls. FHA might represent part of the AN biological spectrum, and a link between these two conditions might exist, possibly related to the degree of psychological and/or hormonal dysfunction. © 2013 Published by Elsevier Ireland Ltd.

Numerical algebraic geometry for model selection and its application to the life sciences

PubMed Central

Gross, Elizabeth; Davis, Brent; Ho, Kenneth L.; Bates, Daniel J.

2016-01-01

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation and model selection. These are all optimization problems, well known to be challenging due to nonlinearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data are available. Here, we consider polynomial models (e.g. mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometrical structures relating models and data, and we demonstrate its utility on examples from cell signalling, synthetic biology and epidemiology. PMID:27733697

Scarless assembly of unphosphorylated DNA fragments with a simplified DATEL method.

PubMed

Ding, Wenwen; Weng, Huanjiao; Jin, Peng; Du, Guocheng; Chen, Jian; Kang, Zhen

2017-05-04

Efficient assembly of multiple DNA fragments is a pivotal technology for synthetic biology. A scarless and sequence-independent DNA assembly method (DATEL) using thermal exonucleases has been developed recently. Here, we present a simplified DATEL (sDATEL) for efficient assembly of unphosphorylated DNA fragments with low cost. The sDATEL method is only dependent on Taq DNA polymerase and Taq DNA ligase. After optimizing the committed parameters of the reaction system such as pH and the concentration of Mg 2+ and NAD+, the assembly efficiency was increased by 32-fold. To further improve the assembly capacity, the number of thermal cycles was optimized, resulting in successful assembly 4 unphosphorylated DNA fragments with an accuracy of 75%. sDATEL could be a desirable method for routine manual and automated assembly.

Measuring molecular motions inside single cells with improved analysis of single-particle trajectories

NASA Astrophysics Data System (ADS)

Rowland, David J.; Biteen, Julie S.

2017-04-01

Single-molecule super-resolution imaging and tracking can measure molecular motions inside living cells on the scale of the molecules themselves. Diffusion in biological systems commonly exhibits multiple modes of motion, which can be effectively quantified by fitting the cumulative probability distribution of the squared step sizes in a two-step fitting process. Here we combine this two-step fit into a single least-squares minimization; this new method vastly reduces the total number of fitting parameters and increases the precision with which diffusion may be measured. We demonstrate this Global Fit approach on a simulated two-component system as well as on a mixture of diffusing 80 nm and 200 nm gold spheres to show improvements in fitting robustness and localization precision compared to the traditional Local Fit algorithm.

Biomimetic strategies for engineering composite tissues.

PubMed

Lee, Nancy; Robinson, Jennifer; Lu, Helen

2016-08-01

The formation of multiple tissue types and their integration into composite tissue units presents a frontier challenge in regenerative engineering. Tissue-tissue synchrony is crucial in providing structural support for internal organs and enabling daily activities. This review highlights the state-of-the-art in composite tissue scaffold design, and explores how biomimicry can be strategically applied to avoid over-engineering the scaffold. Given the complexity of biological tissues, determining the most relevant parameters for recapitulating native structure-function relationships through strategic biomimicry will reduce the burden for clinical translation. It is anticipated that these exciting efforts in composite tissue engineering will enable integrative and functional repair of common soft tissue injuries and lay the foundation for total joint or limb regeneration. Copyright © 2016 Elsevier Ltd. All rights reserved.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Mathematics as a Conduit for Translational Research in Post-Traumatic Osteoarthritis

PubMed Central

Ayati, Bruce P.; Kapitanov, Georgi I.; Coleman, Mitchell C.; Anderson, Donald D.; Martin, James A.

2016-01-01

Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a “conduit of translation”. The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. PMID:27653021

Promoting inquiry-based teaching in laboratory courses: are we meeting the grade?

PubMed

Beck, Christopher; Butler, Amy; da Silva, Karen Burke

2014-01-01

Over the past decade, repeated calls have been made to incorporate more active teaching and learning in undergraduate biology courses. The emphasis on inquiry-based teaching is especially important in laboratory courses, as these are the courses in which students are applying the process of science. To determine the current state of research on inquiry-based teaching in undergraduate biology laboratory courses, we reviewed the recent published literature on inquiry-based exercises. The majority of studies in our data set were in the subdisciplines of biochemistry, cell biology, developmental biology, genetics, and molecular biology. In addition, most exercises were guided inquiry, rather than open ended or research based. Almost 75% of the studies included assessment data, with two-thirds of these studies including multiple types of assessment data. However, few exercises were assessed in multiple courses or at multiple institutions. Furthermore, assessments were rarely based on published instruments. Although the results of the studies in our data set show a positive effect of inquiry-based teaching in biology laboratory courses on student learning gains, research that uses the same instrument across a range of courses and institutions is needed to determine whether these results can be generalized. © 2014 C. Beck et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Multiscale Hy3S: hybrid stochastic simulation for supercomputers.

PubMed

Salis, Howard; Sotiropoulos, Vassilios; Kaznessis, Yiannis N

2006-02-24

Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems.

Multisensor Instrument for Real-Time Biological Monitoring

NASA Technical Reports Server (NTRS)

Zhang, Sean (Zhanxiang); Xu, Guoda; Qiu, Wei; Lin, Freddie

2004-01-01

The figure schematically depicts an instrumentation system, called a fiber optic-based integration system (FOBIS), that is undergoing development to enable real-time monitoring of fluid cell cultures, bioprocess flows, and the like. The FOBIS design combines a micro flow cytometer (MFC), a microphotometer (MP), and a fluorescence-spectrum- or binding-force-measuring micro-sensor (MS) in a single instrument that is capable of measuring multiple biological parameters simultaneously or sequentially. The fiber-optic-based integration system is so named because the MFC, the MP, and the MS are integrated into a single optical system that is coupled to light sources and photometric equipment via optical fibers. The optical coupling components also include a wavelength-division multiplexer and diffractive optical elements. The FOBIS includes a laserdiode- and fiber-optic-based optical trapping subsystem (optical tweezers ) with microphotometric and micro-sensing capabilities for noninvasive confinement and optical measurement of relevant parameters of a single cell or other particle. Some of the measurement techniques implemented together by the FOBIS have long been used separately to obtain basic understanding of the optical properties of individual cells and other organisms, the optical properties of populations of organisms, and the interrelationships among these properties, physiology of the organisms, and physical processes that govern the media that surround the organisms. For example, flow cytometry yields information on numerical concentrations, cross-sectional areas, and types of cells or other particles. Micro-sensing can be used to measure pH and concentrations of oxygen, carbon dioxide, glucose, metabolites, calcium, and antigens in a cell-culture fluid, thereby providing feedback that can be helpful in improving control over a bioprocess. Microphotometry (including measurements of scattering and fluorescence) can yield further information about optically trapped individual particles. In addition to the multifunctionality not previously available in a single biological monitoring system, the FOBIS offers advantages of low mass, sensitivity, accuracy, portability, low cost, compactness (the overall dimensions of the fully developed FOBIS sensor head are expected to be less than 1 by 1 by 2 cm), and immunity to electromagnetic interference at suboptical frequencies. FOBIS could be useful in a variety of laboratory and field settings in such diverse endeavors as medical, veterinary, and general biological research; medical and veterinary diagnosis monitoring of industrial bioprocesses; and analysis of biological contaminants in air, water, and food.

ShinyKGode: an interactive application for ODE parameter inference using gradient matching.

PubMed

Wandy, Joe; Niu, Mu; Giurghita, Diana; Daly, Rónán; Rogers, Simon; Husmeier, Dirk

2018-07-01

Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualized alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. Supplementary data are available at Bioinformatics online.

Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change-A review.

PubMed

Boyd, Philip W; Collins, Sinead; Dupont, Sam; Fabricius, Katharina; Gattuso, Jean-Pierre; Havenhand, Jonathan; Hutchins, David A; Riebesell, Ulf; Rintoul, Max S; Vichi, Marcello; Biswas, Haimanti; Ciotti, Aurea; Gao, Kunshan; Gehlen, Marion; Hurd, Catriona L; Kurihara, Haruko; McGraw, Christina M; Navarro, Jorge M; Nilsson, Göran E; Passow, Uta; Pörtner, Hans-Otto

2018-06-01

Marine life is controlled by multiple physical and chemical drivers and by diverse ecological processes. Many of these oceanic properties are being altered by climate change and other anthropogenic pressures. Hence, identifying the influences of multifaceted ocean change, from local to global scales, is a complex task. To guide policy-making and make projections of the future of the marine biosphere, it is essential to understand biological responses at physiological, evolutionary and ecological levels. Here, we contrast and compare different approaches to multiple driver experiments that aim to elucidate biological responses to a complex matrix of ocean global change. We present the benefits and the challenges of each approach with a focus on marine research, and guidelines to navigate through these different categories to help identify strategies that might best address research questions in fundamental physiology, experimental evolutionary biology and community ecology. Our review reveals that the field of multiple driver research is being pulled in complementary directions: the need for reductionist approaches to obtain process-oriented, mechanistic understanding and a requirement to quantify responses to projected future scenarios of ocean change. We conclude the review with recommendations on how best to align different experimental approaches to contribute fundamental information needed for science-based policy formulation. © 2018 John Wiley & Sons Ltd.

Adaption of Ulva pertusa to multiple-contamination of heavy metals and nutrients: Biological mechanism of outbreak of Ulva sp. green tide.

PubMed

Ge, Changzi; Yu, Xiru; Kan, Manman; Qu, Chunfeng

2017-12-15

The multiple-contamination of heavy metals and nutrients worsens increasingly and Ulva sp. green tide occurs almost simultaneously. To reveal the biological mechanism for outbreak of the green tide, Ulva pertusa was exposed to seven-day-multiple-contamination. The relation between pH variation (V pH ), Chl a content, ratio of (Chl a content)/(Chl b content) (R chla/chlb ), SOD activity of U. pertusa (A SOD ) and contamination concentration is [Formula: see text] (p<0.05), C chla =0.88 ±0.09 -0.01 ±0.00 ×C Cd (p<0.05), [Formula: see text] (p<0.05), and [Formula: see text] (p<0.05), respectively. C ammonia , C Cd and C Zn is concentration of ammonia, Cd 2+ and Zn 2+ , respectively. Comparing the contamination concentrations of seawaters where Ulva sp. green tide occurred and the contamination concentrations set in the present work, U. pertusa can adapt to multiple-contaminations in these waters. Thus, the adaption to multiple-contamination may be one biological mechanism for the outbreak of Ulva sp. green tide. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection of multiple perturbations in multi-omics biological networks.

PubMed

Griffin, Paula J; Zhang, Yuqing; Johnson, William Evan; Kolaczyk, Eric D

2018-05-17

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA). © 2018, The International Biometric Society.

Chemical biology 2012: from drug targets to biological systems and back.

PubMed

Socher, Elke; Grossmann, Tom N

2013-01-02

Multiple sites sharing a common target: This year's EMBO conference on chemical biology encouraged over 340 researchers to come to Heidelberg, Germany, and discuss the use of diverse chemical strategies and tools to investigate biological questions and better understand cellular processes. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it.

PubMed

Kell, Douglas B

2013-12-01

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially. © 2013 The Author Journal compilation © 2013 FEBS.

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables

PubMed Central

Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R.; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C.; Downing, James R.; Lamba, Jatinder

2009-01-01

Motivation: In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Results: Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Availability: Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org. Contact: stanley.pounds@stjude.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19528086

Evolutionary Tradeoffs between Economy and Effectiveness in Biological Homeostasis Systems

PubMed Central

Szekely, Pablo; Sheftel, Hila; Mayo, Avi; Alon, Uri

2013-01-01

Biological regulatory systems face a fundamental tradeoff: they must be effective but at the same time also economical. For example, regulatory systems that are designed to repair damage must be effective in reducing damage, but economical in not making too many repair proteins because making excessive proteins carries a fitness cost to the cell, called protein burden. In order to see how biological systems compromise between the two tasks of effectiveness and economy, we applied an approach from economics and engineering called Pareto optimality. This approach allows calculating the best-compromise systems that optimally combine the two tasks. We used a simple and general model for regulation, known as integral feedback, and showed that best-compromise systems have particular combinations of biochemical parameters that control the response rate and basal level. We find that the optimal systems fall on a curve in parameter space. Due to this feature, even if one is able to measure only a small fraction of the system's parameters, one can infer the rest. We applied this approach to estimate parameters in three biological systems: response to heat shock and response to DNA damage in bacteria, and calcium homeostasis in mammals. PMID:23950698

Evolutionary tradeoffs between economy and effectiveness in biological homeostasis systems.

PubMed

Szekely, Pablo; Sheftel, Hila; Mayo, Avi; Alon, Uri

2013-01-01

Biological regulatory systems face a fundamental tradeoff: they must be effective but at the same time also economical. For example, regulatory systems that are designed to repair damage must be effective in reducing damage, but economical in not making too many repair proteins because making excessive proteins carries a fitness cost to the cell, called protein burden. In order to see how biological systems compromise between the two tasks of effectiveness and economy, we applied an approach from economics and engineering called Pareto optimality. This approach allows calculating the best-compromise systems that optimally combine the two tasks. We used a simple and general model for regulation, known as integral feedback, and showed that best-compromise systems have particular combinations of biochemical parameters that control the response rate and basal level. We find that the optimal systems fall on a curve in parameter space. Due to this feature, even if one is able to measure only a small fraction of the system's parameters, one can infer the rest. We applied this approach to estimate parameters in three biological systems: response to heat shock and response to DNA damage in bacteria, and calcium homeostasis in mammals.

[Mathematic modeling and experimental validation of macrostate quality expression for multicomponent in Chinese materia medica].

PubMed

He, Fuyuan; Deng, Kaiwen; Shi, Jilian; Liu, Wenlong; Pi, Fengjuan

2011-11-01

To establish the unitive multicomponent quality system bridged macrostate mathematic model parameters of material quality and microstate component concentration for Chinese materia medica (CMM). According to law of biologic laws of thermodynamics, the state functions of macrostate qulity of the CMM were established. The validation test was carried out as modeling drug as alcohol extract of Radix Rhozome (AERR), their enthalpy of combustion was determined, and entropy and the capability of information by chromatographic fingerprint were assayed, and then the biologic apparent macrostate parameters were calculated. The biologic macrostate mathematic models, for the CMM quality controll, were established as parameters as the apparent equilibrium constant, biologic enthalpy, Gibbs free energy and biologic entropy etc. The total molarity for the 10 batchs of AERR were 0.153 4 mmol x g(-1) with 28.26% of RSD, with the average of apparent equilibrium constants, biologic enthalpy, Gibbs free energy and biologic entropy were 0.039 65, 8 005 J x mol(-1), -2.408 x 10(7) J x mol(-1) and - 8.078 x 10(4) J x K(-1) with RSD as 6.020%, 1.860%, 42.32% and 42.31%, respectively. The macrostate quality models for CMM can represent their intrinsic quality for multicomponent dynamic system such as the CMM, to manifest out as if the forest away from or tree near from to see it.

The Impact of Escape Alternative Position Change in Multiple-Choice Test on the Psychometric Properties of a Test and Its Items Parameters

ERIC Educational Resources Information Center

Hamadneh, Iyad Mohammed

2015-01-01

This study aimed at investigating the impact changing of escape alternative position in multiple-choice test on the psychometric properties of a test and it's items parameters (difficulty, discrimination & guessing), and estimation of examinee ability. To achieve the study objectives, a 4-alternative multiple choice type achievement test…

Detecting Biological Warfare Agents

PubMed Central

Song, Linan; Ahn, Soohyoun

2005-01-01

We developed a fiber-optic, microsphere-based, high-density array composed of 18 species-specific probe microsensors to identify biological warfare agents. We simultaneously identified multiple biological warfare agents in environmental samples by looking at specific probe responses after hybridization and response patterns of the multiplexed array. PMID:16318712

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

PubMed Central

Murakami, Yohei

2014-01-01

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

A tunable algorithm for collective decision-making.

PubMed

Pratt, Stephen C; Sumpter, David J T

2006-10-24

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

[Immunological Markers in Organ Transplantation].

PubMed

Beckmann, J H; Heits, N; Braun, F; Becker, T

2017-04-01

The immunological monitoring in organ transplantation is based mainly on the determination of laboratory parameters as surrogate markers of organ dysfunction. Structural damage, caused by alloreactivity, can only be detected by invasive biopsy of the graft, which is why inevitably rejection episodes are diagnosed at a rather progressive stage. New non-invasive specific markers that enable transplant clinicians to identify rejection episodes at an earlier stage, on the molecular level, are needed. The accurate identification of rejection episodes and the establishment of operational tolerance permit early treatment or, respectively, a controlled cessation of immunosuppression. In addition, new prognostic biological markers are expected to allow a pre-transplant risk stratification thus having an impact on organ allocation and immunosuppressive regimen. New high-throughput screening methods allow simultaneous examination of hundreds of characteristics and the generation of specific biological signatures, which might give concrete information about acute rejection, chronic dysfunction as well as operational tolerance. Even though multiple studies and a variety of publications report about important advances on this subject, almost no new biological marker has been implemented in clinical practice as yet. Nevertheless, new technologies, in particular analysis of the genome, transcriptome, proteome and metabolome will make personalised transplantation medicine possible and will further improve the long-term results and graft survival rates. This article gives a survey of the limitations and possibilities of new immunological markers in organ transplantation. Georg Thieme Verlag KG Stuttgart · New York.

Biological systems for human life support: Review of the research in the USSR

NASA Technical Reports Server (NTRS)

Shepelev, Y. Y.

1979-01-01

Various models of biological human life support systems are surveyed. Biological structures, dimensions, and functional parameters of man-chlorella-microorganism models are described. Significant observations and the results obtained from these models are reported.

Taguchi's off line method and Multivariate loss function approach for quality management and optimization of process parameters -A review

NASA Astrophysics Data System (ADS)

Bharti, P. K.; Khan, M. I.; Singh, Harbinder

2010-10-01

Off-line quality control is considered to be an effective approach to improve product quality at a relatively low cost. The Taguchi method is one of the conventional approaches for this purpose. Through this approach, engineers can determine a feasible combination of design parameters such that the variability of a product's response can be reduced and the mean is close to the desired target. The traditional Taguchi method was focused on ensuring good performance at the parameter design stage with one quality characteristic, but most products and processes have multiple quality characteristics. The optimal parameter design minimizes the total quality loss for multiple quality characteristics. Several studies have presented approaches addressing multiple quality characteristics. Most of these papers were concerned with maximizing the parameter combination of signal to noise (SN) ratios. The results reveal the advantages of this approach are that the optimal parameter design is the same as the traditional Taguchi method for the single quality characteristic; the optimal design maximizes the amount of reduction of total quality loss for multiple quality characteristics. This paper presents a literature review on solving multi-response problems in the Taguchi method and its successful implementation in various industries.

Amino Acid Enantiomeric Ratios in Biogeochemistry: Complications and Opportunities

NASA Astrophysics Data System (ADS)

McDonald, G. D.; Sun, H. J.; Tsapin, A. I.

2003-12-01

Amino acid enantiomeric ratios have been used for many years as an indicator of the process of racemization, and thus as a method to determine the age of biological samples such as bones, shells, and teeth. Dating biological samples by this method relies on an accurate knowledge of the environmental temperatures the sample has experienced, and the racemization kinetic parameters in the sample matrix. In some environments, where an independent dating method such as radiocarbon is available, the observed amino acid D/L ratios are found to be either higher or lower than those expected due to racemization alone. The observed D/L ratios in these cases can be clues to biogeochemical processes operating in addition to, or in place of, chemical racemization. In Siberian permafrost (Brinton et al. 2002, Astrobiology 2, 77) we have found D/L ratios lower than expected, which we have interpreted as evidence for low-level D-amino acid metabolism and recycling in microorganisms previously thought to be metabolically dormant. In microbially-colonized Antarctic Dry Valley sandstones (McDonald and Sun 2002, Eos Trans. AGU 83, Fall Meet. Suppl., Abstract B11A-0720) we have found D/L ratios higher than can be accounted for by racemization alone, most likely due to the accumulation of D-amino-acid-containing peptidoglycan material from multiple bacterial generations. D/L profiles in polar ices and in ice-covered lakes (Tsapin et al. 2002, Astrobiology 2, 632) can be used to indicate the sources and histories of water or ice samples. Multiple biological and biogeochemical processes may complicate the interpretation of amino acid enantiomeric excesses in both terrestrial and extraterrestrial samples; however, amino acid racemization remains a useful tool in biogeochemistry and astrobiology. With a good knowledge of the environmental history of samples, amino acid D/L profiles can be used as a window into processes such as molecular repair and biomass turnover that are difficult to detect by other means, particularly over geological time scales.

Stochastic control system parameter identifiability

NASA Technical Reports Server (NTRS)

Lee, C. H.; Herget, C. J.

