Evaluation of iodide deficiency in the lactating rat and pup using a biologically based dose response (BBDR) Model***

EPA Science Inventory

A biologically-based dose response (BBDR) model for the hypothalamic-pituitary thyroid (HPT) axis in the lactating rat and nursing pup was developed to describe the perturbations caused by iodide deficiency on the 1-IPT axis. Model calibrations, carried out by adjusting key model...

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.

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

PubMed

Roehner, Nicholas; Zhang, Zhen; Nguyen, Tramy; Myers, Chris J

2015-08-21

In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

The NASA Space Radiobiology Risk Assessment Project

NASA Astrophysics Data System (ADS)

Cucinotta, Francis A.; Huff, Janice; Ponomarev, Artem; Patel, Zarana; Kim, Myung-Hee

The current first phase (2006-2011) has the three major goals of: 1) optimizing the conventional cancer risk models currently used based on the double-detriment life-table and radiation quality functions; 2) the integration of biophysical models of acute radiation syndromes; and 3) the development of new systems radiation biology models of cancer processes. The first-phase also includes continued uncertainty assessment of space radiation environmental models and transport codes, and relative biological effectiveness factors (RBE) based on flight data and NSRL results, respectively. The second phase of the (2012-2016) will: 1) develop biophysical models of central nervous system risks (CNS); 2) achieve comphrensive systems biology models of cancer processes using data from proton and heavy ion studies performed at NSRL; and 3) begin to identify computational models of biological countermeasures. Goals for the third phase (2017-2021) include: 1) the development of a systems biology model of cancer risks for operational use at NASA; 2) development of models of degenerative risks, 2) quantitative models of counter-measure impacts on cancer risks; and 3) indiviudal based risk assessments. Finally, we will support a decision point to continue NSRL research in support of NASA's exploration goals beyond 2021, and create an archival of NSRL research results for continued analysis. Details on near term goals, plans for a WEB based data resource of NSRL results, and a space radiation Wikepedia are described.

Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems

PubMed Central

Cilfone, Nicholas A.; Kirschner, Denise E.; Linderman, Jennifer J.

2015-01-01

Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level. PMID:26366228

On Crowd-verification of Biological Networks

PubMed Central

Ansari, Sam; Binder, Jean; Boue, Stephanie; Di Fabio, Anselmo; Hayes, William; Hoeng, Julia; Iskandar, Anita; Kleiman, Robin; Norel, Raquel; O’Neel, Bruce; Peitsch, Manuel C.; Poussin, Carine; Pratt, Dexter; Rhrissorrakrai, Kahn; Schlage, Walter K.; Stolovitzky, Gustavo; Talikka, Marja

2013-01-01

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community. PMID:24151423

Web-based applications for building, managing and analysing kinetic models of biological systems.

PubMed

Lee, Dong-Yup; Saha, Rajib; Yusufi, Faraaz Noor Khan; Park, Wonjun; Karimi, Iftekhar A

2009-01-01

Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.

Coupling biology and oceanography in models.

PubMed

Fennel, W; Neumann, T

2001-08-01

The dynamics of marine ecosystems, i.e. the changes of observable chemical-biological quantities in space and time, are driven by biological and physical processes. Predictions of future developments of marine systems need a theoretical framework, i.e. models, solidly based on research and understanding of the different processes involved. The natural way to describe marine systems theoretically seems to be the embedding of chemical-biological models into circulation models. However, while circulation models are relatively advanced the quantitative theoretical description of chemical-biological processes lags behind. This paper discusses some of the approaches and problems in the development of consistent theories and indicates the beneficial potential of the coupling of marine biology and oceanography in models.

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

EPA Science Inventory

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...

A MULTISTAGE BIOLOGICALLY BASED MATHEMATICAL MODEL FOR MOUSE LIVER TUMORS INDUCED BY DICHLOROACETIC ACID (DCA) - EXPLORATION OF THE MODEL

EPA Science Inventory

A biologically based mathematical model for the induction of liver tumors in mice by dichloroacetic acid (DCA) has been developed from histopathologic analysis of the livers of exposed mice. This analysis suggests that following chronic exposure to DCA, carcinomas can arise dire...

Agent-based modelling in synthetic biology.

PubMed

Gorochowski, Thomas E

2016-11-30

Biological systems exhibit complex behaviours that emerge at many different levels of organization. These span the regulation of gene expression within single cells to the use of quorum sensing to co-ordinate the action of entire bacterial colonies. Synthetic biology aims to make the engineering of biology easier, offering an opportunity to control natural systems and develop new synthetic systems with useful prescribed behaviours. However, in many cases, it is not understood how individual cells should be programmed to ensure the emergence of a required collective behaviour. Agent-based modelling aims to tackle this problem, offering a framework in which to simulate such systems and explore cellular design rules. In this article, I review the use of agent-based models in synthetic biology, outline the available computational tools, and provide details on recently engineered biological systems that are amenable to this approach. I further highlight the challenges facing this methodology and some of the potential future directions. © 2016 The Author(s).