1975-01-01

The parameter identification problem of general discrete time, nonlinear, multiple input/multiple output dynamic systems with Gaussian white distributed measurement errors is considered. The knowledge of the system parameterization was assumed to be known. Concepts of local parameter identifiability and local constrained maximum likelihood parameter identifiability were established. A set of sufficient conditions for the existence of a region of parameter identifiability was derived. A computation procedure employing interval arithmetic was provided for finding the regions of parameter identifiability. If the vector of the true parameters is locally constrained maximum likelihood (CML) identifiable, then with probability one, the vector of true parameters is a unique maximal point of the maximum likelihood function in the region of parameter identifiability and the constrained maximum likelihood estimation sequence will converge to the vector of true parameters.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example. PMID:23515190

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental disturbances, is also proposed, together with a simulation example.

Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.

Microbiome studies in the biological control of plant pathogens

USDA-ARS?s Scientific Manuscript database

Biological control of plant pathogens, although it has been a successful alternative that has allowed to select microorganisms for the generation of bioproducts and to understand multiple biological mechanisms, cannot be considered as a strategy defined only from the selection of a range of cultiva...

Sensitivity Analysis of Cf-252 (sf) Neutron and Gamma Observables in CGMF

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carter, Austin Lewis; Talou, Patrick; Stetcu, Ionel

CGMF is a Monte Carlo code that simulates the decay of primary fission fragments by emission of neutrons and gamma rays, according to the Hauser-Feshbach equations. As the CGMF code was recently integrated into the MCNP6.2 transport code, great emphasis has been placed on providing optimal parameters to CGMF such that many different observables are accurately represented. Of these observables, the prompt neutron spectrum, prompt neutron multiplicity, prompt gamma spectrum, and prompt gamma multiplicity are crucial for accurate transport simulations of criticality and nonproliferation applications. This contribution to the ongoing efforts to improve CGMF presents a study of the sensitivitymore » of various neutron and gamma observables to several input parameters for Californium-252 spontaneous fission. Among the most influential parameters are those that affect the input yield distributions in fragment mass and total kinetic energy (TKE). A new scheme for representing Y(A,TKE) was implemented in CGMF using three fission modes, S1, S2 and SL. The sensitivity profiles were calculated for 17 total parameters, which show that the neutron multiplicity distribution is strongly affected by the TKE distribution of the fragments. The total excitation energy (TXE) of the fragments is shared according to a parameter RT, which is defined as the ratio of the light to heavy initial temperatures. The sensitivity profile of the neutron multiplicity shows a second order effect of RT on the mean neutron multiplicity. A final sensitivity profile was produced for the parameter alpha, which affects the spin of the fragments. Higher values of alpha lead to higher fragment spins, which inhibit the emission of neutrons. Understanding the sensitivity of the prompt neutron and gamma observables to the many CGMF input parameters provides a platform for the optimization of these parameters.« less

Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Ricciuto, D. M.; Safta, C.; Debusschere, B.; Najm, H. N.; Thornton, P. E.

2015-12-01

For computationally expensive climate models, Monte-Carlo approaches of exploring the input parameter space are often prohibitive due to slow convergence with respect to ensemble size. To alleviate this, we build inexpensive surrogates using uncertainty quantification (UQ) methods employing Polynomial Chaos (PC) expansions that approximate the input-output relationships using as few model evaluations as possible. However, when many uncertain input parameters are present, such UQ studies suffer from the curse of dimensionality. In particular, for 50-100 input parameters non-adaptive PC representations have infeasible numbers of basis terms. To this end, we develop and employ Weighted Iterative Bayesian Compressive Sensing to learn the most important input parameter relationships for efficient, sparse PC surrogate construction with posterior uncertainty quantified due to insufficient data. Besides drastic dimensionality reduction, the uncertain surrogate can efficiently replace the model in computationally intensive studies such as forward uncertainty propagation and variance-based sensitivity analysis, as well as design optimization and parameter estimation using observational data. We applied the surrogate construction and variance-based uncertainty decomposition to Accelerated Climate Model for Energy (ACME) Land Model for several output QoIs at nearly 100 FLUXNET sites covering multiple plant functional types and climates, varying 65 input parameters over broad ranges of possible values. This work is supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research, Accelerated Climate Modeling for Energy (ACME) project. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.

PubMed

Peikon, Ian D; Fitzsimmons, Nathan A; Lebedev, Mikhail A; Nicolelis, Miguel A L

2009-06-15

Collection and analysis of limb kinematic data are essential components of the study of biological motion, including research into biomechanics, kinesiology, neurophysiology and brain-machine interfaces (BMIs). In particular, BMI research requires advanced, real-time systems capable of sampling limb kinematics with minimal contact to the subject's body. To answer this demand, we have developed an automated video tracking system for real-time tracking of multiple body parts in freely behaving primates. The system employs high-contrast markers painted on the animal's joints to continuously track the three-dimensional positions of their limbs during activity. Two-dimensional coordinates captured by each video camera are combined and converted to three-dimensional coordinates using a quadratic fitting algorithm. Real-time operation of the system is accomplished using direct memory access (DMA). The system tracks the markers at a rate of 52 frames per second (fps) in real-time and up to 100fps if video recordings are captured to be later analyzed off-line. The system has been tested in several BMI primate experiments, in which limb position was sampled simultaneously with chronic recordings of the extracellular activity of hundreds of cortical cells. During these recordings, multiple computational models were employed to extract a series of kinematic parameters from neuronal ensemble activity in real-time. The system operated reliably under these experimental conditions and was able to compensate for marker occlusions that occurred during natural movements. We propose that this system could also be extended to applications that include other classes of biological motion.

Telescopic multi-resolution augmented reality

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; Frenchi, Christopher; Szu, Harold

2014-05-01

To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as `make believe' entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

Laser biostimulation of patients suffering from multiple sclerosis in respect to the biological influence of laser light

NASA Astrophysics Data System (ADS)

Peszynski-Drews, Cezary; Klimek, Andrzej; Sopinski, Marek; Obrzejta, Dominik

2003-10-01

The authors discuss the results, obtained so far during three years' clinical examination, of laser therapy in the treatment of patients suffering from multiple sclerosis. They regard both the results of former laboratory experiments and so far discovered mechanisms of biological influence of laser light as an objective explanation of high effectiveness of laser therapy in the csae of this so far incurable disease. They discuss wide range of biological mechanisms of laser therapy, examined so far on different levels (cell, tissue, organ), allowing the explanation of beneficial influence of laser light in pathogenetically different morbidities.

A Comparison of Multiple Methods for Estimating Parasitemia of Hemogregarine Hemoparasites (Apicomplexa: Adeleorina) and Its Application for Studying Infection in Natural Populations

PubMed Central

Maia, João P.; Harris, D. James; Carranza, Salvador; Gómez-Díaz, Elena

2014-01-01

Identifying factors influencing infection patterns among hosts is critical for our understanding of the evolution and impact of parasitism in natural populations. However, the correct estimation of infection parameters depends on the performance of detection and quantification methods. In this study, we designed a quantitative PCR (qPCR) assay targeting the 18 S rRNA gene to estimate prevalence and intensity of Hepatozoon infection and compared its performance with microscopy and PCR. Using qPCR, we also compared various protocols that differ in the biological source and the extraction methods. Our results show that the qPCR approach on DNA extracted from blood samples, regardless of the extraction protocol, provided the most sensitive estimates of Hepatozoon infection parameters; while allowed us to differentiate between mixed infections of Adeleorinid (Hepatozoon) and Eimeriorinid (Schellackia and Lankesterella), based on the analysis of melting curves. We also show that tissue and saline methods can be used as low-cost alternatives in parasitological studies. The next step was to test our qPCR assay in a biological context, and for this purpose we investigated infection patterns between two sympatric lacertid species, which are naturally infected with apicomplexan hemoparasites, such as the genera Schellackia (Eimeriorina) and Hepatozoon (Adeleorina). From a biological standpoint, we found a positive correlation between Hepatozoon intensity of infection and host body size within each host species, being significantly higher in males, and higher in the smaller sized host species. These variations can be associated with a number of host intrinsic factors, like hormonal and immunological traits, that require further investigation. Our findings are relevant as they pinpoint the importance of accounting for methodological issues to better estimate infection in parasitological studies, and illustrate how between-host factors can influence parasite distributions in sympatric natural populations. PMID:24743340

Seasonal changes in the body size of two rotifer species living in activated sludge follow the Temperature-Size Rule.

PubMed

Kiełbasa, Anna; Walczyńska, Aleksandra; Fiałkowska, Edyta; Pajdak-Stós, Agnieszka; Kozłowski, Jan

2014-12-01

Temperature-Size Rule (TSR) is a phenotypic body size response of ectotherms to changing temperature. It is known from the laboratory studies, but seasonal patterns in the field were not studied so far. We examined the body size changes in time of rotifers inhabiting activated sludge. We hypothesize that temperature is the most influencing parameter in sludge environment, leading sludge rotifers to seasonally change their body size according to TSR, and that oxygen content also induces the size response. The presence of TSR in Lecane inermis rotifer was tested in a laboratory study with two temperature and two food-type treatments. The effect of interaction between temperature and food was significant; L. inermis followed TSR in one food type only. The seasonal variability in the body sizes of the rotifers L. inermis and Cephalodella gracilis was estimated by monthly sampling and analyzed by multiple regression, in relation to the sludge parameters selected as the most influential by multivariate analysis, and predicted to alter rotifer body size (temperature and oxygen). L. inermis varied significantly in size throughout the year, and this variability is explained by temperature as predicted by the TSR, but not by oxygen availability. C. gracilis also varied in size, though this variability was explained by both temperature and oxygen. We suggest that sludge age acts as a mortality factor in activated sludge. It may have a seasonal effect on the body size of L. inermis and modify a possible effect of oxygen. Activated sludge habitat is driven by both biological processes and human regulation, yet its resident organisms follow general evolutionary rule as they do in other biological systems. The interspecific response patterns differ, revealing the importance of taking species-specific properties into account. Our findings are applicable to sludge properties enhancement through optimizing the conditions for its biological component.

Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach.

PubMed

Peng, Huiming; Peng, Tao; Wen, Jianguo; Engler, David A; Matsunami, Risë K; Su, Jing; Zhang, Le; Chang, Chung-Che Jeff; Zhou, Xiaobo

2014-07-01

p38 mitogen-activated protein kinase activation plays an important role in resistance to chemotherapeutic cytotoxic drugs in treating multiple myeloma (MM). However, how the p38 mitogen-activated protein kinase signaling pathway is involved in drug resistance, in particular the roles that the various p38 isoforms play, remains largely unknown. To explore the underlying mechanisms, we developed a novel systems biology approach by integrating liquid chromatography-mass spectrometry and reverse phase protein array data from human MM cell lines with computational pathway models in which the unknown parameters were inferred using a proposed novel algorithm called modularized factor graph. New mechanisms predicted by our models suggest that combined activation of various p38 isoforms may result in drug resistance in MM via regulating the related pathways including extracellular signal-regulated kinase (ERK) pathway and NFкB pathway. ERK pathway regulating cell growth is synergistically regulated by p38δ isoform, whereas nuclear factor kappa B (NFкB) pathway regulating cell apoptosis is synergistically regulated by p38α isoform. This finding that p38δ isoform promotes the phosphorylation of ERK1/2 in MM cells treated with bortezomib was validated by western blotting. Based on the predicted mechanisms, we further screened drug combinations in silico and found that a promising drug combination targeting ERK1/2 and NFκB might reduce the effects of drug resistance in MM cells. This study provides a framework of a systems biology approach to studying drug resistance and drug combination selection. RPPA experimental Data and Matlab source codes of modularized factor graph for parameter estimation are freely available online at http://ctsb.is.wfubmc.edu/publications/modularized-factor-graph.php. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer.

PubMed

Khalid, Samra; Hanif, Rumeza; Tareen, Samar H K; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

2016-01-01

Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER- α ) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. In-silico based discrete and continuous modeling of ER- α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER- α , IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER- α , IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

A comparison of multiple methods for estimating parasitemia of hemogregarine hemoparasites (apicomplexa: adeleorina) and its application for studying infection in natural populations.

PubMed

Maia, João P; Harris, D James; Carranza, Salvador; Gómez-Díaz, Elena

2014-01-01

Identifying factors influencing infection patterns among hosts is critical for our understanding of the evolution and impact of parasitism in natural populations. However, the correct estimation of infection parameters depends on the performance of detection and quantification methods. In this study, we designed a quantitative PCR (qPCR) assay targeting the 18 S rRNA gene to estimate prevalence and intensity of Hepatozoon infection and compared its performance with microscopy and PCR. Using qPCR, we also compared various protocols that differ in the biological source and the extraction methods. Our results show that the qPCR approach on DNA extracted from blood samples, regardless of the extraction protocol, provided the most sensitive estimates of Hepatozoon infection parameters; while allowed us to differentiate between mixed infections of Adeleorinid (Hepatozoon) and Eimeriorinid (Schellackia and Lankesterella), based on the analysis of melting curves. We also show that tissue and saline methods can be used as low-cost alternatives in parasitological studies. The next step was to test our qPCR assay in a biological context, and for this purpose we investigated infection patterns between two sympatric lacertid species, which are naturally infected with apicomplexan hemoparasites, such as the genera Schellackia (Eimeriorina) and Hepatozoon (Adeleorina). From a biological standpoint, we found a positive correlation between Hepatozoon intensity of infection and host body size within each host species, being significantly higher in males, and higher in the smaller sized host species. These variations can be associated with a number of host intrinsic factors, like hormonal and immunological traits, that require further investigation. Our findings are relevant as they pinpoint the importance of accounting for methodological issues to better estimate infection in parasitological studies, and illustrate how between-host factors can influence parasite distributions in sympatric natural populations.

Timing of clamping and factors associated with iron stores in full-term newborns

PubMed Central

Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Martins, Mariana Campos; do Prado, Mara Rúbia Maciel Cardoso; Ribeiro, Andréia Queiroz; Sant’Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2014-01-01

OBJECTIVE To analyze the impact of timing of clamping and obstetric, biological and socioeconomic factors on the iron stores of full-term newborns. METHODS Cross-sectional study between October 2011 and July 2012 in which hematological parameters were evaluated for newborns in Viçosa, MG, Southeastern Brazil. It involved collecting 7 mL of umbilical cord blood from 144 full-term not underweight newborns. The parameters investigated were complete blood count, serum iron, ferritin and C-reactive protein. The time of umbilical cord clamping was measured using a digital timer without interfering in the procedures of childbirth. The birth data were collected from Live Birth Certificates and other information was obtained from the mother through a questionnaire applied in the first month postpartum. Analysis of multiple linear regression was then used to estimate the influence of biological, obstetrics and socioeconomic factors on the ferritin levels at birth. RESULTS The median ferritin was 130.3 µg/L (n = 129, minimum = 16.4; maximum = 420.5 µg/L), the mean serum iron was 137.9 μg/dL (n = 144, SD = 39.29) and mean hemoglobin was 14.7 g/dL (n = 144, SD = 1.47). The median time of cord clamping was 36 seconds, ranging between 7 and 100. The bivariate analysis detected an association between ferritin levels and color of the child, timing clamping of 60 seconds, type of delivery, the presence of gestational diabetes and per capita family income. In multivariate analysis, the variables per capita income, number of antenatal visits and length at birth accounted for 22.0% of variation in ferritin levels. CONCLUSIONS Iron stores at birth were influenced by biological, obstetric and social characteristics. Tackling anemia should involve creating policies aimed at reducing social inequalities, improving the quality of antenatal care, as well as implementing a criterion of delayed clamping of the umbilical cord within the guidelines of labor. PMID:24789632

[A complexity analysis of Chinese herbal property theory: the multiple expressions of herbal property].

PubMed

Jin, Rui; Zhang, Bing

2012-12-01

Chinese herbal property is the highly summarized concept of herbal nature and pharmaceutical effect, which reflect the characteristics of herbal actions on human body. These herbal actions, also interpreted as presenting the information about pharmaceutical effect contained in herbal property on the biological carrier, are defined as herbal property expressions. However, the biological expression of herbal property is believed to possess complex features for the involved complexity of Chinese medicine and organism. Firstly, there are multiple factors which could influence the expression results of herbal property such as the growth environment, harvest season and preparing methods of medicinal herbs, and physique and syndrome of body. Secondly, there are multiple biological approaches and biochemical indicators for the expression of the same property. This paper elaborated these complexities for further understanding of herbal property. The individuality of herbs and expression factors should be well analyzed in the related studies.

Systematic study of rapidity dispersion parameter in high energy nucleus-nucleus interactions

NASA Astrophysics Data System (ADS)

Bhattacharyya, Swarnapratim; Haiduc, Maria; Neagu, Alina Tania; Firu, Elena

2014-03-01

A systematic study of rapidity dispersion parameter as a quantitative measure of clustering of particles has been carried out in the interactions of 16O, 28Si and 32S projectiles at 4.5 A GeV/c with heavy (AgBr) and light (CNO) groups of targets present in the nuclear emulsion. For all the interactions, the total ensemble of events has been divided into four overlapping multiplicity classes depending on the number of shower particles. For all the interactions and for each multiplicity class, the rapidity dispersion parameter values indicate the occurrence of clusterization during the multiparticle production at Dubna energy. The measured rapidity dispersion parameter values are found to decrease with the increase of average multiplicity for all the interactions. The dependence of rapidity dispersion parameter on the average multiplicity can be successfully described by a relation D(η) = a + b + c2. The experimental results have been compared with the results obtained from the analysis of Monte Carlo simulated (MC-RAND) events. MC-RAND events show weaker clusterization among the pions in comparison to the experimental data.

A novel optogenetically tunable frequency modulating oscillator

PubMed Central

2018-01-01

Synthetic biology has enabled the creation of biological reconfigurable circuits, which perform multiple functions monopolizing a single biological machine; Such a system can switch between different behaviours in response to environmental cues. Previous work has demonstrated switchable dynamical behaviour employing reconfigurable logic gate genetic networks. Here we describe a computational framework for reconfigurable circuits in E.coli using combinations of logic gates, and also propose the biological implementation. The proposed system is an oscillator that can exhibit tunability of frequency and amplitude of oscillations. Further, the frequency of operation can be changed optogenetically. Insilico analysis revealed that two-component light systems, in response to light within a frequency range, can be used for modulating the frequency of the oscillator or stopping the oscillations altogether. Computational modelling reveals that mixing two colonies of E.coli oscillating at different frequencies generates spatial beat patterns. Further, we show that these oscillations more robustly respond to input perturbations compared to the base oscillator, to which the proposed oscillator is a modification. Compared to the base oscillator, the proposed system shows faster synchronization in a colony of cells for a larger region of the parameter space. Additionally, the proposed oscillator also exhibits lesser synchronization error in the transient period after input perturbations. This provides a strong basis for the construction of synthetic reconfigurable circuits in bacteria and other organisms, which can be scaled up to perform functions in the field of time dependent drug delivery with tunable dosages, and sets the stage for further development of circuits with synchronized population level behaviour. PMID:29389936

A novel optogenetically tunable frequency modulating oscillator.

PubMed

Mahajan, Tarun; Rai, Kshitij

2018-01-01

Synthetic biology has enabled the creation of biological reconfigurable circuits, which perform multiple functions monopolizing a single biological machine; Such a system can switch between different behaviours in response to environmental cues. Previous work has demonstrated switchable dynamical behaviour employing reconfigurable logic gate genetic networks. Here we describe a computational framework for reconfigurable circuits in E.coli using combinations of logic gates, and also propose the biological implementation. The proposed system is an oscillator that can exhibit tunability of frequency and amplitude of oscillations. Further, the frequency of operation can be changed optogenetically. Insilico analysis revealed that two-component light systems, in response to light within a frequency range, can be used for modulating the frequency of the oscillator or stopping the oscillations altogether. Computational modelling reveals that mixing two colonies of E.coli oscillating at different frequencies generates spatial beat patterns. Further, we show that these oscillations more robustly respond to input perturbations compared to the base oscillator, to which the proposed oscillator is a modification. Compared to the base oscillator, the proposed system shows faster synchronization in a colony of cells for a larger region of the parameter space. Additionally, the proposed oscillator also exhibits lesser synchronization error in the transient period after input perturbations. This provides a strong basis for the construction of synthetic reconfigurable circuits in bacteria and other organisms, which can be scaled up to perform functions in the field of time dependent drug delivery with tunable dosages, and sets the stage for further development of circuits with synchronized population level behaviour.

Efficient Ensemble State-Parameters Estimation Techniques in Ocean Ecosystem Models: Application to the North Atlantic

NASA Astrophysics Data System (ADS)

El Gharamti, M.; Bethke, I.; Tjiputra, J.; Bertino, L.

2016-02-01

Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors. Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem is often carried out by separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed. We test the performance of the new smoothing-based schemes against the standard EnKF in a one-dimensional configuration of the Norwegian Earth System Model (NorESM) in the North Atlantic. We use nutrients profile (up to 2000 m deep) data and surface partial CO2 measurements from Mike weather station (66o N, 2o E) to estimate different biological parameters of phytoplanktons and zooplanktons. We analyze the performance of the filters in terms of complexity and accuracy of the state and parameters estimates.

Dependence of the multiplicities of secondary particles on the impact parameter in collisions of high-energy neon and iron nuclei with photoemulsion nuclei

NASA Technical Reports Server (NTRS)

Dudkin, V. E.; Kovalev, E. E.; Nefedov, N. A.; Antonchik, V. A.; Bogdanov, S. D.; Kosmach, V. F.; Likhachev, A. YU.; Benton, E. V.; Crawford, H. J.

1995-01-01

A method is proposed for finding the dependence of mean multiplicities of secondaries on the nucleus-collision impact parameter from the data on the total interaction ensemble. The impact parameter has been shown to completely define the mean characteristics of an individual interaction event. A difference has been found between experimental results and the data calculated in terms of the cascade-evaporation model at impact-parameter values below 3 fm.

Define of internal recirculation coefficient for biological wastewater treatment in anoxic and aerobic bioreactors

NASA Astrophysics Data System (ADS)

Rossinskyi, Volodymyr

2018-02-01

The biological wastewater treatment technologies in anoxic and aerobic bioreactors with recycle of sludge mixture are used for the effective removal of organic compounds from wastewater. The change rate of sludge mixture recirculation between bioreactors leads to a change and redistribution of concentrations of organic compounds in sludge mixture in bioreactors and change hydrodynamic regimes in bioreactors. Determination of the coefficient of internal recirculation of sludge mixture between bioreactors is important for the choice of technological parameters of biological treatment (wastewater treatment duration in anoxic and aerobic bioreactors, flow capacity of recirculation pumps). Determination of the coefficient of internal recirculation of sludge mixture requires integrated consideration of hydrodynamic parameter (flow rate), kinetic parameter (rate of oxidation of organic compounds) and physical-chemical parameter of wastewater (concentration of organic compounds). The conducted numerical experiment from the proposed mathematical equations allowed to obtain analytical dependences of the coefficient of internal recirculation sludge mixture between bioreactors on the concentration of organic compounds in wastewater, the duration of wastewater treatment in bioreactors.

A Diagnostic Assessment for Introductory Molecular and Cell Biology

ERIC Educational Resources Information Center

Shi, Jia; Wood, William B.; Martin, Jennifer M.; Guild, Nancy A.; Vicens, Quentin; Knight, Jennifer K.

2010-01-01

We have developed and validated a tool for assessing understanding of a selection of fundamental concepts and basic knowledge in undergraduate introductory molecular and cell biology, focusing on areas in which students often have misconceptions. This multiple-choice Introductory Molecular and Cell Biology Assessment (IMCA) instrument is designed…

Biological auctions with multiple rewards

PubMed Central

Reiter, Johannes G.; Kanodia, Ayush; Gupta, Raghav; Nowak, Martin A.; Chatterjee, Krishnendu

2015-01-01

The competition for resources among cells, individuals or species is a fundamental characteristic of evolution. Biological all-pay auctions have been used to model situations where multiple individuals compete for a single resource. However, in many situations multiple resources with various values exist and single reward auctions are not applicable. We generalize the model to multiple rewards and study the evolution of strategies. In biological all-pay auctions the bid of an individual corresponds to its strategy and is equivalent to its payment in the auction. The decreasingly ordered rewards are distributed according to the decreasingly ordered bids of the participating individuals. The reproductive success of an individual is proportional to its fitness given by the sum of the rewards won minus its payments. Hence, successful bidding strategies spread in the population. We find that the results for the multiple reward case are very different from the single reward case. While the mixed strategy equilibrium in the single reward case with more than two players consists of mostly low-bidding individuals, we show that the equilibrium can convert to many high-bidding individuals and a few low-bidding individuals in the multiple reward case. Some reward values lead to a specialization among the individuals where one subpopulation competes for the rewards and the other subpopulation largely avoids costly competitions. Whether the mixed strategy equilibrium is an evolutionarily stable strategy (ESS) depends on the specific values of the rewards. PMID:26180069

Automatic classification of fluorescence and optical diffusion spectroscopy data in neuro-oncology

NASA Astrophysics Data System (ADS)

Savelieva, T. A.; Loshchenov, V. B.; Goryajnov, S. A.; Potapov, A. A.

2018-04-01

The complexity of the biological tissue spectroscopic analysis due to the overlap of biological molecules' absorption spectra, multiple scattering effect, as well as measurement geometry in vivo has caused the relevance of this work. In the neurooncology the problem of tumor boundaries delineation is especially acute and requires the development of new methods of intraoperative diagnosis. Methods of optical spectroscopy allow detecting various diagnostically significant parameters non-invasively. 5-ALA induced protoporphyrin IX is frequently used as fluorescent tumor marker in neurooncology. At the same time analysis of the concentration and the oxygenation level of haemoglobin and significant changes of light scattering in tumor tissues have a high diagnostic value. This paper presents an original method for the simultaneous registration of backward diffuse reflectance and fluorescence spectra, which allows defining all the parameters listed above simultaneously. The clinical studies involving 47 patients with intracranial glial tumors of II-IV Grades were carried out in N.N. Burdenko National Medical Research Center of Neurosurgery. To register the spectral dependences the spectroscopic system LESA- 01-BIOSPEC was used with specially developed w-shaped diagnostic fiber optic probe. The original algorithm of combined spectroscopic signal processing was developed. We have created a software and hardware, which allowed (as compared with the methods currently used in neurosurgical practice) to increase the sensitivity of intraoperative demarcation of intracranial tumors from 78% to 96%, specificity of 60% to 82%. The result of analysis of different techniques of automatic classification shows that in our case the most appropriate is the k Nearest Neighbors algorithm with cubic metrics.

gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development.

PubMed

Hosseini, Iraj; Gajjala, Anita; Bumbaca Yadav, Daniela; Sukumaran, Siddharth; Ramanujan, Saroja; Paxson, Ricardo; Gadkar, Kapil

2018-04-01

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology ® to execute various common PKPD analyses. The MATLAB ® -based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB ® -based open-source application and is publicly available to download from MATLAB ® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

Molecular Dynamics Simulations, Challenges and Opportunities: A Biologist's Prospective.

PubMed

Kumari, Indu; Sandhu, Padmani; Ahmed, Mushtaq; Akhter, Yusuf

2017-08-30

Molecular dynamics (MD) is a computational technique which is used to study biomolecules in virtual environment. Each of the constituent atoms represents a particle and hence the biomolecule embodies a multi-particle mechanical system analyzed within a simulation box during MD analysis. The potential energies of the atoms are explained by a mathematical expression consisting of different forces and space parameters. There are various software and force fields that have been developed for MD studies of the biomolecules. MD analysis has unravelled the various biological mechanisms (protein folding/unfolding, protein-small molecule interactions, protein-protein interactions, DNA/RNA-protein interactions, proteins embedded in membrane, lipid-lipid interactions, drug transport etc.) operating at the atomic and molecular levels. However, there are still some parameters including torsions in amino acids, carbohydrates (whose structure is extended and not well defined like that of proteins) and single stranded nucleic acids for which the force fields need further improvement, although there are several workers putting in constant efforts in these directions. The existing force fields are not efficient for studying the crowded environment inside the cells, since these interactions involve multiple factors in real time. Therefore, the improved force fields may provide the opportunities for their wider applications on the complex biosystems in diverse cellular conditions. In conclusion, the intervention of MD in the basic sciences involving interdisciplinary approaches will be helpful for understanding many fundamental biological and physiological processes at the molecular levels that may be further applied in various fields including biotechnology, fisheries, sustainable agriculture and biomedical research. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Rheological and molecular weight comparisons of approved hyaluronic acid products - preliminary standards for establishing class III medical device equivalence.

PubMed

Braithwaite, Gavin J C; Daley, Michael J; Toledo-Velasquez, David

2016-01-01

Hyaluronic acid of various molecular weights has been in use for the treatment of osteoarthritis knee pain for decades. Worldwide, these products are regulated as either as drugs or devices and in some countries as both. In the US, this class of products is regulated as Class III medical devices, which places specific regulatory requirements on developers of these materials under a Pre-Market Approval process, typically requiring data from prospective randomized controlled clinical studies. In 1984 pharmaceutical manufacturers became able to file an Abbreviated New Drug Application for approval of a generic drug, thus establishing standards for demonstrating equivalence to an existing chemical entity. Recently, the first biosimilar, or 'generic biologic', was approved. Biosimilars are biological products that are approved by the FDA because they are 'highly similar' to a reference product, and have been shown to have no clinically meaningful differences from the reference product. For devices, Class II medical devices have a pathway for declaring equivalence to an existing product by filing a 510 k application for FDA clearance. However, until recently no equivalent regulatory pathway was available to Class III devices. In this paper, we consider the critical mechanical performance parameters for intra-articular hyaluronic products to demonstrate indistinguishable characteristics. Analogous to the aforementioned pathways that allow for a demonstration of equivalence, we examine these parameters for an existing, marketed device and compare molecular weight and rheological properties of multiple batches of a similar product. We propose that this establishes a scientific rationale for establishing Class III medical device equivalence.

Statistical analysis of polarization interference images of biological fluids polycrystalline films in the tasks of optical anisotropy weak changes differentiation

NASA Astrophysics Data System (ADS)

Ushenko, Yu. O.; Dubolazov, O. V.; Ushenko, V. O.; Zhytaryuk, V. G.; Prydiy, O. G.; Pavlyukovich, N.; Pavlyukovich, O.

2018-01-01

In this paper, we present the results of a statistical analysis of polarization-interference images of optically thin histological sections of biological tissues and polycrystalline films of biological fluids of human organs. A new analytical parameter is introduced-the local contrast of the interference pattern in the plane of a polarizationinhomogeneous microscopic image of a biological preparation. The coordinate distributions of the given parameter and the sets of statistical moments of the first-fourth order that characterize these distributions are determined. On this basis, the differentiation of degenerative-dystrophic changes in the myocardium and the polycrystalline structure of the synovial fluid of the human knee with different pathologies is realized.

Allostatic load and biological anthropology.

PubMed

Edes, Ashley N; Crews, Douglas E

2017-01-01

Multiple stressors affect developing and adult organisms, thereby partly structuring their phenotypes. Determining how stressors influence health, well-being, and longevity in human and nonhuman primate populations are major foci within biological anthropology. Although much effort has been devoted to examining responses to multiple environmental and sociocultural stressors, no holistic metric to measure stress-related physiological dysfunction has been widely applied within biological anthropology. Researchers from disciplines outside anthropology are using allostatic load indices (ALIs) to estimate such dysregulation and examine life-long outcomes of stressor exposures, including morbidity and mortality. Following allostasis theory, allostatic load represents accumulated physiological and somatic damage secondary to stressors and senescent processes experienced over the lifespan. ALIs estimate this wear-and-tear using a composite of biomarkers representing neuroendocrine, cardiovascular, metabolic, and immune systems. Across samples, ALIs are associated significantly with multiple individual characteristics (e.g., age, sex, education, DNA variation) of interest within biological anthropology. They also predict future outcomes, including aspects of life history variation (e.g., survival, lifespan), mental and physical health, morbidity and mortality, and likely health disparities between groups, by stressor exposures, ethnicity, occupations, and degree of departure from local indigenous life ways and integration into external and commodified ones. ALIs also may be applied to similar stress-related research areas among nonhuman primates. Given the reports from multiple research endeavors, here we propose ALIs may be useful for assessing stressors, stress responses, and stress-related dysfunction, current and long-term cognitive function, health and well-being, and risk of early mortality across many research programs within biological anthropology. © 2017 American Association of Physical Anthropologists.

A high-throughput screening approach to discovering good forms of biologically inspired visual representation.

PubMed

Pinto, Nicolas; Doukhan, David; DiCarlo, James J; Cox, David D

2009-11-01

While many models of biological object recognition share a common set of "broad-stroke" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model--e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct "parts" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.

A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

PubMed Central

Pinto, Nicolas; Doukhan, David; DiCarlo, James J.; Cox, David D.

2009-01-01

While many models of biological object recognition share a common set of “broad-stroke” properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model—e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct “parts” have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end NVIDIA graphic cards and the PlayStation 3's IBM Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision. PMID:19956750

Development of a multiple-parameter nonlinear perturbation procedure for transonic turbomachinery flows: Preliminary application to design/optimization problems

NASA Technical Reports Server (NTRS)

Stahara, S. S.; Elliott, J. P.; Spreiter, J. R.

1983-01-01

An investigation was conducted to continue the development of perturbation procedures and associated computational codes for rapidly determining approximations to nonlinear flow solutions, with the purpose of establishing a method for minimizing computational requirements associated with parametric design studies of transonic flows in turbomachines. The results reported here concern the extension of the previously developed successful method for single parameter perturbations to simultaneous multiple-parameter perturbations, and the preliminary application of the multiple-parameter procedure in combination with an optimization method to blade design/optimization problem. In order to provide as severe a test as possible of the method, attention is focused in particular on transonic flows which are highly supercritical. Flows past both isolated blades and compressor cascades, involving simultaneous changes in both flow and geometric parameters, are considered. Comparisons with the corresponding exact nonlinear solutions display remarkable accuracy and range of validity, in direct correspondence with previous results for single-parameter perturbations.

Fast dictionary generation and searching for magnetic resonance fingerprinting.

PubMed

Jun Xie; Mengye Lyu; Jian Zhang; Hui, Edward S; Wu, Ed X; Ze Wang

2017-07-01

A super-fast dictionary generation and searching (DGS) algorithm was developed for MR parameter quantification using magnetic resonance fingerprinting (MRF). MRF is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But it has a multiplicative computation complexity, resulting in a big burden of dictionary generating, saving, and retrieving, which can easily be intractable for any state-of-art computers. Based on retrospective analysis of the dictionary matching object function, a multi-scale ZOOM like DGS algorithm, dubbed as MRF-ZOOM, was proposed. MRF ZOOM is quasi-parameter-separable so the multiplicative computation complexity is broken into additive one. Evaluations showed that MRF ZOOM was hundreds or thousands of times faster than the original MRF parameter quantification method even without counting the dictionary generation time in. Using real data, it yielded nearly the same results as produced by the original method. MRF ZOOM provides a super-fast solution for MR parameter quantification.

Accurate determination of electronic transport properties of silicon wafers by nonlinear photocarrier radiometry with multiple pump beam sizes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qian; University of the Chinese Academy of Sciences, Beijing 100039; Li, Bincheng, E-mail: bcli@uestc.ac.cn

2015-12-07

In this paper, photocarrier radiometry (PCR) technique with multiple pump beam sizes is employed to determine simultaneously the electronic transport parameters (the carrier lifetime, the carrier diffusion coefficient, and the front surface recombination velocity) of silicon wafers. By employing the multiple pump beam sizes, the influence of instrumental frequency response on the multi-parameter estimation is totally eliminated. A nonlinear PCR model is developed to interpret the PCR signal. Theoretical simulations are performed to investigate the uncertainties of the estimated parameter values by investigating the dependence of a mean square variance on the corresponding transport parameters and compared to that obtainedmore » by the conventional frequency-scan method, in which only the frequency dependences of the PCR amplitude and phase are recorded at single pump beam size. Simulation results show that the proposed multiple-pump-beam-size method can improve significantly the accuracy of the determination of the electronic transport parameters. Comparative experiments with a p-type silicon wafer with resistivity 0.1–0.2 Ω·cm are performed, and the electronic transport properties are determined simultaneously. The estimated uncertainties of the carrier lifetime, diffusion coefficient, and front surface recombination velocity are approximately ±10.7%, ±8.6%, and ±35.4% by the proposed multiple-pump-beam-size method, which is much improved than ±15.9%, ±29.1%, and >±50% by the conventional frequency-scan method. The transport parameters determined by the proposed multiple-pump-beam-size PCR method are in good agreement with that obtained by a steady-state PCR imaging technique.« less

Terrestrial reproduction as an adaptation to steep terrain in African toads

PubMed Central

Müller, Hendrik; Hafner, Julian; Penner, Johannes; Mazuch, Tomáš; Rödel, Mark-Oliver; Loader, Simon P.

2017-01-01

How evolutionary novelties evolve is a major question in evolutionary biology. It is widely accepted that changes in environmental conditions shift the position of selective optima, and advancements in phylogenetic comparative approaches allow the rigorous testing of such correlated transitions. A longstanding question in vertebrate biology has been the evolution of terrestrial life histories in amphibians and here, by investigating African bufonids, we test whether terrestrial modes of reproduction have evolved as adaptations to particular abiotic habitat parameters. We reconstruct and date the most complete species-level molecular phylogeny and estimate ancestral states for reproductive modes. By correlating continuous habitat measurements from remote sensing data and locality records with life-history transitions, we discover that terrestrial modes of reproduction, including viviparity evolved multiple times in this group, most often directly from fully aquatic modes. Terrestrial modes of reproduction are strongly correlated with steep terrain and low availability of accumulated water sources. Evolutionary transitions to terrestrial modes of reproduction occurred synchronously with or after transitions in habitat, and we, therefore, interpret terrestrial breeding as an adaptation to these abiotic conditions, rather than an exaptation that facilitated the colonization of montane habitats. PMID:28356450

Design and implementation of an automated compound management system in support of lead optimization.

PubMed

Quintero, Catherine; Kariv, Ilona

2009-06-01

To meet the needs of the increasingly rapid and parallelized lead optimization process, a fully integrated local compound storage and liquid handling system was designed and implemented to automate the generation of assay-ready plates directly from newly submitted and cherry-picked compounds. A key feature of the system is the ability to create project- or assay-specific compound-handling methods, which provide flexibility for any combination of plate types, layouts, and plate bar-codes. Project-specific workflows can be created by linking methods for processing new and cherry-picked compounds and control additions to produce a complete compound set for both biological testing and local storage in one uninterrupted workflow. A flexible cherry-pick approach allows for multiple, user-defined strategies to select the most appropriate replicate of a compound for retesting. Examples of custom selection parameters include available volume, compound batch, and number of freeze/thaw cycles. This adaptable and integrated combination of software and hardware provides a basis for reducing cycle time, fully automating compound processing, and ultimately increasing the rate at which accurate, biologically relevant results can be produced for compounds of interest in the lead optimization process.

Using Bioinformatics Approach to Explore the Pharmacological Mechanisms of Multiple Ingredients in Shuang-Huang-Lian

PubMed Central

Zhang, Bai-xia; Li, Jian; Gu, Hao; Li, Qiang; Zhang, Qi; Zhang, Tian-jiao; Wang, Yun; Cai, Cheng-ke

2015-01-01

Due to the proved clinical efficacy, Shuang-Huang-Lian (SHL) has developed a variety of dosage forms. However, the in-depth research on targets and pharmacological mechanisms of SHL preparations was scarce. In the presented study, the bioinformatics approaches were adopted to integrate relevant data and biological information. As a result, a PPI network was built and the common topological parameters were characterized. The results suggested that the PPI network of SHL exhibited a scale-free property and modular architecture. The drug target network of SHL was structured with 21 functional modules. According to certain modules and pharmacological effects distribution, an antitumor effect and potential drug targets were predicted. A biological network which contained 26 subnetworks was constructed to elucidate the antipneumonia mechanism of SHL. We also extracted the subnetwork to explicitly display the pathway where one effective component acts on the pneumonia related targets. In conclusions, a bioinformatics approach was established for exploring the drug targets, pharmacological activity distribution, effective components of SHL, and its mechanism of antipneumonia. Above all, we identified the effective components and disclosed the mechanism of SHL from the view of system. PMID:26495421

Terrestrial reproduction as an adaptation to steep terrain in African toads.

PubMed

Liedtke, H Christoph; Müller, Hendrik; Hafner, Julian; Penner, Johannes; Gower, David J; Mazuch, Tomáš; Rödel, Mark-Oliver; Loader, Simon P

2017-03-29

How evolutionary novelties evolve is a major question in evolutionary biology. It is widely accepted that changes in environmental conditions shift the position of selective optima, and advancements in phylogenetic comparative approaches allow the rigorous testing of such correlated transitions. A longstanding question in vertebrate biology has been the evolution of terrestrial life histories in amphibians and here, by investigating African bufonids, we test whether terrestrial modes of reproduction have evolved as adaptations to particular abiotic habitat parameters. We reconstruct and date the most complete species-level molecular phylogeny and estimate ancestral states for reproductive modes. By correlating continuous habitat measurements from remote sensing data and locality records with life-history transitions, we discover that terrestrial modes of reproduction, including viviparity evolved multiple times in this group, most often directly from fully aquatic modes. Terrestrial modes of reproduction are strongly correlated with steep terrain and low availability of accumulated water sources. Evolutionary transitions to terrestrial modes of reproduction occurred synchronously with or after transitions in habitat, and we, therefore, interpret terrestrial breeding as an adaptation to these abiotic conditions, rather than an exaptation that facilitated the colonization of montane habitats. © 2017 The Author(s).