Methods for improving simulations of biological systems: systemic computation and fractal proteins

PubMed Central

Bentley, Peter J.

2009-01-01

Modelling and simulation are becoming essential for new fields such as synthetic biology. Perhaps the most important aspect of modelling is to follow a clear design methodology that will help to highlight unwanted deficiencies. The use of tools designed to aid the modelling process can be of benefit in many situations. In this paper, the modelling approach called systemic computation (SC) is introduced. SC is an interaction-based language, which enables individual-based expression and modelling of biological systems, and the interactions between them. SC permits a precise description of a hypothetical mechanism to be written using an intuitive graph-based or a calculus-based notation. The same description can then be directly run as a simulation, merging the hypothetical mechanism and the simulation into the same entity. However, even when using well-designed modelling tools to produce good models, the best model is not always the most accurate one. Frequently, computational constraints or lack of data make it infeasible to model an aspect of biology. Simplification may provide one way forward, but with inevitable consequences of decreased accuracy. Instead of attempting to replace an element with a simpler approximation, it is sometimes possible to substitute the element with a different but functionally similar component. In the second part of this paper, this modelling approach is described and its advantages are summarized using an exemplar: the fractal protein model. Finally, the paper ends with a discussion of good biological modelling practice by presenting lessons learned from the use of SC and the fractal protein model. PMID:19324681

Long-Term Conceptual Retrieval by College Biology Majors Following Model-Based Instruction

ERIC Educational Resources Information Center

Dauer, Joseph T.; Long, Tammy M.

2015-01-01

One of the goals of college-level introductory biology is to establish a foundation of knowledge and skills that can be built upon throughout a biology curriculum. In a reformed introductory biology course, we used iterative model construction as a pedagogical tool to promote students' understanding about conceptual connections, particularly those…

ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra

PubMed Central

2011-01-01

Background Many biological systems are modeled qualitatively with discrete models, such as probabilistic Boolean networks, logical models, Petri nets, and agent-based models, to gain a better understanding of them. The computational complexity to analyze the complete dynamics of these models grows exponentially in the number of variables, which impedes working with complex models. There exist software tools to analyze discrete models, but they either lack the algorithmic functionality to analyze complex models deterministically or they are inaccessible to many users as they require understanding the underlying algorithm and implementation, do not have a graphical user interface, or are hard to install. Efficient analysis methods that are accessible to modelers and easy to use are needed. Results We propose a method for efficiently identifying attractors and introduce the web-based tool Analysis of Dynamic Algebraic Models (ADAM), which provides this and other analysis methods for discrete models. ADAM converts several discrete model types automatically into polynomial dynamical systems and analyzes their dynamics using tools from computer algebra. Specifically, we propose a method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra. Based on extensive experimentation with both discrete models arising in systems biology and randomly generated networks, we found that the algebraic algorithms presented in this manuscript are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness. For a large set of published complex discrete models, ADAM identified the attractors in less than one second. Conclusions Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics. PMID:21774817

BIOCOMPUTATION: some history and prospects.

PubMed

Cull, Paul

2013-06-01

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Virtual Tissues and Developmental Systems Biology (book chapter)

EPA Science Inventory

Virtual tissue (VT) models provide an in silico environment to simulate cross-scale properties in specific tissues or organs based on knowledge of the underlying biological networks. These integrative models capture the fundamental interactions in a biological system and enable ...

Trait-Dependent Biogeography: (Re)Integrating Biology into Probabilistic Historical Biogeographical Models.

PubMed

Sukumaran, Jeet; Knowles, L Lacey

2018-06-01

The development of process-based probabilistic models for historical biogeography has transformed the field by grounding it in modern statistical hypothesis testing. However, most of these models abstract away biological differences, reducing species to interchangeable lineages. We present here the case for reintegration of biology into probabilistic historical biogeographical models, allowing a broader range of questions about biogeographical processes beyond ancestral range estimation or simple correlation between a trait and a distribution pattern, as well as allowing us to assess how inferences about ancestral ranges themselves might be impacted by differential biological traits. We show how new approaches to inference might cope with the computational challenges resulting from the increased complexity of these trait-based historical biogeographical models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of a GCR Event-based Risk Model

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Ponomarev, Artem L.; Plante, Ianik; Carra, Claudio; Kim, Myung-Hee

2009-01-01

A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how GCR event rates mapped to biological signaling induction and relaxation times. We considered several hypotheses related to signaling and cancer risk, and then performed simulations for conditions where aberrant or adaptive signaling would occur on long-duration space mission. Our results do not support the conventional assumptions of dose, linearity and additivity. A discussion on how event-based systems biology models, which focus on biological signaling as the mechanism to propagate damage or adaptation, can be further developed for cancer and CNS space radiation risk projections is given.