Exploring the Factors Related to Acceptance of Evolutionary Theory among Turkish Preservice Biology Teachers: Toward a More Informative Conceptual Ecology for Biological Evolution

ERIC Educational Resources Information Center

Deniz, Hasan; Donnelly, Lisa A.; Yilmaz, Irfan

2008-01-01

In this study, using multiple regression analysis, we aimed to explore the factors related to acceptance of evolutionary theory among preservice Turkish biology teachers using conceptual ecology for biological evolution as a theoretical lens. We aimed to determine the extent to which we can account for the variance in acceptance of evolutionary…

Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology through Constructing and Testing Computational Theories--An Embodied Modeling Approach

ERIC Educational Resources Information Center

Wilensky, Uri; Reisman, Kenneth

2006-01-01

Biological phenomena can be investigated at multiple levels, from the molecular to the cellular to the organismic to the ecological. In typical biology instruction, these levels have been segregated. Yet, it is by examining the connections between such levels that many phenomena in biology, and complex systems in general, are best explained. We…

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

Case studies on ESA-doping as revealed by the Biological Passport.

PubMed

Zorzoli, Mario; Rossi, Francesca

2012-11-01

Blood doping, through the increase of red cells, induces changes of hematological parameters. The aim of the Biological Passport is first to analyse individual longitudinal profiles in order to identify, through variations of the specific parameters, doping manipulations. Additionally, on the basis of abnormal values or profiles, athletes can be targeted for traditional anti-doping tests in order to detect forbidden substances or methods. We report the experience of the International Cycling Union in applying the Biological Passport to target athletes for the presence of erythropoiesis stimulating agents. All positive results which have been reported between 2008 and 2010 concerning athletes enrolled in the Biological Passport program are presented. Four cases are discussed more in details. To conclude, we propose possible ways of using the Biological Passport in order to better understand athletes' doping modalities, so that testing programs efficiency can be improved. Copyright © 2012 John Wiley & Sons, Ltd.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

PubMed

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

Elastic membranes in confinement.

PubMed

Bostwick, J B; Miksis, M J; Davis, S H

2016-07-01

An elastic membrane stretched between two walls takes a shape defined by its length and the volume of fluid it encloses. Many biological structures, such as cells, mitochondria and coiled DNA, have fine internal structure in which a membrane (or elastic member) is geometrically 'confined' by another object. Here, the two-dimensional shape of an elastic membrane in a 'confining' box is studied by introducing a repulsive confinement pressure that prevents the membrane from intersecting the wall. The stage is set by contrasting confined and unconfined solutions. Continuation methods are then used to compute response diagrams, from which we identify the particular membrane mechanics that generate mitochondria-like shapes. Large confinement pressures yield complex response diagrams with secondary bifurcations and multiple turning points where modal identities may change. Regions in parameter space where such behaviour occurs are then mapped. © 2016 The Author(s).

Spirulina-Templated Metal Microcoils with Controlled Helical Structures for THz Electromagnetic Responses

PubMed Central

Kamata, Kaori; Piao, Zhenzi; Suzuki, Soichiro; Fujimori, Takahiro; Tajiri, Wataru; Nagai, Keiji; Iyoda, Tomokazu; Yamada, Atsushi; Hayakawa, Toshiaki; Ishiwara, Mitsuteru; Horaguchi, Satoshi; Belay, Amha; Tanaka, Takuo; Takano, Keisuke; Hangyo, Masanori

2014-01-01

Microstructures in nature are ultrafine and ordered in biological roles, which have attracted material scientists. Spirulina forms three-dimensional helical microstructure, one of remarkable features in nature beyond our current processing technology such as lithography in terms of mass-productivity and structural multiplicity. Spirulina varies its diameter, helical pitch, and/or length against growing environment. This unique helix is suggestive of a tiny electromagnetic coil, if composed of electro-conductive metal, which brought us main concept of this work. Here, we describe the biotemplating process onto Spirulina surface to fabricate metal microcoils. Structural parameters of the microcoil can be controlled by the cultivation conditions of Spirulina template and also purely one-handed microcoil can be fabricated. A microcoil dispersion sheet exhibited optically active response attributed to structural resonance in terahertz-wave region. PMID:24815190

Simple and versatile long range swept source for optical coherence tomography applications

NASA Astrophysics Data System (ADS)

Bräuer, Bastian; Lippok, Norman; Murdoch, Stuart G.; Vanholsbeeck, Frédérique

2015-12-01

We present a versatile long coherence length swept-source laser design for optical coherence tomography applications. This design consists of a polygonal spinning mirror and an optical gain chip in a modified Littman-Metcalf cavity. A narrowband intra-cavity filter is implemented through multiple passes off a diffraction grating set at grazing incidence. The key advantage of this design is that it can be readily adapted to any wavelength regions for which broadband gain chips are available. We demonstrate this by implementing sources at 1650 nm, 1550 nm, 1310 nm and 1050 nm. In particular, we present a 1310 nm swept source laser with 24 mm coherence length, 95 nm optical bandwidth, 2 kHz maximum sweep frequency and 7.5 mW average output power. These parameters make it a suitable source for the imaging of biological samples.

Chemical Fucosylation of a Polysaccharide: A Semisynthetic Access to Fucosylated Chondroitin Sulfate.

PubMed

Laezza, Antonio; Iadonisi, Alfonso; Castro, Cristina De; De Rosa, Mario; Schiraldi, Chiara; Parrilli, Michelangelo; Bedini, Emiliano

2015-07-13

Chemical O-glycosylation of polysaccharides is an almost unexplored reaction. This is mainly due to the difficulties in derivatizing such complex biomacromolecules in a quantitative manner and with a fine control of the obtained structural parameters. In this work, chondroitin raw material from a microbial source was chemo- and regioselectively protected to give two polysaccharide intermediates, that acted in turn as glycosyl acceptors in fucosylation reactions. Further manipulations on the fucosylated polysaccharides, including multiple de-O-benzylation and sulfation, furnished for the first time nonanimal sourced fucosylated chondroitin sulfates (fCSs)-polysaccharides obtained so far exclusively from sea cucumbers (Echinoidea, Holothuroidea) and showing several very interesting biological activities. A semisynthetic fCS was characterized from a structural point of view by means of 2D-NMR techniques, and preliminarily assayed in an anticoagulant test.

A Theoretical Approach to Analyze the Parametric Influence on Spatial Patterns of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) Populations.

PubMed

Garcia, A G; Godoy, W A C

2017-06-01

Studies of the influence of biological parameters on the spatial distribution of lepidopteran insects can provide useful information for managing agricultural pests, since the larvae of many species cause serious impacts on crops. Computational models to simulate the spatial dynamics of insect populations are increasingly used, because of their efficiency in representing insect movement. In this study, we used a cellular automata model to explore different patterns of population distribution of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), when the values of two biological parameters that are able to influence the spatial pattern (larval viability and adult longevity) are varied. We mapped the spatial patterns observed as the parameters varied. Additionally, by using population data for S. frugiperda obtained in different hosts under laboratory conditions, we were able to describe the expected spatial patterns occurring in corn, cotton, millet, and soybean crops based on the parameters varied. The results are discussed from the perspective of insect ecology and pest management. We concluded that computational approaches can be important tools to study the relationship between the biological parameters and spatial distributions of lepidopteran insect pests.

Longitudinal multiple imputation approaches for body mass index or other variables with very low individual-level variability: the mibmi command in Stata.

PubMed

Kontopantelis, Evangelos; Parisi, Rosa; Springate, David A; Reeves, David

2017-01-13

In modern health care systems, the computerization of all aspects of clinical care has led to the development of large data repositories. For example, in the UK, large primary care databases hold millions of electronic medical records, with detailed information on diagnoses, treatments, outcomes and consultations. Careful analyses of these observational datasets of routinely collected data can complement evidence from clinical trials or even answer research questions that cannot been addressed in an experimental setting. However, 'missingness' is a common problem for routinely collected data, especially for biological parameters over time. Absence of complete data for the whole of a individual's study period is a potential bias risk and standard complete-case approaches may lead to biased estimates. However, the structure of the data values makes standard cross-sectional multiple-imputation approaches unsuitable. In this paper we propose and evaluate mibmi, a new command for cleaning and imputing longitudinal body mass index data. The regression-based data cleaning aspects of the algorithm can be useful when researchers analyze messy longitudinal data. Although the multiple imputation algorithm is computationally expensive, it performed similarly or even better to existing alternatives, when interpolating observations. The mibmi algorithm can be a useful tool for analyzing longitudinal body mass index data, or other longitudinal data with very low individual-level variability.

A Multispecies Framework for Landscape Conservation Planning

USGS Publications Warehouse

Schwenk, W.S.; Donovan, T.M.

2011-01-01

Rapidly changing landscapes have spurred the need for quantitative methods for conservation assessment and planning that encompass large spatial extents. We devised and tested a multispecies framework for conservation planning to complement single-species assessments and ecosystem-level approaches. Our framework consisted of 4 elements: sampling to effectively estimate population parameters, measuring how human activity affects landscapes at multiple scales, analyzing the relation between landscape characteristics and individual species occurrences, and evaluating and comparing the responses of multiple species to landscape modification. We applied the approach to a community of terrestrial birds across 25,000 km2 with a range of intensities of human development. Human modification of land cover, road density, and other elements of the landscape, measured at multiple spatial extents, had large effects on occupancy of the 67 species studied. Forest composition within 1 km of points had a strong effect on occupancy of many species and a range of negative, intermediate, and positive associations. Road density within 1 km of points, percent evergreen forest within 300 m, and distance from patch edge were also strongly associated with occupancy for many species. We used the occupancy results to group species into 11 guilds that shared patterns of association with landscape characteristics. Our multispecies approach to conservation planning allowed us to quantify the trade-offs of different scenarios of land-cover change in terms of species occupancy. ?? 2011 Society for Conservation Biology.

Development of a Biological Science Quantitative Reasoning Exam (BioSQuaRE)

ERIC Educational Resources Information Center

Stanhope, Liz; Ziegler, Laura; Haque, Tabassum; Le, Laura; Vinces, Marcelo; Davis, Gregory K.; Zieffler, Andrew; Brodfuehrer, Peter; Preest, Marion; Belitsky, Jason M.; Umbanhowar, Charles, Jr.; Overvoorde, Paul J.

2017-01-01

Multiple reports highlight the increasingly quantitative nature of biological research and the need to innovate means to ensure that students acquire quantitative skills. We present a tool to support such innovation. The Biological Science Quantitative Reasoning Exam (BioSQuaRE) is an assessment instrument designed to measure the quantitative…

Interdisciplinary Lessons for the Teaching of Biology from the Practice of Evo-Devo

ERIC Educational Resources Information Center

Love, Alan C.

2013-01-01

Evolutionary developmental biology (Evo-devo) is a vibrant area of contemporary life science that should be (and is) increasingly incorporated into teaching curricula. Although the inclusion of this content is important for biological pedagogy at multiple levels of instruction, there are also philosophical lessons that can be drawn from the…

Multiple frequency method for operating electrochemical sensors

DOEpatents

Martin, Louis P [San Ramon, CA

2012-05-15

A multiple frequency method for the operation of a sensor to measure a parameter of interest using calibration information including the steps of exciting the sensor at a first frequency providing a first sensor response, exciting the sensor at a second frequency providing a second sensor response, using the second sensor response at the second frequency and the calibration information to produce a calculated concentration of the interfering parameters, using the first sensor response at the first frequency, the calculated concentration of the interfering parameters, and the calibration information to measure the parameter of interest.

Achieving Consistent Multiple Daily Low-Dose Bacillus anthracis Spore Inhalation Exposures in the Rabbit Model

PubMed Central

Barnewall, Roy E.; Comer, Jason E.; Miller, Brian D.; Gutting, Bradford W.; Wolfe, Daniel N.; Director-Myska, Alison E.; Nichols, Tonya L.; Taft, Sarah C.

2012-01-01

Repeated low-level exposures to biological agents could occur before or after the remediation of an environmental release. This is especially true for persistent agents such as B. anthracis spores, the causative agent of anthrax. Studies were conducted to examine aerosol methods needed for consistent daily low aerosol concentrations to deliver a low-dose (less than 106 colony forming units (CFU) of B. anthracis spores) and included a pilot feasibility characterization study, acute exposure study, and a multiple 15 day exposure study. This manuscript focuses on the state-of-the-science aerosol methodologies used to generate and aerosolize consistent daily low aerosol concentrations and resultant low inhalation doses to rabbits. The pilot feasibility characterization study determined that the aerosol system was consistent and capable of producing very low aerosol concentrations. In the acute, single day exposure experiment, targeted inhaled doses of 1 × 102, 1 × 103, 1 × 104, and 1 × 105 CFU were used. In the multiple daily exposure experiment, rabbits were exposed multiple days to targeted inhaled doses of 1 × 102, 1 × 103, and 1 × 104 CFU. In all studies, targeted inhaled doses remained consistent from rabbit-to-rabbit and day-to-day. The aerosol system produced aerosolized spores within the optimal mass median aerodynamic diameter particle size range to reach deep lung alveoli. Consistency of the inhaled dose was aided by monitoring and recording respiratory parameters during the exposure with real-time plethysmography. Overall, the presented results show that the animal aerosol system was stable and highly reproducible between different studies and over multiple exposure days. PMID:22919662

Development of a Biological Science Quantitative Reasoning Exam (BioSQuaRE)

PubMed Central

Stanhope, Liz; Ziegler, Laura; Haque, Tabassum; Le, Laura; Vinces, Marcelo; Davis, Gregory K.; Zieffler, Andrew; Brodfuehrer, Peter; Preest, Marion; M. Belitsky, Jason; Umbanhowar, Charles; Overvoorde, Paul J.

2017-01-01

Multiple reports highlight the increasingly quantitative nature of biological research and the need to innovate means to ensure that students acquire quantitative skills. We present a tool to support such innovation. The Biological Science Quantitative Reasoning Exam (BioSQuaRE) is an assessment instrument designed to measure the quantitative skills of undergraduate students within a biological context. The instrument was developed by an interdisciplinary team of educators and aligns with skills included in national reports such as BIO2010, Scientific Foundations for Future Physicians, and Vision and Change. Undergraduate biology educators also confirmed the importance of items included in the instrument. The current version of the BioSQuaRE was developed through an iterative process using data from students at 12 postsecondary institutions. A psychometric analysis of these data provides multiple lines of evidence for the validity of inferences made using the instrument. Our results suggest that the BioSQuaRE will prove useful to faculty and departments interested in helping students acquire the quantitative competencies they need to successfully pursue biology, and useful to biology students by communicating the importance of quantitative skills. We invite educators to use the BioSQuaRE at their own institutions. PMID:29196427

Minimizing energy dissipation of matrix multiplication kernel on Virtex-II

NASA Astrophysics Data System (ADS)

Choi, Seonil; Prasanna, Viktor K.; Jang, Ju-wook

2002-07-01

In this paper, we develop energy-efficient designs for matrix multiplication on FPGAs. To analyze the energy dissipation, we develop a high-level model using domain-specific modeling techniques. In this model, we identify architecture parameters that significantly affect the total energy (system-wide energy) dissipation. Then, we explore design trade-offs by varying these parameters to minimize the system-wide energy. For matrix multiplication, we consider a uniprocessor architecture and a linear array architecture to develop energy-efficient designs. For the uniprocessor architecture, the cache size is a parameter that affects the I/O complexity and the system-wide energy. For the linear array architecture, the amount of storage per processing element is a parameter affecting the system-wide energy. By using maximum amount of storage per processing element and minimum number of multipliers, we obtain a design that minimizes the system-wide energy. We develop several energy-efficient designs for matrix multiplication. For example, for 6×6 matrix multiplication, energy savings of upto 52% for the uniprocessor architecture and 36% for the linear arrary architecture is achieved over an optimized library for Virtex-II FPGA from Xilinx.

Fluorescent probes for the simultaneous detection of multiple analytes in biology.

PubMed

Kolanowski, Jacek L; Liu, Fei; New, Elizabeth J

2018-01-02

Many of the key questions facing cellular biology concern the location and concentration of chemical species, from signalling molecules to metabolites to exogenous toxins. Fluorescent sensors (probes) have revolutionised the understanding of biological systems through their exquisite sensitivity to specific analytes. Probe design has focussed on selective sensors for individual analytes, but many of the most pertinent biological questions are related to the interaction of more than one chemical species. While it is possible to simultaneously use multiple sensors for such applications, data interpretation will be confounded by the fact that sensors will have different uptake, localisation and metabolism profiles. An alternative solution is to instead use a single probe that responds to two analytes, termed a dual-responsive probe. Recent progress in this field has yielded exciting probes, some of which have demonstrated biological application. Here we review work that has been carried out to date, and suggest future research directions that will harness the considerable potential of dual-responsive fluorescent probes.

Versatile and on-demand biologics co-production in yeast.

PubMed

Cao, Jicong; Perez-Pinera, Pablo; Lowenhaupt, Ky; Wu, Ming-Ru; Purcell, Oliver; de la Fuente-Nunez, Cesar; Lu, Timothy K

2018-01-08

Current limitations to on-demand drug manufacturing can be addressed by technologies that streamline manufacturing processes. Combining the production of two or more drugs into a single batch could not only be useful for research, clinical studies, and urgent therapies but also effective when combination therapies are needed or where resources are scarce. Here we propose strategies to concurrently produce multiple biologics from yeast in single batches by multiplexing strain development, cell culture, separation, and purification. We demonstrate proof-of-concept for three biologics co-production strategies: (i) inducible expression of multiple biologics and control over the ratio between biologic drugs produced together; (ii) consolidated bioprocessing; and (iii) co-expression and co-purification of a mixture of two monoclonal antibodies. We then use these basic strategies to produce drug mixtures as well as to separate drugs. These strategies offer a diverse array of options for on-demand, flexible, low-cost, and decentralized biomanufacturing applications without the need for specialized equipment.

Strategies for Controlled Delivery of Biologics for Cartilage Repair

PubMed Central

Lam, Johnny; Lu, Steven; Kasper, F. Kurtis; Mikos, Antonios G.

2014-01-01

The delivery of biologics is an important component in the treatment of osteoarthritis and the functional restoration of articular cartilage. Numerous factors have been implicated in the cartilage repair process, but the uncontrolled delivery of these factors may not only reduce their full reparative potential and can also cause unwanted morphological effects. It is therefore imperative to consider the type of biologic to be delivered, the method of delivery, and the temporal as well as spatial presentation of the biologic to achieve the desired effect in cartilage repair. Additionally, the delivery of a single factor may not be sufficient in guiding neo-tissue formation, motivating recent research towards the delivery of multiple factors. This review will discuss the roles of various biologics involved in cartilage repair and the different methods of delivery for appropriate healing responses. A number of spatiotemporal strategies will then be emphasized for the controlled delivery of single and multiple bioactive factors in both in vitro and in vivo cartilage tissue engineering applications. PMID:24993610

Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research

PubMed Central

Dietrich, Johannes W.; Landgrafe-Mende, Gabi; Wiora, Evelin; Chatzitomaris, Apostolos; Klein, Harald H.; Midgley, John E. M.; Hoermann, Rudolf

2016-01-01

Although technical problems of thyroid testing have largely been resolved by modern assay technology, biological variation remains a challenge. This applies to subclinical thyroid disease, non-thyroidal illness syndrome, and those 10% of hypothyroid patients, who report impaired quality of life, despite normal thyrotropin (TSH) concentrations under levothyroxine (L-T4) replacement. Among multiple explanations for this condition, inadequate treatment dosage and monotherapy with L-T4 in subjects with impaired deiodination have received major attention. Translation to clinical practice is difficult, however, since univariate reference ranges for TSH and thyroid hormones fail to deliver robust decision algorithms for therapeutic interventions in patients with more subtle thyroid dysfunctions. Advances in mathematical and simulative modeling of pituitary–thyroid feedback control have improved our understanding of physiological mechanisms governing the homeostatic behavior. From multiple cybernetic models developed since 1956, four examples have also been translated to applications in medical decision-making and clinical trials. Structure parameters representing fundamental properties of the processing structure include the calculated secretory capacity of the thyroid gland (SPINA-GT), sum activity of peripheral deiodinases (SPINA-GD) and Jostel’s TSH index for assessment of thyrotropic pituitary function, supplemented by a recently published algorithm for reconstructing the personal set point of thyroid homeostasis. In addition, a family of integrated models (University of California-Los Angeles platform) provides advanced methods for bioequivalence studies. This perspective article delivers an overview of current clinical research on the basis of mathematical thyroid models. In addition to a summary of large clinical trials, it provides previously unpublished results of validation studies based on simulation and clinical samples. PMID:27375554

Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research.

PubMed

Dietrich, Johannes W; Landgrafe-Mende, Gabi; Wiora, Evelin; Chatzitomaris, Apostolos; Klein, Harald H; Midgley, John E M; Hoermann, Rudolf

2016-01-01

Although technical problems of thyroid testing have largely been resolved by modern assay technology, biological variation remains a challenge. This applies to subclinical thyroid disease, non-thyroidal illness syndrome, and those 10% of hypothyroid patients, who report impaired quality of life, despite normal thyrotropin (TSH) concentrations under levothyroxine (L-T4) replacement. Among multiple explanations for this condition, inadequate treatment dosage and monotherapy with L-T4 in subjects with impaired deiodination have received major attention. Translation to clinical practice is difficult, however, since univariate reference ranges for TSH and thyroid hormones fail to deliver robust decision algorithms for therapeutic interventions in patients with more subtle thyroid dysfunctions. Advances in mathematical and simulative modeling of pituitary-thyroid feedback control have improved our understanding of physiological mechanisms governing the homeostatic behavior. From multiple cybernetic models developed since 1956, four examples have also been translated to applications in medical decision-making and clinical trials. Structure parameters representing fundamental properties of the processing structure include the calculated secretory capacity of the thyroid gland (SPINA-GT), sum activity of peripheral deiodinases (SPINA-GD) and Jostel's TSH index for assessment of thyrotropic pituitary function, supplemented by a recently published algorithm for reconstructing the personal set point of thyroid homeostasis. In addition, a family of integrated models (University of California-Los Angeles platform) provides advanced methods for bioequivalence studies. This perspective article delivers an overview of current clinical research on the basis of mathematical thyroid models. In addition to a summary of large clinical trials, it provides previously unpublished results of validation studies based on simulation and clinical samples.

A consistent S-Adenosylmethionine force field improved by dynamic Hirshfeld-I atomic charges for biomolecular simulation

NASA Astrophysics Data System (ADS)

Saez, David Adrian; Vöhringer-Martinez, Esteban

2015-10-01

S-Adenosylmethionine (AdoMet) is involved in many biological processes as cofactor in enzymes transferring its sulfonium methyl group to various substrates. Additionally, it is used as drug and nutritional supplement to reduce the pain in osteoarthritis and against depression. Due to the biological relevance of AdoMet it has been part of various computational simulation studies and will also be in the future. However, to our knowledge no rigorous force field parameter development for its simulation in biological systems has been reported. Here, we use electronic structure calculations combined with molecular dynamics simulations in explicit solvent to develop force field parameters compatible with the AMBER99 force field. Additionally, we propose new dynamic Hirshfeld-I atomic charges which are derived from the polarized electron density of AdoMet in aqueous solution to describe its electrostatic interactions in biological systems. The validation of the force field parameters and the atomic charges is performed against experimental interproton NOE distances of AdoMet in aqueous solution and crystal structures of AdoMet in the cavity of three representative proteins.

Biological monitoring results for cadmium exposed workers.

PubMed

McDiarmid, M A; Freeman, C S; Grossman, E A; Martonik, J

1996-11-01

As part of a settlement agreement with the Occupational Safety and Health Administration (OSHA) involving exposure to cadmium (Cd), a battery production facility provided medical surveillance data to OSHA for review. Measurements of cadmium in blood, cadmium in urine, and beta 2-microglobulin in urine were obtained for more than 100 workers over an 18-month period. Some airborne Cd exposure data were also made available. Two subpopulations of this cohort were of primary interest in evaluating compliance with the medical surveillance provisions of the Cadmium Standard. These were a group of 16 workers medically removed from cadmium exposure due to elevations in some biological parameter, and a group of platemakers. Platemaking had presented a particularly high exposure opportunity and had recently undergone engineering interventions to minimize exposure. The effect on three biological monitoring parameters of medical removal protection in the first group and engineering controls in platemakers is reported. Results reveal that both medical removal from cadmium exposures and exposure abatement through the use of engineering and work practice controls generally result in declines in biological monitoring parameters of exposed workers. Implications for the success of interventions are discussed.

Robust Design of Biological Circuits: Evolutionary Systems Biology Approach

PubMed Central

Chen, Bor-Sen; Hsu, Chih-Yuan; Liou, Jing-Jia

2011-01-01

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise. PMID:22187523

Robust design of biological circuits: evolutionary systems biology approach.

PubMed

Chen, Bor-Sen; Hsu, Chih-Yuan; Liou, Jing-Jia

2011-01-01

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.

HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing.

PubMed

Wan, Shixiang; Zou, Quan

2017-01-01

Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types. Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction. The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource. THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

Modulating Wnt Signaling Pathway to Enhance Allograft Integration in Orthopedic Trauma Treatment

DTIC Science & Technology

2013-10-01

presented below. Quantitative output provides an extensive set of data but we have chosen to present the most relevant parameters that are reflected in...multiple parameters .  Most samples have been mechanically tested and data extracted for multiple parameters .  Histological evaluation of subset of...Sumner, D. R. Saline Irrigation Does Not Affect Bone Formation or Fixation Strength of Hydroxyapatite /Tricalcium Phosphate-Coated Implants in a Rat Model

Is multiple-sequence alignment required for accurate inference of phylogeny?

PubMed

Höhl, Michael; Ragan, Mark A

2007-04-01

The process of inferring phylogenetic trees from molecular sequences almost always starts with a multiple alignment of these sequences but can also be based on methods that do not involve multiple sequence alignment. Very little is known about the accuracy with which such alignment-free methods recover the correct phylogeny or about the potential for increasing their accuracy. We conducted a large-scale comparison of ten alignment-free methods, among them one new approach that does not calculate distances and a faster variant of our pattern-based approach; all distance-based alignment-free methods are freely available from http://www.bioinformatics.org.au (as Python package decaf+py). We show that most methods exhibit a higher overall reconstruction accuracy in the presence of high among-site rate variation. Under all conditions that we considered, variants of the pattern-based approach were significantly better than the other alignment-free methods. The new pattern-based variant achieved a speed-up of an order of magnitude in the distance calculation step, accompanied by a small loss of tree reconstruction accuracy. A method of Bayesian inference from k-mers did not improve on classical alignment-free (and distance-based) methods but may still offer other advantages due to its Bayesian nature. We found the optimal word length k of word-based methods to be stable across various data sets, and we provide parameter ranges for two different alphabets. The influence of these alphabets was analyzed to reveal a trade-off in reconstruction accuracy between long and short branches. We have mapped the phylogenetic accuracy for many alignment-free methods, among them several recently introduced ones, and increased our understanding of their behavior in response to biologically important parameters. In all experiments, the pattern-based approach emerged as superior, at the expense of higher resource consumption. Nonetheless, no alignment-free method that we examined recovers the correct phylogeny as accurately as does an approach based on maximum-likelihood distance estimates of multiply aligned sequences.

A Simulation Model for Studying Effects of Pollution and Freshwater Inflow on Secondary Productivity in an Ecosystem. Ph.D. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Johnson, R. W.

1974-01-01

A mathematical model of an ecosystem is developed. Secondary productivity is evaluated in terms of man related and controllable factors. Information from an existing physical parameters model is used as well as pertinent biological measurements. Predictive information of value to estuarine management is presented. Biological, chemical, and physical parameters measured in order to develop models of ecosystems are identified.

DLRS: gene tree evolution in light of a species tree.

PubMed

Sjöstrand, Joel; Sennblad, Bengt; Arvestad, Lars; Lagergren, Jens

2012-11-15

PrIME-DLRS (or colloquially: 'Delirious') is a phylogenetic software tool to simultaneously infer and reconcile a gene tree given a species tree. It accounts for duplication and loss events, a relaxed molecular clock and is intended for the study of homologous gene families, for example in a comparative genomics setting involving multiple species. PrIME-DLRS uses a Bayesian MCMC framework, where the input is a known species tree with divergence times and a multiple sequence alignment, and the output is a posterior distribution over gene trees and model parameters. PrIME-DLRS is available for Java SE 6+ under the New BSD License, and JAR files and source code can be downloaded from http://code.google.com/p/jprime/. There is also a slightly older C++ version available as a binary package for Ubuntu, with download instructions at http://prime.sbc.su.se. The C++ source code is available upon request. joel.sjostrand@scilifelab.se or jens.lagergren@scilifelab.se. PrIME-DLRS is based on a sound probabilistic model (Åkerborg et al., 2009) and has been thoroughly validated on synthetic and biological datasets (Supplementary Material online).

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

COACH: profile-profile alignment of protein families using hidden Markov models.

PubMed

Edgar, Robert C; Sjölander, Kimmen

2004-05-22

Alignments of two multiple-sequence alignments, or statistical models of such alignments (profiles), have important applications in computational biology. The increased amount of information in a profile versus a single sequence can lead to more accurate alignments and more sensitive homolog detection in database searches. Several profile-profile alignment methods have been proposed and have been shown to improve sensitivity and alignment quality compared with sequence-sequence methods (such as BLAST) and profile-sequence methods (e.g. PSI-BLAST). Here we present a new approach to profile-profile alignment we call Comparison of Alignments by Constructing Hidden Markov Models (HMMs) (COACH). COACH aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM. We compare the alignment accuracy of COACH with two recently published methods: Yona and Levitt's prof_sim and Sadreyev and Grishin's COMPASS. On two sets of reference alignments selected from the FSSP database, we find that COACH is able, on average, to produce alignments giving the best coverage or the fewest errors, depending on the chosen parameter settings. COACH is freely available from www.drive5.com/lobster

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Reproducible, high-throughput synthesis of colloidal nanocrystals for optimization in multidimensional parameter space.

PubMed

Chan, Emory M; Xu, Chenxu; Mao, Alvin W; Han, Gang; Owen, Jonathan S; Cohen, Bruce E; Milliron, Delia J

2010-05-12

While colloidal nanocrystals hold tremendous potential for both enhancing fundamental understanding of materials scaling and enabling advanced technologies, progress in both realms can be inhibited by the limited reproducibility of traditional synthetic methods and by the difficulty of optimizing syntheses over a large number of synthetic parameters. Here, we describe an automated platform for the reproducible synthesis of colloidal nanocrystals and for the high-throughput optimization of physical properties relevant to emerging applications of nanomaterials. This robotic platform enables precise control over reaction conditions while performing workflows analogous to those of traditional flask syntheses. We demonstrate control over the size, size distribution, kinetics, and concentration of reactions by synthesizing CdSe nanocrystals with 0.2% coefficient of variation in the mean diameters across an array of batch reactors and over multiple runs. Leveraging this precise control along with high-throughput optical and diffraction characterization, we effectively map multidimensional parameter space to tune the size and polydispersity of CdSe nanocrystals, to maximize the photoluminescence efficiency of CdTe nanocrystals, and to control the crystal phase and maximize the upconverted luminescence of lanthanide-doped NaYF(4) nanocrystals. On the basis of these demonstrative examples, we conclude that this automated synthesis approach will be of great utility for the development of diverse colloidal nanomaterials for electronic assemblies, luminescent biological labels, electroluminescent devices, and other emerging applications.

[Construction and analysis of a monitoring system with remote real-time multiple physiological parameters based on cloud computing].

PubMed

Zhu, Lingyun; Li, Lianjie; Meng, Chunyan

2014-12-01

There have been problems in the existing multiple physiological parameter real-time monitoring system, such as insufficient server capacity for physiological data storage and analysis so that data consistency can not be guaranteed, poor performance in real-time, and other issues caused by the growing scale of data. We therefore pro posed a new solution which was with multiple physiological parameters and could calculate clustered background data storage and processing based on cloud computing. Through our studies, a batch processing for longitudinal analysis of patients' historical data was introduced. The process included the resource virtualization of IaaS layer for cloud platform, the construction of real-time computing platform of PaaS layer, the reception and analysis of data stream of SaaS layer, and the bottleneck problem of multi-parameter data transmission, etc. The results were to achieve in real-time physiological information transmission, storage and analysis of a large amount of data. The simulation test results showed that the remote multiple physiological parameter monitoring system based on cloud platform had obvious advantages in processing time and load balancing over the traditional server model. This architecture solved the problems including long turnaround time, poor performance of real-time analysis, lack of extensibility and other issues, which exist in the traditional remote medical services. Technical support was provided in order to facilitate a "wearable wireless sensor plus mobile wireless transmission plus cloud computing service" mode moving towards home health monitoring for multiple physiological parameter wireless monitoring.

Identifying Martian Hydrothermal Sites: Geological Investigation Utilizing Multiple Datasets

NASA Technical Reports Server (NTRS)

Dohm, J. M.; Baker, V. R.; Anderson, R. C.; Scott, D. H.; Rice, J. W., Jr.; Hare, T. M.

2000-01-01

Comprehensive geological investigations of martian landscapes that may have been modified by magmatic-driven hydrothermal activity, utilizing multiple datasets, will yield prime target sites for future hydrological, mineralogical, and biological investigations.

Into the field: naturalistic education and the future of conservation.

PubMed

Hayes, Mark A

2009-10-01

Some educational psychologists and researchers have argued that there are multiple ways of being intelligent. In the early 1980s, Howard Gardner presented a theory of multiple intelligences by proposing that humans can be described not by a single kind of intelligence, or intelligence quotient score, but rather by a variety of kinds of intelligence. This idea of considering multiple views of intelligence has helped educators look at intelligence from a less rigid, more expansive perspective. I considered how the relatively new concept of naturalistic intelligence, which is the cognitive potential to process information that is exhibited by expert naturalists, might influence the design of undergraduate biology curricula. Naturalistic intelligence can be fostered in undergraduate biology students by emphasizing the need for well-rounded scientific naturalists; developing curricula that involves students in outdoor inquiry-based projects; and helping students learn how to observe both the natural world and their own learning, skills that are essential to developing expert naturalistic knowledge. Professors, graduate students, and administrators can improve the naturalistic intelligence of undergraduate biology students by giving these students opportunities to be involved in outdoor research. Time spent outdoors alone and among people with expertise in natural history, ecology, and conservation biology will have important influences on the knowledge and skills biology undergraduates learn, the careers they pursue, and the contributions they make to conserving Earth's biodiversity.

The use and QA of biologically related models for treatment planning: short report of the TG-166 of the therapy physics committee of the AAPM.

PubMed

Allen Li, X; Alber, Markus; Deasy, Joseph O; Jackson, Andrew; Ken Jee, Kyung-Wook; Marks, Lawrence B; Martel, Mary K; Mayo, Charles; Moiseenko, Vitali; Nahum, Alan E; Niemierko, Andrzej; Semenenko, Vladimir A; Yorke, Ellen D

2012-03-01

Treatment planning tools that use biologically related models for plan optimization and/or evaluation are being introduced for clinical use. A variety of dose-response models and quantities along with a series of organ-specific model parameters are included in these tools. However, due to various limitations, such as the limitations of models and available model parameters, the incomplete understanding of dose responses, and the inadequate clinical data, the use of biologically based treatment planning system (BBTPS) represents a paradigm shift and can be potentially dangerous. There will be a steep learning curve for most planners. The purpose of this task group is to address some of these relevant issues before the use of BBTPS becomes widely spread. In this report, the authors (1) discuss strategies, limitations, conditions, and cautions for using biologically based models and parameters in clinical treatment planning; (2) demonstrate the practical use of the three most commonly used commercially available BBTPS and potential dosimetric differences between biologically model based and dose-volume based treatment plan optimization and evaluation; (3) identify the desirable features and future directions in developing BBTPS; and (4) provide general guidelines and methodology for the acceptance testing, commissioning, and routine quality assurance (QA) of BBTPS.

Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves.

PubMed

Jiménez, José; García, Emilio J; Llaneza, Luis; Palacios, Vicente; González, Luis Mariano; García-Domínguez, Francisco; Múñoz-Igualada, Jaime; López-Bao, José Vicente

2016-08-01

In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical-site-occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost-effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well-coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population-parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters. © 2016 Society for Conservation Biology.

QCScreen: a software tool for data quality control in LC-HRMS based metabolomics.

PubMed

Simader, Alexandra Maria; Kluger, Bernhard; Neumann, Nora Katharina Nicole; Bueschl, Christoph; Lemmens, Marc; Lirk, Gerald; Krska, Rudolf; Schuhmacher, Rainer

2015-10-24

Metabolomics experiments often comprise large numbers of biological samples resulting in huge amounts of data. This data needs to be inspected for plausibility before data evaluation to detect putative sources of error e.g. retention time or mass accuracy shifts. Especially in liquid chromatography-high resolution mass spectrometry (LC-HRMS) based metabolomics research, proper quality control checks (e.g. for precision, signal drifts or offsets) are crucial prerequisites to achieve reliable and comparable results within and across experimental measurement sequences. Software tools can support this process. The software tool QCScreen was developed to offer a quick and easy data quality check of LC-HRMS derived data. It allows a flexible investigation and comparison of basic quality-related parameters within user-defined target features and the possibility to automatically evaluate multiple sample types within or across different measurement sequences in a short time. It offers a user-friendly interface that allows an easy selection of processing steps and parameter settings. The generated results include a coloured overview plot of data quality across all analysed samples and targets and, in addition, detailed illustrations of the stability and precision of the chromatographic separation, the mass accuracy and the detector sensitivity. The use of QCScreen is demonstrated with experimental data from metabolomics experiments using selected standard compounds in pure solvent. The application of the software identified problematic features, samples and analytical parameters and suggested which data files or compounds required closer manual inspection. QCScreen is an open source software tool which provides a useful basis for assessing the suitability of LC-HRMS data prior to time consuming, detailed data processing and subsequent statistical analysis. It accepts the generic mzXML format and thus can be used with many different LC-HRMS platforms to process both multiple quality control sample types as well as experimental samples in one or more measurement sequences.

Configuration of multiple human stressors and their impacts on fish assemblages in Alpine river basins of Austria.

PubMed

Schinegger, Rafaela; Pucher, Matthias; Aschauer, Christiane; Schmutz, Stefan

2018-03-01

This work addresses multiple human stressors and their impacts on fish assemblages of the Drava and Mura rivers in southern Austria. The impacts of single and multiple human stressors on riverine fish assemblages in these basins were disentangled, based on an extensive dataset. Stressor configuration, i.e. various metrics of multiple stressors belonging to stressor groups hydrology, morphology, connectivity and water quality were investigated for the first time at river basin scale in Austria. As biological response variables, the Fish Index Austria (FIA) and its related single as well as the WFD biological- and total state were investigated. Stressor-response analysis shows divergent results, but a general trend of decreasing ecological integrity with increasing number of stressors and maximum stressor is observed. Fish metrics based on age structure, fish region index and biological status responded best to single stressors and/or their combinations. The knowledge gained in this work provides a basis for advanced investigations in Alpine river basins and beyond, supports WFD implementation and helps prioritizing further actions towards multi-stressor restoration- and management. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Statistical inference from multiple iTRAQ experiments without using common reference standards.

PubMed

Herbrich, Shelley M; Cole, Robert N; West, Keith P; Schulze, Kerry; Yager, James D; Groopman, John D; Christian, Parul; Wu, Lee; O'Meally, Robert N; May, Damon H; McIntosh, Martin W; Ruczinski, Ingo

2013-02-01

Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or "masterpool", in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.

Bayesian model comparison and parameter inference in systems biology using nested sampling.

PubMed

Pullen, Nick; Morris, Richard J

2014-01-01

Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focuses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design.

Molecular biological analysis in a patient with multiple lung adenocarcinomas.

PubMed

Wakayama, Tomoshige; Hirata, Hirokuni; Suka, Shunsuke; Sato, Kozo; Tatewaki, Masamitsu; Souma, Ryosuke; Satoh, Hideyuki; Tamura, Motohiko; Matsumura, Yuji; Imada, Hiroki; Sugiyama, Kumiya; Arima, Masafumi; Kurasawa, Kazuhiro; Fukuda, Takeshi; Fukushima, Yasutsugu

2018-05-01

The utility of molecular biological analysis in lung adenocarcinoma has been demonstrated. Herein we report a rare case presenting as multiple lung adenocarcinomas with four different EGFR gene mutations detected in three lung tumors. After opacification was detected by routine chest X-ray, the patient, a 64-year-old woman, underwent chest computed tomography which revealed a right lung segment S4 ground-glass nodule (GGN). Follow-up computed tomography revealed a 42 mm GGN nodule with a 26 mm nodule (S6) and a 20 mm GGN (S10). Histopathology of resected specimens from the right middle and lower lobes revealed all three nodules were adenocarcinomas. Four EGFR mutations were detected; no three tumors had the same mutations. Molecular biological analysis is a promising tool for the diagnosis of primary tumors in patients with multiple lung carcinomas of the same histotype, enabling appropriate treatment. © 2018 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

PubMed Central

Zhang, Shujun

2018-01-01

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study. PMID:29377896

Rectal Bleeding After High-Dose-Rate Brachytherapy Combined With Hypofractionated External-Beam Radiotherapy for Localized Prostate Cancer: The Relationship Between Dose-Volume Histogram Parameters and the Occurrence Rate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Okamoto, Masahiko, E-mail: masaoka@showa.gunma-u.ac.jp; Ishikawa, Hitoshi; Ebara, Takeshi

2012-02-01

Purpose: To determine the predictive risk factors for Grade 2 or worse rectal bleeding after high-dose-rate brachytherapy (HDR-BT) combined with hypofractionated external-beam radiotherapy (EBRT) for prostate cancer using dose-volume histogram analysis. Methods and Materials: The records of 216 patients treated with HDR-BT combined with EBRT were analyzed. The treatment protocols for HDR-BT were 5 Gy Multiplication-Sign five times in 3 days or 7 Gy Multiplication-Sign three, 10.5 Gy Multiplication-Sign two, or 9 Gy Multiplication-Sign two in 2 days. The EBRT doses ranged from 45 to 51 Gy with a fractional dose of 3 Gy. Results: In 20 patients Grade 2more » or worse rectal bleeding developed, and the cumulative incidence rate was 9% at 5 years. By converting the HDR-BT and EBRT radiation doses into biologic effective doses (BED), the BED{sub 3} at rectal volumes of 5% and 10% in the patients who experienced bleeding were significantly higher than those in the remaining 196 patients. Univariate analysis showed that a higher rectal BED{sub 3-5%} and the use of fewer needles in brachytherapy were correlated with the incidence of bleeding, but BED{sub 3-5%} was found to be the only significant factor on multivariate analysis. Conclusions: The radiation dose delivered to small rectal lesions as 5% is important for predicting Grade 2 or worse rectal bleeding after HDR-BT combined with EBRT for prostate cancer.« less

Removal of Multiple Contaminants: Biological Treatment

EPA Science Inventory

This presentation contains (1) background material on nitrate, perchlorate and ammonia contamination in the continental US; (2) scientific background on biological drinking water treatment; (3) results of bench-scale anaerobic and aerobic treatment studies; (4) results of pilot-s...

Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets.

PubMed

Salem, Saeed; Ozcaglar, Cagri

2014-01-01

Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the challenges of protein functional annotation and biological module discovery based on a single gene expression data, which suffers from spurious coexpression. We propose a joint mining algorithm that constructs a weighted hybrid similarity graph whose nodes are the coexpression links. The weight of an edge between two coexpression links in this hybrid graph is a linear combination of the topological similarities and co-appearance similarities of the corresponding two coexpression links. Clustering the weighted hybrid similarity graph yields recurrent coexpression link clusters (modules). Experimental results on Human gene expression datasets show that the reported modules are functionally homogeneous as evident by their enrichment with biological process GO terms and KEGG pathways.

New biological agents in the treatment of multiple sclerosis.

PubMed

Buc, M

2018-01-01

Multiple sclerosis (MS) is an inflammatory disease induced by autoimmune processes. Their understanding has resulted in an introduction of biological agents to its treatment. Interferon beta and glatiramer acetate have been in clinical practice for more than 20 years. Nowadays, novel biologics, which target molecules involved in immunopathological processes more specifically have entered the scene. They are represented by monoclonal antibodies binding to molecules VLA4 (natalizumab), CD20 (ocrelizumab), CD52 (alemtuzumab) or alpha subunit of IL-2 receptor (daclizumab) or by small molecules such as those modulating the receptors involved in regulation of lymphocyte migration (fingolimod, ozanimod) or in induction of lymphopenia by apoptosis (dimethyl fumarate, cladribine). In the article, we shortly describe their efficacies, adverse reactions and perspectives of a future development in MS biologics. A treatment of neuromyelitis optica by monoclonal antibodies (rituximab, aquaporumab) is given too (Tab. 1, Fig. 2, Ref. 71).

Development and Analysis of an Instrument to Assess Student Understanding of Foundational Concepts before Biochemistry Coursework

ERIC Educational Resources Information Center

Villafane, Sachel M.; Bailey, Cheryl P.; Loertscher, Jennifer; Minderhout, Vicky; Lewis, Jennifer E.

2011-01-01

Biochemistry is a challenging subject because student learning depends on the application of previously learned concepts from general chemistry and biology to new, biological contexts. This article describes the development of a multiple-choice instrument intended to measure five concepts from general chemistry and three from biology that are…

Infusion of Quantitative and Statistical Concepts into Biology Courses Does Not Improve Quantitative Literacy

ERIC Educational Resources Information Center

Beck, Christopher W.

2018-01-01

Multiple national reports have pushed for the integration of quantitative concepts into the context of disciplinary science courses. The aim of this study was to evaluate the quantitative and statistical literacy of biology students and explore learning gains when those skills were taught implicitly in the context of biology. I examined gains in…

Exploring Pedagogical Content Knowledge of Biology Graduate Teaching Assistants through Their Participation in Lesson Study

ERIC Educational Resources Information Center

Lampley, Sandra A.; Gardner, Grant E.; Barlow, Angela T.

2018-01-01

Graduate teaching assistants (GTAs) are responsible for teaching the majority of biology undergraduate laboratory sections, although many feel underprepared to do so. This study explored the impact of biology GTA participation in a professional development model known as lesson study. Using a case study methodology with multiple qualitative data…

Multiple year effects of a biological control agent (Diorhabda carinulata) on Tamarix (saltcedar) ecosystem exchanges of carbon dioxide and water

USDA-ARS?s Scientific Manuscript database

Biological control of Tamarix spp. (saltcedar) with Diorhabda carinulata (the northern tamarisk beetle) is currently underway in several western states U.S.A. through historical releases and the natural migration of this insect. Given the widespread dispersal of this biological control agent and its...

Closing the Loop: Involving Faculty in the Assessment of Scientific and Quantitative Reasoning Skills of Biology Majors

ERIC Educational Resources Information Center

Hurney, Carol A.; Brown, Justin; Griscom, Heather Peckham; Kancler, Erika; Wigtil, Clifton J.; Sundre, Donna

2011-01-01

The development of scientific and quantitative reasoning skills in undergraduates majoring in science, technology, engineering, and mathematics (STEM) is an objective of many courses and curricula. The Biology Department at James Madison University (JMU) assesses these essential skills in graduating biology majors by using a multiple-choice exam…

Limited Bandwidth Recognition of Collective Behaviors in Bio-Inspired Swarms

DTIC Science & Technology

2014-05-09

collective? Some swarm models exhibit multiple emergent behaviors from the same parameters. This provides increased expressivity at the cost of...swarms, namely, how do you know what the swarm is doing if you can’t ob- serve every agent in the collective? Some swarm models exhibit multiple ...flocking [15, 21, 12] or cyclic behavior [8, 7], and in some cases can exhibit multiple group behaviors depending on the model parameters used [6, 3, 17

The beginning of Space Life Science in China exploration rockets for biological experiment during 1960's

NASA Astrophysics Data System (ADS)

Jiang, Peidong; Zhang, Jingxue

The first step of space biological experiment in China was a set of five exploration rockets launched during 1964 to 1966, by Shanghai Institute of Machine and Electricity, and Institute of Biophysics of The Chinese Academy of Sciences. Three T-7AS1rockets for rats, mice and other samples in a biological cabin were launched and recovered safely in July of 1964 and June of 1965. Two T-7AS2rockets for dog, rats, mice and other samples in a biological cabin were launched and recovered safely in July of 1966. Institute of Biophysics in charged of the general design of biological experiments, telemetry of physiological parameters, and selection and training of experiment animals. The samples on-board were: rats, mice, dogs, and test tubes with fruit fly, enzyme, bacteria, E. Coli., lysozyme, bacteriaphage, RNAase, DNAase, crystals of enzyme, etc. Physiological, biochemical, bacte-riological, immunological, genetic, histochemical studies had been conducted, in cellular and sub cellular level. The postures of rat and dog were monitored during flight and under weight-lessness. Physiological parameters of ECG, blood pressure, respiration rate, body temperature were recorded. A dog named"Xiao Bao"was flight in 1966 with video monitor, life support system and conditioned reflex equipment. It flighted for more than 20 minutes and about 70km high. After 40 years, the experimental data recorded of its four physiological parameters during the flight process was reviewed. The change of 4 parameters during various phase of total flight process were compared, analyzed and discussed.

Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra.

PubMed

Liang, Junyi; Xia, Jiangyang; Shi, Zheng; Jiang, Lifen; Ma, Shuang; Lu, Xingjie; Mauritz, Marguerite; Natali, Susan M; Pegoraro, Elaine; Penton, C Ryan; Plaza, César; Salmon, Verity G; Celis, Gerardo; Cole, James R; Konstantinidis, Konstantinos T; Tiedje, James M; Zhou, Jizhong; Schuur, Edward A G; Luo, Yiqi

2018-05-26

Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over five years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g m -2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Force-induced bone growth and adaptation: A system theoretical approach to understanding bone mechanotransduction

NASA Astrophysics Data System (ADS)

Maldonado, Solvey; Findeisen, Rolf

2010-06-01

The modeling, analysis, and design of treatment therapies for bone disorders based on the paradigm of force-induced bone growth and adaptation is a challenging task. Mathematical models provide, in comparison to clinical, medical and biological approaches an structured alternative framework to understand the concurrent effects of the multiple factors involved in bone remodeling. By now, there are few mathematical models describing the appearing complex interactions. However, the resulting models are complex and difficult to analyze, due to the strong nonlinearities appearing in the equations, the wide range of variability of the states, and the uncertainties in parameters. In this work, we focus on analyzing the effects of changes in model structure and parameters/inputs variations on the overall steady state behavior using systems theoretical methods. Based on an briefly reviewed existing model that describes force-induced bone adaptation, the main objective of this work is to analyze the stationary behavior and to identify plausible treatment targets for remodeling related bone disorders. Identifying plausible targets can help in the development of optimal treatments combining both physical activity and drug-medication. Such treatments help to improve/maintain/restore bone strength, which deteriorates under bone disorder conditions, such as estrogen deficiency.

Adhesion analysis for chromium nitride thin films deposited by reactive magnetron sputtering

NASA Astrophysics Data System (ADS)

Rusu, F. M.; Merie, V. V.; Pintea, I. M.; Molea, A.

2016-08-01

The thin film industry is continuously growing due to the wide range of applications that require the fabrication of advanced components such as sensors, biological implants, micro-electromechanical devices, optical coatings and so on. The selection regarding the deposition materials, as well as the deposition technology influences the properties of the material and determines the suitability of devices for certain real-world applications. This paper is focused on the adhesion force for several chromium nitride thin films obtained by reactive magnetron sputtering. All chromium nitride thin films were deposited on a silicon substrate, the discharge current and the argon flow being kept constant. The main purpose of the paper is to determine the influence of deposition parameters on the adhesion force. Therefore some of the deposition parameters were varied in order to study their effect on the adhesion force. Experimentally, the values of the adhesion force were determined in multiple points for each sample using the spectroscopy in point mode of the atomic force microscope. The obtained values were used to estimate the surface energy of the CrN thin films based on two existing mathematical models for the adhesion force when considering the contact between two bodies.

A combined evaluation of the characteristics and acute toxicity of antibiotic wastewater.

PubMed

Yu, Xin; Zuo, Jiane; Li, Ruixia; Gan, Lili; Li, Zaixing; Zhang, Fei

2014-08-01

The conventional parameters and acute toxicities of antibiotic wastewater collected from each treatment unit of an antibiotic wastewater treatment plant have been investigated. The investigation of the conventional parameters indicated that the antibiotic wastewater treatment plant performed well under the significant fluctuation in influent water quality. The results of acute toxicity indicated that the toxicity of antibiotic wastewater could be reduced by 94.3 percent on average after treatment. However, treated antibiotic effluents were still toxic to Vibrio fischeri. The toxicity of antibiotic production wastewater could be attributed to the joint effects of toxic compound mixtures in wastewater. Moreover, aerobic biological treatment processes, including sequencing batch reactor (SBR) and aerobic biofilm reactor, played the most important role in reducing toxicity by 92.4 percent. Pearson׳s correlation coefficients revealed that toxicity had a strong and positive linear correlation with organic substances, nitrogenous compounds, S(2-), volatile phenol, cyanide, As, Zn, Cd, Ni and Fe. Ammonia nitrogen (NH4(+)) was the greatest contributor to toxicity according to the stepwise regression method. The multiple regression model was a good fit for [TU50-15 min] as a function of [NH₄(+)] with the determination coefficient of 0.981. Copyright © 2014 Elsevier Inc. All rights reserved.

Relationships Between Quantitative Pulse-Echo Ultrasound Parameters from the Superficial Zone of the Human Articular Cartilage and Changes in Surface Roughness, Collagen Content or Collagen Orientation Caused by Early Degeneration.

PubMed

Kiyan, Wataru; Ito, Akira; Nakagawa, Yasuaki; Mukai, Shogo; Mori, Koji; Arai, Tatsuo; Uchino, Eiichiro; Okuno, Yasushi; Kuroki, Hiroshi

2017-08-01

We aimed to quantitatively investigate the relationship between amplitude-based pulse-echo ultrasound parameters and early degeneration of the knee articular cartilage. Twenty samples from six human femoral condyles judged as grade 0 or 1 according to International Cartilage Repair Society grading were assessed using a 15-MHz pulsed-ultrasound 3-D scanning system ex vivo. Surface roughness (R q ), average collagen content (A 1 ) and collagen orientation (A 12 ) in the superficial zone of the cartilage were measured via laser microscopy and Fourier transform infrared imaging spectroscopy. Multiple regression analysis with a linear mixed-effects model (LMM) revealed that a time-domain reflection coefficient at the cartilage surface (R c ) had a significant coefficient of determination with R q and A 12 (R LMMm 2 =0.79); however, R c did not correlate with A 1 . Concerning the collagen characteristic in the superficial zone, R c was found to be a sensitive indicator reflecting collagen disorganization, not collagen content, for the early degeneration samples. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

PubMed

Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

2016-05-01

This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

Quantification of Cardiomyocyte Alignment from Three-Dimensional (3D) Confocal Microscopy of Engineered Tissue.

PubMed

Kowalski, William J; Yuan, Fangping; Nakane, Takeichiro; Masumoto, Hidetoshi; Dwenger, Marc; Ye, Fei; Tinney, Joseph P; Keller, Bradley B

2017-08-01

Biological tissues have complex, three-dimensional (3D) organizations of cells and matrix factors that provide the architecture necessary to meet morphogenic and functional demands. Disordered cell alignment is associated with congenital heart disease, cardiomyopathy, and neurodegenerative diseases and repairing or replacing these tissues using engineered constructs may improve regenerative capacity. However, optimizing cell alignment within engineered tissues requires quantitative 3D data on cell orientations and both efficient and validated processing algorithms. We developed an automated method to measure local 3D orientations based on structure tensor analysis and incorporated an adaptive subregion size to account for multiple scales. Our method calculates the statistical concentration parameter, κ, to quantify alignment, as well as the traditional orientational order parameter. We validated our method using synthetic images and accurately measured principal axis and concentration. We then applied our method to confocal stacks of cleared, whole-mount engineered cardiac tissues generated from human-induced pluripotent stem cells or embryonic chick cardiac cells and quantified cardiomyocyte alignment. We found significant differences in alignment based on cellular composition and tissue geometry. These results from our synthetic images and confocal data demonstrate the efficiency and accuracy of our method to measure alignment in 3D tissues.

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2018-03-26

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

Predicting features of breast cancer with gene expression patterns.

PubMed

Lu, Xuesong; Lu, Xin; Wang, Zhigang C; Iglehart, J Dirk; Zhang, Xuegong; Richardson, Andrea L

2008-03-01

Data from gene expression arrays hold an enormous amount of biological information. We sought to determine if global gene expression in primary breast cancers contained information about biologic, histologic, and anatomic features of the disease in individual patients. Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers [estrogen receptor (ER) and HER2], histologic features [grade and lymphatic-vascular invasion (LVI)], and stage parameters (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy was determined by cross-validation error rate; multidimensional scaling (MDS) allowed visualization of the predicted states under study. Models built from gene expression data accurately predict ER and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data is inaccurate at predicting tumor size, lymph node status or LVI. The best model for prediction of nodal status included tumor size, LVI status and pathologically defined tumor subtype (based on combinations of ER, HER2, and grade); the addition of microarray-based prediction to this model failed to improve the prediction accuracy. Global gene expression supports a binary division of ER, HER2, and grade, clearly separating tumors into two categories; intermediate values for these bio-indicators do not define intermediate tumor subsets. Results are consistent with a model of regional metastasis that depends on inherent biologic differences in metastatic propensity between breast cancer subtypes, upon which time and chance then operate.

Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion

PubMed Central

Sun, Fuqiang; Liu, Le; Li, Xiaoyang; Liao, Haitao

2016-01-01

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product’s performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner’s ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters. PMID:27509499

Stochastic Modeling and Analysis of Multiple Nonlinear Accelerated Degradation Processes through Information Fusion.

PubMed

Sun, Fuqiang; Liu, Le; Li, Xiaoyang; Liao, Haitao

2016-08-06

Accelerated degradation testing (ADT) is an efficient technique for evaluating the lifetime of a highly reliable product whose underlying failure process may be traced by the degradation of the product's performance parameters with time. However, most research on ADT mainly focuses on a single performance parameter. In reality, the performance of a modern product is usually characterized by multiple parameters, and the degradation paths are usually nonlinear. To address such problems, this paper develops a new s-dependent nonlinear ADT model for products with multiple performance parameters using a general Wiener process and copulas. The general Wiener process models the nonlinear ADT data, and the dependency among different degradation measures is analyzed using the copula method. An engineering case study on a tuner's ADT data is conducted to demonstrate the effectiveness of the proposed method. The results illustrate that the proposed method is quite effective in estimating the lifetime of a product with s-dependent performance parameters.

Anisotropy of the angular distribution of fission fragments in heavy-ion fusion-fission reactions: The influence of the level-density parameter and the neck thickness

NASA Astrophysics Data System (ADS)

Naderi, D.; Pahlavani, M. R.; Alavi, S. A.

2013-05-01

Using the Langevin dynamical approach, the neutron multiplicity and the anisotropy of angular distribution of fission fragments in heavy ion fusion-fission reactions were calculated. We applied one- and two-dimensional Langevin equations to study the decay of a hot excited compound nucleus. The influence of the level-density parameter on neutron multiplicity and anisotropy of angular distribution of fission fragments was investigated. We used the level-density parameter based on the liquid drop model with two different values of the Bartel approach and Pomorska approach. Our calculations show that the anisotropy and neutron multiplicity are affected by level-density parameter and neck thickness. The calculations were performed on the 16O+208Pb and 20Ne+209Bi reactions. Obtained results in the case of the two-dimensional Langevin with a level-density parameter based on Bartel and co-workers approach are in better agreement with experimental data.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

Deconstructing and constructing innate immune functions using molecular sensors and actuators

NASA Astrophysics Data System (ADS)

Coutinho, Kester; Inoue, Takanari

2016-05-01

White blood cells such as neutrophils and macrophages are made competent for chemotaxis and phagocytosis -- the dynamic cellular behaviors that are hallmarks of their innate immune functions -- by the reorganization of complex biological circuits during differentiation. Conventional loss-of-function approaches have revealed that more than 100 genes participate in these cellular functions, and we have begun to understand the intricate signaling circuits that are built up from these gene products. We now appreciate: (1) that these circuits come in a variety of flavors -- so that we can make a distinction between genetic circuits, metabolic circuits and signaling circuits; and (2) that they are usually so complex that the assumption of multiple feedback loops, as well as that of crosstalk between seemingly independent pathways, is now routine. It has not escaped our notice, however, that just as physicists and electrical engineers have long been able to disentangle complex electric circuits simply by repetitive cycles of probing and measuring electric currents using a voltmeter, we might similarly be able to dissect these intricate biological circuits by incorporating equivalent approaches in the fields of cell biology and bioengineering. Existing techniques in biology for probing individual circuit components are unfortunately lacking, so that the overarching goal of drawing an exact circuit diagram for the whole cell -- complete with kinetic parameters for connections between individual circuit components -- is not yet in near sight. My laboratory and others have thus begun the development of a new series of molecular tools that can measurably investigate the circuit connectivity inside living cells, as if we were doing so on a silicon board. In these proceedings, I will introduce some of these techniques, provide examples of their implementation, and offer a perspective on directions moving forward.

Differences in quantification of DNA double-strand breaks assessed by 53BP1/γH2AX focus formation assays and the comet assay in mammalian cells treated with irradiation and N-acetyl-L-cysteine.

PubMed

Kurashige, Tomomi; Shimamura, Mika; Nagayama, Yuji

2016-06-01

The biological effect of ionizing radiation (IR) on genomic DNA is thought to be either direct or indirect; the latter is mediated by IR induction of free radicals and reactive oxygen species (ROS). This study was designed to evaluate the effect of N-acetyl-L-cysteine (NAC), a well-known ROS-scavenging antioxidant, on IR induction of genotoxicity, cytotoxicity and ROS production in mammalian cells, and aimed to clarify the conflicting data in previous publications. Although we clearly demonstrate the beneficial effect of NAC on IR-induced genotoxicity and cytotoxicity (determined using the micronucleus assay and cell viability/clonogenic assays), the data on NAC's effect on DNA double-strand break (DSB) formation were inconsistent in different assays. Specifically, mitigation of IR-induced DSBs by NAC was readily detected by the neutral comet assay, but not by the γH2AX or 53BP1 focus assays. NAC is a glutathione precursor and exerts its effect after conversion to glutathione, and presumably it has its own biological activity. Assuming that the focus assay reflects the biological responses to DSBs (detection and repair), while the comet assay reflects the physical status of genomic DNA, our results indicate that the comet assay could readily detect the antioxidant effect of NAC on DSB formation. However, NAC's biological effect might affect the detection of DSB repair by the focus assays. Our data illustrate that multiple parameters should be carefully used to analyze DNA damage when studying potential candidates for radioprotective compounds. © The Author 2016. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

[Establishment and Management of Multiple Myeloma Specimen Bank Applied for Molecular Biological Researches].

PubMed

Li, Han-Qing; Mei, Jian-Gang; Cao, Hong-Qin; Shao, Liang-Jing; Zhai, Yong-Ping

2017-12-01

To establish a multiple myeloma specimen bank applied for molecular biological researches and to explore the methods of specimen collection, transportation, storage, quality control and the management of specimen bank. Bone marrow and blood samples were collected from multiple myeloma patients, plasma cell sorting were operated after the separation of mononuclear cells from bone marrow specimens. The plasma cells were divided into 2 parts, one was added with proper amount of TRIzol and then kept in -80 °C refrigerator for subsequent RNA extraction, the other was added with proper amount of calf serum cell frozen liquid and then kept in -80 °C refrigerator for subsequent cryopreservation of DNA extraction after numbered respectively. Serum and plasma were separated from peripheral blood, specimens of serum and plasma were then stored at -80 °C refrigerator after registration. Meantime, the myeloma specimen information management system was established, managed and maintained by specially-assigned persons and continuous modification and improvement in the process of use as to facilitate the rapid collection, management, query of the effective samples and clinical data. A total of 244 portions plasma cells, 564 portions of serum, and 1005 portions of plasma were collected, clinical characters were documented. A multiple myeloma specimen bank have been established initially, which can provide quality samples and related clinical information for molecular biological research on multiple myeloma.

The Gas6/TAM System and Multiple Sclerosis.

PubMed

Bellan, Mattia; Pirisi, Mario; Sainaghi, Pier Paolo

2016-10-28

Growth arrest specific 6 (Gas6) is a multimodular circulating protein, the biological actions of which are mediated by the interaction with three transmembrane tyrosine kinase receptors: Tyro3, Axl, and MerTK, collectively named TAM. Over the last few decades, many progresses have been done in the understanding of the biological activities of this highly pleiotropic system, which plays a role in the regulation of immune response, inflammation, coagulation, cell growth, and clearance of apoptotic bodies. Recent findings have further related Gas6 and TAM receptors to neuroinflammation in general and, specifically, to multiple sclerosis (MS). In this paper, we review the biology of the Gas6/TAM system and the current evidence supporting its potential role in the pathogenesis of MS.

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology

PubMed Central

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view. PMID:23515240

A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-off on Phenotype Robustness in Biological Networks Part I: Gene Regulatory Networks in Systems and Evolutionary Biology.

PubMed

Chen, Bor-Sen; Lin, Ying-Po

2013-01-01

Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties observed in biological systems at different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be enough to confer intrinsic robustness in order to tolerate intrinsic parameter fluctuations, genetic robustness for buffering genetic variations, and environmental robustness for resisting environmental disturbances. With this, the phenotypic stability of biological network can be maintained, thus guaranteeing phenotype robustness. This paper presents a survey on biological systems and then develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation in systems and evolutionary biology. Further, from the unifying mathematical framework, it was discovered that the phenotype robustness criterion for biological networks at different levels relies upon intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness. When this is true, the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in systems and evolutionary biology can also be investigated through their corresponding phenotype robustness criterion from the systematic point of view.

The Distributed Biological Observatory (DBO)-A Change Detection Array in the Pacific Arctic Sector

NASA Astrophysics Data System (ADS)

Grebmeier, J. M.; Moore, S. E.; Cooper, L. W.; Frey, K. E.; Pickart, R. S.

2011-12-01

The Pacific sector of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Variations in upper ocean water hydrography, planktonic production, pelagic-benthic coupling and sediment carbon cycling are all influenced by sea ice and temperature changes. Climate changes are likely to result in shifts in species composition and abundance, northward range expansions, and changes in lower trophic level productivity that can directly cascade and affect the life cycles of higher trophic level organisms. Several regionally critical marine sites in the Pacific Arctic sector that have very high biomass and are focused foraging points for apex predators have been re-occupied during multiple international cruises. The data documenting the importance of these ecosystem "hotspots" provide a growing marine time-series from the northern Bering Sea to Barrow Canyon at the boundary of the Chukchi and Beaufort seas. Results from these studies show spatial changes in carbon production and export to the sediments as indicated by infaunal community composition and biomass, shifts in sediment grain size on a S-to-N latitudinal gradient, and range extensions for lower trophic levels and further northward migration of higher trophic organisms, such as gray whales. There is also direct evidence of negative impacts on ice dependent species, such as walrus and polar bears. To more systematically track the broad biological response to sea ice retreat and associated environmental change, an international consortium of scientists are developing a "Distributed Biological Observatory" (DBO) that includes selected biological measurements at multiple trophic levels. The DBO currently focuses on five regional biological "hotspot" locations along a latitudinal gradient. Hydrographic transects occupied from spring to fall in 2010 and 2011 at two pilot sites in the SE Chukchi Sea and Barrow Canyon provide repeat collections of water parameters over the seasons that are unavailable from single cruises. This sampling indicates freshening and warming as Pacific seawater transits northward over the spring to fall seasons, with impacts on both plankton and benthic prey bases for larger marine mammals and seabirds. The intent of the DBO is to serve as a change detection array for the identification and consistent monitoring of biophysical responses. This network of spatially explicit DBOs is being organized through the Pacific Arctic Group (PAG), a collaborative network endorsed by the International Arctic Science Committee. Our presentation will provide new information to evaluate the status and developing trends of the marine biological system as it responds to the rapid environmental change.

Agricultural Exposures, Multiple Myeloma Etiology: Profile of Jonathan Hofmann

Cancer.gov

Tenure-track investigator Jonathan Hofmann, Ph.D., M.P.H., has established a research program in the Occupational and Environmental Epidemiology Branch focused on the role of agricultural exposures in the etiology of multiple myeloma and other cancers, and on understanding the biological mechanisms that influence the development and progression of multiple myeloma.

Imaging and the completion of the omics paradigm in breast cancer.

PubMed

Leithner, D; Horvat, J V; Ochoa-Albiztegui, R E; Thakur, S; Wengert, G; Morris, E A; Helbich, T H; Pinker, K

2018-06-08

Within the field of oncology, "omics" strategies-genomics, transcriptomics, proteomics, metabolomics-have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with "omics" data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology-pathology correlation from the anatomical-histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.

The relevance of time series in molecular ecology and conservation biology.

PubMed

Habel, Jan C; Husemann, Martin; Finger, Aline; Danley, Patrick D; Zachos, Frank E

2014-05-01

The genetic structure of a species is shaped by the interaction of contemporary and historical factors. Analyses of individuals from the same population sampled at different points in time can help to disentangle the effects of current and historical forces and facilitate the understanding of the forces driving the differentiation of populations. The use of such time series allows for the exploration of changes at the population and intraspecific levels over time. Material from museum collections plays a key role in understanding and evaluating observed population structures, especially if large numbers of individuals have been sampled from the same locations at multiple time points. In these cases, changes in population structure can be assessed empirically. The development of new molecular markers relying on short DNA fragments (such as microsatellites or single nucleotide polymorphisms) allows for the analysis of long-preserved and partially degraded samples. Recently developed techniques to construct genome libraries with a reduced complexity and next generation sequencing and their associated analysis pipelines have the potential to facilitate marker development and genotyping in non-model species. In this review, we discuss the problems with sampling and available marker systems for historical specimens and demonstrate that temporal comparative studies are crucial for the estimation of important population genetic parameters and to measure empirically the effects of recent habitat alteration. While many of these analyses can be performed with samples taken at a single point in time, the measurements are more robust if multiple points in time are studied. Furthermore, examining the effects of habitat alteration, population declines, and population bottlenecks is only possible if samples before and after the respective events are included. © 2013 The Authors. Biological Reviews © 2013 Cambridge Philosophical Society.

BCM Search Launcher--an integrated interface to molecular biology data base search and analysis services available on the World Wide Web.

PubMed

Smith, R F; Wiese, B A; Wojzynski, M K; Davison, D B; Worley, K C

1996-05-01

The BCM Search Launcher is an integrated set of World Wide Web (WWW) pages that organize molecular biology-related search and analysis services available on the WWW by function, and provide a single point of entry for related searches. The Protein Sequence Search Page, for example, provides a single sequence entry form for submitting sequences to WWW servers that offer remote access to a variety of different protein sequence search tools, including BLAST, FASTA, Smith-Waterman, BEAUTY, PROSITE, and BLOCKS searches. Other Launch pages provide access to (1) nucleic acid sequence searches, (2) multiple and pair-wise sequence alignments, (3) gene feature searches, (4) protein secondary structure prediction, and (5) miscellaneous sequence utilities (e.g., six-frame translation). The BCM Search Launcher also provides a mechanism to extend the utility of other WWW services by adding supplementary hypertext links to results returned by remote servers. For example, links to the NCBI's Entrez data base and to the Sequence Retrieval System (SRS) are added to search results returned by the NCBI's WWW BLAST server. These links provide easy access to auxiliary information, such as Medline abstracts, that can be extremely helpful when analyzing BLAST data base hits. For new or infrequent users of sequence data base search tools, we have preset the default search parameters to provide the most informative first-pass sequence analysis possible. We have also developed a batch client interface for Unix and Macintosh computers that allows multiple input sequences to be searched automatically as a background task, with the results returned as individual HTML documents directly to the user's system. The BCM Search Launcher and batch client are available on the WWW at URL http:@gc.bcm.tmc.edu:8088/search-launcher.html.

Variation in Tomato spotted wilt virus titer in Frankliniella occidentalis and its association with frequency of transmission.

PubMed

Rotenberg, Dorith; Krishna Kumar, Nallur K; Ullman, Diane E; Montero-Astúa, Mauricio; Willis, David K; German, Thomas L; Whitfield, Anna E

2009-04-01

Tomato spotted wilt virus (TSWV) is transmitted in a persistent propagative manner by Frankliniella occidentalis, the western flower thrips. While it is well established that vector competence depends on TSWV acquisition by young larvae and virus replication within the insect, the biological factors associated with frequency of transmission have not been well characterized. We hypothesized that the number of transmission events by a single adult thrips is determined, in part, by the amount of virus harbored (titer) by the insect. Transmission time-course experiments were conducted using a leaf disk assay to determine the efficiency and frequency of TSWV transmission following 2-day inoculation access periods (IAPs). Virus titer in individual adult thrips was determined by real-time quantitative reverse transcriptase-PCR (qRT-PCR) at the end of the experiments. On average, 59% of adults transmitted the virus during the first IAP (2 to 3 days post adult-eclosion). Male thrips were more efficient at transmitting TSWV multiple times compared with female thrips of the same cohort. However, females harbored two to three times more copies of TSWV-N RNA per insect, indicating that factors other than absolute virus titer in the insect contribute to a successful transmission event. Examination of virus titer in individual insects at the end of the third IAP (7 days post adult-eclosion) revealed significant and consistent positive associations between frequency of transmission and virus titer. Our data support the hypothesis that a viruliferous thrips is more likely to transmit multiple times if it harbors a high titer of virus. This quantitative relationship provides new insights into the biological parameters that may influence the spread of TSWV by thrips.

Experimental Design for Parameter Estimation of Gene Regulatory Networks

PubMed Central

Timmer, Jens

2012-01-01

Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines. PMID:22815723

Understanding the sorption and biotransformation of organic micropollutants in innovative biological wastewater treatment technologies.

PubMed

Alvarino, T; Suarez, S; Lema, J; Omil, F

2018-02-15

New technologies for wastewater treatment have been developed in the last years based on the combination of biological reactors operating under different redox conditions. Their efficiency in the removal of organic micropollutants (OMPs) has not been clearly assessed yet. This review paper is focussed on understanding the sorption and biotransformation of a selected group of 17 OMPs, including pharmaceuticals, hormones and personal care products, during biological wastewater treatment processes. Apart from considering the role of "classical" operational parameters, new factors such as biomass conformation and particle size, upward velocity applied or the addition of adsorbents have been considered. It has been found that the OMP removal by sorption not only depends on their physico-chemical characteristics and other parameters, such as the biomass conformation and particle size, or some operational conditions also relevant. Membrane biological reactors (MBR), have shown to enhance sorption and biotransformation of some OMPs. The same applies to technologies bases on direct addition of activated carbon in bioreactors. The OMP biotransformation degree and pathway is mainly driven by the redox potential and the primary substrate activity. The combination of different redox potentials in hybrid reactor systems can significantly enhance the overall OMP removal efficiency. Sorption and biotransformation can be synergistically promoted in biological reactors by the addition of activated carbon. The deeper knowledge of the main parameters influencing OMP removal provided by this review will allow optimizing the biological processes in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Selected National Cancer Institute Breast Cancer Research Topics | NIH MedlinePlus the Magazine

MedlinePlus

... effective treatments for these women. The Integrative Cancer Biology Program combines experimental and clinical research with mathematical modeling to gain new insights into cancer biology, prevention, diagnostics, and treatments. Multiple centers are developing ...

Methods and means of 3D diffuse Mueller-matrix tomography of depolarizing optically anisotropic biological layers

NASA Astrophysics Data System (ADS)

Dubolazov, O. V.; Ushenko, V. O.; Trifoniuk, L.; Ushenko, Yu. O.; Zhytaryuk, V. G.; Prydiy, O. G.; Grytsyuk, M.; Kushnerik, L.; Meglinskiy, I.

2017-09-01

A new technique of Mueller-matrix mapping of polycrystalline structure of histological sections of biological tissues is suggested. The algorithms of reconstruction of distribution of parameters of linear and circular birefringence of prostate histological sections are found. The interconnections between such distributions and parameters of linear and circular birefringence of prostate tissue histological sections are defined. The comparative investigations of coordinate distributions of phase anisotropy parameters formed by fibrillar networks of prostate tissues of different pathological states (adenoma and carcinoma) are performed. The values and ranges of change of the statistical (moments of the 1st - 4th order) parameters of coordinate distributions of the value of linear and circular birefringence are defined. The objective criteria of cause of Benign and malignant conditions differentiation are determined.

Prediction of biological integrity based on environmental similarity--revealing the scale-dependent link between study area and top environmental predictors.

PubMed

Bedoya, David; Manolakos, Elias S; Novotny, Vladimir

2011-03-01

Indices of Biological integrity (IBI) are considered valid indicators of the overall health of a water body because the biological community is an endpoint within natural systems. However, prediction of biological integrity using information from multi-parameter environmental observations is a challenging problem due to the hierarchical organization of the natural environment, the existence of nonlinear inter-dependencies among variables as well as natural stochasticity and measurement noise. We present a method for predicting the Fish Index of Biological Integrity (IBI) using multiple environmental observations at the state-scale in Ohio. Instream (chemical and physical quality) and offstream parameters (regional and local upstream land uses, stream fragmentation, and point source density and intensity) are used for this purpose. The IBI predictions are obtained using the environmental site-similarity concept and following a simple to implement leave-one-out cross validation approach. An IBI prediction for a sampling site is calculated by averaging the observed IBI scores of observations clustered in the most similar branch of a dendrogram--a hierarchical clustering tree of environmental observations--built using the rest of the observations. The standardized Euclidean distance is used to assess dissimilarity between observations. The constructed predictive model was able to explain 61% of the IBI variability statewide. Stream fragmentation and regional land use explained 60% of the variability; the remaining 1% was explained by instream habitat quality. Metrics related to local land use, water quality, and point source density and intensity did not improve the predictive model at the state-scale. The impact of local environmental conditions was evaluated by comparing local characteristics between well- and mispredicted sites. Significant differences in local land use patterns and upstream fragmentation density explained some of the model's over-predictions. Local land use conditions explained some of the model's IBI under-predictions at the state-scale since none of the variables within this group were included in the best final predictive model. Under-predicted sites also had higher levels of downstream fragmentation. The proposed variables ranking and predictive modeling methodology is very well suited for the analysis of hierarchical environments, such as natural fresh water systems, with many cross-correlated environmental variables. It is computationally efficient, can be fully automated, does not make any pre-conceived assumptions on the variables interdependency structure (such as linearity), and it is able to rank variables in a database and generate IBI predictions using only non-parametric easy to implement hierarchical clustering. Copyright © 2011 Elsevier Ltd. All rights reserved.

Measuring erosion rates of contaminated cohesive sediments using laboratory and in-situ devices in combination: experiences of investigations in River Elbe and Saale

NASA Astrophysics Data System (ADS)

Noack, Markus; Gerbersdorf, Sabine; Hillebrand, Gudrun; Kasimir, Petra; Wieprecht, Silke

2014-05-01

Deposition of contaminated sediments in areas of no or low flow velocity such as groyne fields or impounded river stretches represent a significant thread to water quality if long-deposited sediments are remobilized during flood and storm events. In contrast to non-cohesive sediments the dynamics of cohesive sediments is not fully understood mainly because of multiple physico-chemical factors and variable biological influence. Hence, site-specific investigations are required to develop water management strategies as well as modelling approaches to predict the dynamic behavior of cohesive material. The Institute for Modelling Hydraulic and Environmental Systems (IWS, University of Stuttgart) has a strong experience in developing measuring strategies and techniques to deal with the complex interactions between biological and sedimentary characteristics regarding erosion and remobilization of cohesive material. Specifically, the detection of critical shear stresses for incipient motion of cohesive particles has been realized for both one laboratory device (SETEG) and an in-situ device. For site-specific investigations ideally both methods should be combined. The first method (SETEG) includes the on-site extraction of sediment cores allowing for depth-dependent analysis under controlled laboratory conditions, while the second one measures the surface only but reduces possible artifacts due to sediment withdrawal and transport. Both methods were applied at groyne fields and deposition areas of the River Elbe and River Saale, which are both heavily affected by pollution of anthropogenic contaminants mainly originating from the release of chemical industry before 1990. Next to the detection of critical shear stresses and erosion rates, further sedimentary attributes are analyzed such as particle size distribution, water content and density as well as biological attributes such as TOC and microbial mass. The analyses of the sediment cores result in vertical profiles for all sedimentary and biological parameters giving highly complementary insights into the rather complex erosion and resuspension properties of cohesive fine sediments. Further, the detected critical shear stress between the in-situ and laboratory device are compared and especially in case of deviations the biological parameters can be highly beneficial to explain the measured critical shear stress and variances between in situ and laboratory devices. The investigations in both study sites have shown that the joint application of the measuring devices gives comprehensive information which is required to determine the risk of remobilization properly. Keywords: cohesive sediments, critical shear stress, contaminated sediments, incipient motion, biostabilization

A General Approach for Specifying Informative Prior Distributions for PBPK Model Parameters

EPA Science Inventory

Characterization of uncertainty in model predictions is receiving more interest as more models are being used in applications that are critical to human health. For models in which parameters reflect biological characteristics, it is often possible to provide estimates of paramet...

Effects of cover crops on soil quality: Selected chemical and biological parameters

USDA-ARS?s Scientific Manuscript database

Cover crops may improve soil physical, chemical, and biological properties and thus help improve land productivity. The objective of this study was to evaluate short-term changes (6, 9, and 12 weeks) in soil chemical and biological properties as influenced by cover crops for two different soils and...

Biological and analytical variations of 16 parameters related to coagulation screening tests and the activity of coagulation factors.

PubMed

Chen, Qian; Shou, Weiling; Wu, Wei; Guo, Ye; Zhang, Yujuan; Huang, Chunmei; Cui, Wei

2015-04-01

To accurately estimate longitudinal changes in individuals, it is important to take into consideration the biological variability of the measurement. The few studies available on the biological variations of coagulation parameters are mostly outdated. We confirmed the published results using modern, fully automated methods. Furthermore, we added data for additional coagulation parameters. At 8:00 am, 12:00 pm, and 4:00 pm on days 1, 3, and 5, venous blood was collected from 31 healthy volunteers. A total of 16 parameters related to coagulation screening tests as well as the activity of coagulation factors were analyzed; these included prothrombin time, fibrinogen (Fbg), activated partial thromboplastin time, thrombin time, international normalized ratio, prothrombin time activity, activated partial thromboplastin time ratio, fibrin(-ogen) degradation products, as well as the activity of factor II, factor V, factor VII, factor VIII, factor IX, and factor X. All intraindividual coefficients of variation (CVI) values for the parameters of the screening tests (except Fbg) were less than 5%. Conversely, the CVI values for the activity of coagulation factors were all greater than 5%. In addition, we calculated the reference change value to determine whether a significant difference exists between two test results from the same individual. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.

PubMed

Sharifi, Soroosh; Murthy, Sudhir; Takács, Imre; Massoudieh, Arash

2014-03-01

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Effects of multiple scattering on time- and depth-resolved signals in airborne lidar systems

NASA Technical Reports Server (NTRS)

Punjabi, A.; Venable, D. D.

1986-01-01

A semianalytic Monte Carlo radiative transfer model (SALMON) is employed to probe the effects of multiple-scattering events on the time- and depth-resolved lidar signals from homogeneous aqueous media. The effective total attenuation coefficients in the single-scattering approximation are determined as functions of dimensionless parameters characterizing the lidar system and the medium. Results show that single-scattering events dominate when these parameters are close to their lower bounds and that when their values exceed unity multiple-scattering events dominate.

Biomonitoring along the Tropical Southern Indian Coast with Multiple Biomarkers.

PubMed

Vignesh, Sivanandham; Dahms, Hans-Uwe; Muthukumar, Krishnan; Vignesh, Gopalaswamy; James, Rathinam Arthur

2016-01-01

We assessed the spatial and temporal variations of pollution indicators and geochemical and trace metal parameters (23 in total) from water and sediment (144 samples) of three different eco-niches (beach, fishing harbor, and estuary) in larger coastal cities of southern India (Cuddalore and Pondicherry) for one year. A total of 120 marine Pseudomonas isolates were challenged against different concentrations of copper solutions and 10 different antibiotics in heavy metal and antibiotic resistance approaches, respectively. The study shows that 4.16% of the isolates could survive in 250 mM of copper; 70% were resistant to minimum concentrations. Strains were resistant (98.4%) to at least one antibiotic in Cuddalore compared to the Pondicherry (78.4%) region. Pollution index (PI) (0-14.55) and antibiotic resistance index (ARI) (0.05-0.10) ratio indicated that high bacterial and antibiotic loads were released into the coastal environment. The degree of trace metal contamination in sediments were calculated by enrichment factor (EF), contamination factor (CF), pollution load index (PLI), and geo-accumulation index (Igeo). Statistical parameters like two-way analysis of variance (ANOVA), correlation, factor analysis and scatter matrix tools were employed between the 23 parameters in order to find sources, pathways, disparities and interactions of environmental pollutants. It indicates that geochemical and biological parameters were not strongly associated with each other (except a few) and were affected by different sources. Factor analysis elucidated, 'microbe-metal' interaction (Factor 1-48.86%), 'anthropogenic' factor (Factor 2-13.23%) and 'Pseudomonas-Cadmium' factor (Factor 3-11.74%), respectively.

Biomonitoring along the Tropical Southern Indian Coast with Multiple Biomarkers

PubMed Central

Vignesh, Sivanandham; Dahms, Hans-Uwe; Muthukumar, Krishnan; Vignesh, Gopalaswamy; James, Rathinam Arthur

2016-01-01

We assessed the spatial and temporal variations of pollution indicators and geochemical and trace metal parameters (23 in total) from water and sediment (144 samples) of three different eco-niches (beach, fishing harbor, and estuary) in larger coastal cities of southern India (Cuddalore and Pondicherry) for one year. A total of 120 marine Pseudomonas isolates were challenged against different concentrations of copper solutions and 10 different antibiotics in heavy metal and antibiotic resistance approaches, respectively. The study shows that 4.16% of the isolates could survive in 250 mM of copper; 70% were resistant to minimum concentrations. Strains were resistant (98.4%) to at least one antibiotic in Cuddalore compared to the Pondicherry (78.4%) region. Pollution index (PI) (0–14.55) and antibiotic resistance index (ARI) (0.05–0.10) ratio indicated that high bacterial and antibiotic loads were released into the coastal environment. The degree of trace metal contamination in sediments were calculated by enrichment factor (EF), contamination factor (CF), pollution load index (PLI), and geo-accumulation index (Igeo). Statistical parameters like two-way analysis of variance (ANOVA), correlation, factor analysis and scatter matrix tools were employed between the 23 parameters in order to find sources, pathways, disparities and interactions of environmental pollutants. It indicates that geochemical and biological parameters were not strongly associated with each other (except a few) and were affected by different sources. Factor analysis elucidated, ‘microbe–metal’ interaction (Factor 1–48.86%), ‘anthropogenic’ factor (Factor 2–13.23%) and ‘Pseudomonas–Cadmium’ factor (Factor 3–11.74%), respectively. PMID:27941969

Towards physical principles of biological evolution

NASA Astrophysics Data System (ADS)

Katsnelson, Mikhail I.; Wolf, Yuri I.; Koonin, Eugene V.

2018-03-01

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in ‘emergent phenomena’. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice for adequate modeling of the biological level of complexity, and new developments within physics itself are likely to be required.

Multi-chain Markov chain Monte Carlo methods for computationally expensive models

NASA Astrophysics Data System (ADS)

Huang, M.; Ray, J.; Ren, H.; Hou, Z.; Bao, J.

2017-12-01

Markov chain Monte Carlo (MCMC) methods are used to infer model parameters from observational data. The parameters are inferred as probability densities, thus capturing estimation error due to sparsity of the data, and the shortcomings of the model. Multiple communicating chains executing the MCMC method have the potential to explore the parameter space better, and conceivably accelerate the convergence to the final distribution. We present results from tests conducted with the multi-chain method to show how the acceleration occurs i.e., for loose convergence tolerances, the multiple chains do not make much of a difference. The ensemble of chains also seems to have the ability to accelerate the convergence of a few chains that might start from suboptimal starting points. Finally, we show the performance of the chains in the estimation of O(10) parameters using computationally expensive forward models such as the Community Land Model, where the sampling burden is distributed over multiple chains.

The Theory of Multiple Intelligences.

ERIC Educational Resources Information Center

Gardner, Howard

1987-01-01

The multiple intelligence theory is based on cultural contexts, biological analysis, developmental theories, and a vertical theory of faculties. Seven intelligences are identified: linguistic, logical mathematical, musical, spatial, bodily kinesthetic, interpersonal, and intrapersonal. The theory's educational implications are described,…

Individual to Community-Level Faunal Responses to Environmental Change from a Marine Fossil Record of Early Miocene Global Warming

PubMed Central

Belanger, Christina L.

2012-01-01

Modern climate change has a strong potential to shift earth systems and biological communities into novel states that have no present-day analog, leaving ecologists with no observational basis to predict the likely biotic effects. Fossil records contain long time-series of past environmental changes outside the range of modern observation, which are vital for predicting future ecological responses, and are capable of (a) providing detailed information on rates of ecological change, (b) illuminating the environmental drivers of those changes, and (c) recording the effects of environmental change on individual physiological rates. Outcrops of Early Miocene Newport Member of the Astoria Formation (Oregon) provide one such time series. This record of benthic foraminiferal and molluscan community change from continental shelf depths spans a past interval environmental change (∼20.3-16.7 mya) during which the region warmed 2.1–4.5°C, surface productivity and benthic organic carbon flux increased, and benthic oxygenation decreased, perhaps driven by intensified upwelling as on the modern Oregon coast. The Newport Member record shows that (a) ecological responses to natural environmental change can be abrupt, (b) productivity can be the primary driver of faunal change during global warming, (c) molluscs had a threshold response to productivity change while foraminifera changed gradually, and (d) changes in bivalve body size and growth rates parallel changes in taxonomic composition at the community level, indicating that, either directly or indirectly through some other biological parameter, the physiological tolerances of species do influence community change. Ecological studies in modern and fossil records that consider multiple ecological levels, environmental parameters, and taxonomic groups can provide critical information for predicting future ecological change and evaluating species vulnerability. PMID:22558424

Mutagenicity and in vivo toxicity of combined particulate and semivolatile organic fractions of gasoline and diesel engine emissions.

PubMed

Seagrave, JeanClare; McDonald, Jacob D; Gigliotti, Andrew P; Nikula, Kristen J; Seilkop, Steven K; Gurevich, Michael; Mauderly, Joe L

2002-12-01

Exposure to engine emissions is associated with adverse health effects. However, little is known about the relative effects of emissions produced by different operating conditions, fuels, or technologies. Rapid screening techniques are needed to compare the biological effects of emissions with different characteristics. Here, we examined a set of engine emission samples using conventional bioassays. The samples included combined particulate material and semivolatile organic compound fractions of emissions collected from normal- and high-emitter gasoline and diesel vehicles collected at 72 degrees F, and from normal-emitter groups collected at 30 degrees F. The relative potency of the samples was determined by statistical analysis of the dose-response curves. All samples induced bacterial mutagenicity, with a 10-fold range of potency among the samples. Responses to intratracheal instillation in rats indicated generally parallel rankings of the samples by multiple endpoints reflecting cytotoxic, inflammatory, and lung parenchymal changes, allowing selection of a more limited set of parameters for future studies. The parameters selected to assess oxidative stress and macrophage function yielded little useful information. Responses to instillation indicated little difference in potency per unit of combined particulate material and semivolatile organic compound mass between normal-emitter gasoline and diesel vehicles, or between emissions collected at different temperatures. However, equivalent masses of emissions from high-emitter vehicles of both types were more potent than those from normal-emitters. While preliminary in terms of assessing contributions of different emissions to health hazards, the results indicate that a subset of this panel of assays will be useful in providing rapid, cost-effective feedback on the biological impact of modified technology.

Discrimination Factors and Incorporation Rates for Organic Matrix in Shark Teeth Based on a Captive Feeding Study.

PubMed

Zeichner, S S; Colman, A S; Koch, P L; Polo-Silva, C; Galván-Magaña, F; Kim, S L

Sharks migrate annually over large distances and occupy a wide variety of habitats, complicating analysis of lifestyle and diet. A biogeochemical technique often used to reconstruct shark diet and environment preferences is stable isotope analysis, which is minimally invasive and integrates through time and space. There are previous studies that focus on isotopic analysis of shark soft tissues, but there are limited applications to shark teeth. However, shark teeth offer an advantage of multiple ecological snapshots and minimum invasiveness during removal because of their distinct conveyor belt tooth replacement system. In this study, we analyze δ 13 C and δ 15 N values of the organic matrix in leopard shark teeth (Triakis semifasciata) from a captive experiment and report discrimination factors as well as incorporation rates. We found differences in tooth discrimination factors for individuals fed different prey sources (mean ± SD; Δ 13 C squid = 4.7‰ ± 0.5‰, Δ 13 C tilapia = 3.1‰ ± 1.0‰, Δ 15 N squid = 2.0‰ ± 0.7‰, Δ 15 N tilapia = 2.8‰ ± 0.6‰). In addition, these values differed from previously published discrimination factors for plasma, red blood cells, and muscle of the same leopard sharks. Incorporation rates of shark teeth were similar for carbon and nitrogen (mean ± SE; λ C = 0.021 ± 0.009, λ N = 0.024 ± 0.007) and comparable to those of plasma. We emphasize the difference in biological parameters on the basis of tissue substrate and diet items to interpret stable isotope data and apply our results to stable isotope values from blue shark (Prionace glauca) teeth to illustrate the importance of biological parameters to interpret the complex ecology of a migratory shark.

Time series pCO2 at a coastal mooring: Internal consistency, seasonal cycles, and interannual variability

NASA Astrophysics Data System (ADS)

Reimer, Janet J.; Cai, Wei-Jun; Xue, Liang; Vargas, Rodrigo; Noakes, Scott; Hu, Xinping; Signorini, Sergio R.; Mathis, Jeremy T.; Feely, Richard A.; Sutton, Adrienne J.; Sabine, Christopher; Musielewicz, Sylvia; Chen, Baoshan; Wanninkhof, Rik

2017-08-01

Marine carbonate system monitoring programs often consist of multiple observational methods that include underway cruise data, moored autonomous time series, and discrete water bottle samples. Monitored parameters include all, or some of the following: partial pressure of CO2 of the water (pCO2w) and air, dissolved inorganic carbon (DIC), total alkalinity (TA), and pH. Any combination of at least two of the aforementioned parameters can be used to calculate the others. In this study at the Gray's Reef (GR) mooring in the South Atlantic Bight (SAB) we: examine the internal consistency of pCO2w from underway cruise, moored autonomous time series, and calculated from bottle samples (DIC-TA pairing); describe the seasonal to interannual pCO2w time series variability and air-sea flux (FCO2), as well as describe the potential sources of pCO2w variability; and determine the source/sink for atmospheric pCO2. Over the 8.5 years of GR mooring time series, mooring-underway and mooring-bottle calculated-pCO2w strongly correlate with r-values > 0.90. pCO2w and FCO2 time series follow seasonal thermal patterns; however, seasonal non-thermal processes, such as terrestrial export, net biological production, and air-sea exchange also influence variability. The linear slope of time series pCO2w increases by 5.2 ± 1.4 μatm y-1 with FCO2 increasing 51-70 mmol m-2 y-1. The net FCO2 sign can switch interannually with the magnitude varying greatly. Non-thermal pCO2w is also increasing over the time series, likely indicating that terrestrial export and net biological processes drive the long term pCO2w increase.

Development of Multiple-Frequency Ultrasonic Imaging System Using Multiple Resonance Piezoelectric Transducer

NASA Astrophysics Data System (ADS)

Akiyama, Iwaki; Yoshizumi, Natsuki; Saito, Shigemi; Wada, Yuji; Koyama, Daisuke; Nakamura, Kentaro

2012-07-01

The authors have developed a multiple frequency imaging system using a multiple resonance transducer (MRT) consisting of 1-3 composite materials with a low mechanical quality factor Q bonded together. The MRT has a structure consisting of thin and thick piezoelectric plates, two matching layers, and a backing layer. This makes it possible to obtain B-mode images of satisfactory resolution using ultrasonic pulses owing to their short duration. In this paper, the vibration property of the MRT derived through equivalent-circuit analysis is first shown. By utilizing the result, an MRT capable of transmitting ultrasonic pulses for generation of the images of biological tissues with satisfactory resolution is designed and prototyped. Setting the prototype transducer in the mechanical sector probe of commercial ultrasonic diagnosis equipment, the speckle reduction effect is demonstrated using images of various phantoms to mimic biological tissues and a human thyroid.

Functional comparison of microarray data across multiple platforms using the method of percentage of overlapping functions.

PubMed

Li, Zhiguang; Kwekel, Joshua C; Chen, Tao

2012-01-01

Functional comparison across microarray platforms is used to assess the comparability or similarity of the biological relevance associated with the gene expression data generated by multiple microarray platforms. Comparisons at the functional level are very important considering that the ultimate purpose of microarray technology is to determine the biological meaning behind the gene expression changes under a specific condition, not just to generate a list of genes. Herein, we present a method named percentage of overlapping functions (POF) and illustrate how it is used to perform the functional comparison of microarray data generated across multiple platforms. This method facilitates the determination of functional differences or similarities in microarray data generated from multiple array platforms across all the functions that are presented on these platforms. This method can also be used to compare the functional differences or similarities between experiments, projects, or laboratories.

An appraisal of biological responses and network of environmental interactions in non-mining and mining impacted coastal waters.

PubMed

Fernandes, Christabelle E G; Malik, Ashish; Jineesh, V K; Fernandes, Sheryl O; Das, Anindita; Pandey, Sunita S; Kanolkar, Geeta; Sujith, P P; Velip, Dhillan M; Shaikh, Shagufta; Helekar, Samita; Gonsalves, Maria Judith; Nair, Shanta; LokaBharathi, P A

2015-08-01

The coastal waters of Goa and Ratnagiri lying on the West coast of India are influenced by terrestrial influx. However, Goa is influenced anthropogenically by iron-ore mining, while Ratnagiri is influenced by deposition of heavy minerals containing iron brought from the hinterlands. We hypothesize that there could be a shift in biological response along with changes in network of interactions between environmental and biological variables in these mining and non-mining impacted regions, lying 160 nmi apart. Biological and environmental parameters were analyzed during pre-monsoon season. Except silicates, the measured parameters were higher at Goa and related significantly, suggesting bacteria centric, detritus-driven region. At Ratnagiri, phytoplankton biomass related positively with silicate suggesting a region dominated by primary producers. This dominance perhaps got reflected as a higher tertiary yield. Thus, even though the regions are geographically proximate, the different biological response could be attributed to the differences in the web of interactions between the measured variables.

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Bayesian multiple-source localization in an uncertain ocean environment.

PubMed

Dosso, Stan E; Wilmut, Michael J

2011-06-01

This paper considers simultaneous localization of multiple acoustic sources when properties of the ocean environment (water column and seabed) are poorly known. A Bayesian formulation is developed in which the environmental parameters, noise statistics, and locations and complex strengths (amplitudes and phases) of multiple sources are considered to be unknown random variables constrained by acoustic data and prior information. Two approaches are considered for estimating source parameters. Focalization maximizes the posterior probability density (PPD) over all parameters using adaptive hybrid optimization. Marginalization integrates the PPD using efficient Markov-chain Monte Carlo methods to produce joint marginal probability distributions for source ranges and depths, from which source locations are obtained. This approach also provides quantitative uncertainty analysis for all parameters, which can aid in understanding of the inverse problem and may be of practical interest (e.g., source-strength probability distributions). In both approaches, closed-form maximum-likelihood expressions for source strengths and noise variance at each frequency allow these parameters to be sampled implicitly, substantially reducing the dimensionality and difficulty of the inversion. Examples are presented of both approaches applied to single- and multi-frequency localization of multiple sources in an uncertain shallow-water environment, and a Monte Carlo performance evaluation study is carried out. © 2011 Acoustical Society of America

The Molecular Biology Capstone Assessment: A Concept Assessment for Upper-Division Molecular Biology Students

PubMed Central

Couch, Brian A.; Wood, William B.; Knight, Jennifer K.

2015-01-01

Measuring students’ conceptual understandings has become increasingly important to biology faculty members involved in evaluating and improving departmental programs. We developed the Molecular Biology Capstone Assessment (MBCA) to gauge comprehension of fundamental concepts in molecular and cell biology and the ability to apply these concepts in novel scenarios. Targeted at graduating students, the MBCA consists of 18 multiple-true/false (T/F) questions. Each question consists of a narrative stem followed by four T/F statements, which allows a more detailed assessment of student understanding than the traditional multiple-choice format. Questions were iteratively developed with extensive faculty and student feedback, including validation through faculty reviews and response validation through student interviews. The final assessment was taken online by 504 students in upper-division courses at seven institutions. Data from this administration indicate that the MBCA has acceptable levels of internal reliability (α = 0.80) and test–retest stability (r = 0.93). Students achieved a wide range of scores with a 67% overall average. Performance results suggest that students have an incomplete understanding of many molecular biology concepts and continue to hold incorrect conceptions previously documented among introductory-level students. By pinpointing areas of conceptual difficulty, the MBCA can provide faculty members with guidance for improving undergraduate biology programs. PMID:25713098

Multiple objects tracking in fluorescence microscopy.

PubMed

Kalaidzidis, Yannis

2009-01-01

Many processes in cell biology are connected to the movement of compact entities: intracellular vesicles and even single molecules. The tracking of individual objects is important for understanding cellular dynamics. Here we describe the tracking algorithms which have been developed in the non-biological fields and successfully applied to object detection and tracking in biological applications. The characteristics features of the different algorithms are compared.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations

PubMed Central

2012-01-01

Visualization and analysis of molecular networks are both central to systems biology. However, there still exists a large technological gap between them, especially when assessing multiple network levels or hierarchies. Here we present RedeR, an R/Bioconductor package combined with a Java core engine for representing modular networks. The functionality of RedeR is demonstrated in two different scenarios: hierarchical and modular organization in gene co-expression networks and nested structures in time-course gene expression subnetworks. Our results demonstrate RedeR as a new framework to deal with the multiple network levels that are inherent to complex biological systems. RedeR is available from http://bioconductor.org/packages/release/bioc/html/RedeR.html. PMID:22531049

An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation

NASA Astrophysics Data System (ADS)

Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng

2016-09-01

This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.