HOW CAN BIOLOGICALLY-BASED MODELING OF ARSENIC KINETICS AND DYNAMICS INFORM THE RISK ASSESSMENT PROCESS?

EPA Science Inventory

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic met...

Systematic analysis of signaling pathways using an integrative environment.

PubMed

Visvanathan, Mahesh; Breit, Marc; Pfeifer, Bernhard; Baumgartner, Christian; Modre-Osprian, Robert; Tilg, Bernhard

2007-01-01

Understanding the biological processes of signaling pathways as a whole system requires an integrative software environment that has comprehensive capabilities. The environment should include tools for pathway design, visualization, simulation and a knowledge base concerning signaling pathways as one. In this paper we introduce a new integrative environment for the systematic analysis of signaling pathways. This system includes environments for pathway design, visualization, simulation and a knowledge base that combines biological and modeling information concerning signaling pathways that provides the basic understanding of the biological system, its structure and functioning. The system is designed with a client-server architecture. It contains a pathway designing environment and a simulation environment as upper layers with a relational knowledge base as the underlying layer. The TNFa-mediated NF-kB signal trans-duction pathway model was designed and tested using our integrative framework. It was also useful to define the structure of the knowledge base. Sensitivity analysis of this specific pathway was performed providing simulation data. Then the model was extended showing promising initial results. The proposed system offers a holistic view of pathways containing biological and modeling data. It will help us to perform biological interpretation of the simulation results and thus contribute to a better understanding of the biological system for drug identification.

SPARK: A Framework for Multi-Scale Agent-Based Biomedical Modeling.

PubMed

Solovyev, Alexey; Mikheev, Maxim; Zhou, Leming; Dutta-Moscato, Joyeeta; Ziraldo, Cordelia; An, Gary; Vodovotz, Yoram; Mi, Qi

2010-01-01

Multi-scale modeling of complex biological systems remains a central challenge in the systems biology community. A method of dynamic knowledge representation known as agent-based modeling enables the study of higher level behavior emerging from discrete events performed by individual components. With the advancement of computer technology, agent-based modeling has emerged as an innovative technique to model the complexities of systems biology. In this work, the authors describe SPARK (Simple Platform for Agent-based Representation of Knowledge), a framework for agent-based modeling specifically designed for systems-level biomedical model development. SPARK is a stand-alone application written in Java. It provides a user-friendly interface, and a simple programming language for developing Agent-Based Models (ABMs). SPARK has the following features specialized for modeling biomedical systems: 1) continuous space that can simulate real physical space; 2) flexible agent size and shape that can represent the relative proportions of various cell types; 3) multiple spaces that can concurrently simulate and visualize multiple scales in biomedical models; 4) a convenient graphical user interface. Existing ABMs of diabetic foot ulcers and acute inflammation were implemented in SPARK. Models of identical complexity were run in both NetLogo and SPARK; the SPARK-based models ran two to three times faster.

Logic-Based Models for the Analysis of Cell Signaling Networks†

PubMed Central

2010-01-01

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. PMID:20225868

Agent-based modeling: case study in cleavage furrow models.

PubMed

Mogilner, Alex; Manhart, Angelika

2016-11-07

The number of studies in cell biology in which quantitative models accompany experiments has been growing steadily. Roughly, mathematical and computational techniques of these models can be classified as "differential equation based" (DE) or "agent based" (AB). Recently AB models have started to outnumber DE models, but understanding of AB philosophy and methodology is much less widespread than familiarity with DE techniques. Here we use the history of modeling a fundamental biological problem-positioning of the cleavage furrow in dividing cells-to explain how and why DE and AB models are used. We discuss differences, advantages, and shortcomings of these two approaches. © 2016 Mogilner and Manhart. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication 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).

The quest for a new modelling framework in mathematical biology. Comment on "On the interplay between mathematics and biology: Hallmarks towards a new systems biology" by N. Bellomo et al.

NASA Astrophysics Data System (ADS)

Eftimie, Raluca

2015-03-01

One of the main unsolved problems of modern physics is finding a "theory of everything" - a theory that can explain, with the help of mathematics, all physical aspects of the universe. While the laws of physics could explain some aspects of the biology of living systems (e.g., the phenomenological interpretation of movement of cells and animals), there are other aspects specific to biology that cannot be captured by physics models. For example, it is generally accepted that the evolution of a cell-based system is influenced by the activation state of cells (e.g., only activated and functional immune cells can fight diseases); on the other hand, the evolution of an animal-based system can be influenced by the psychological state (e.g., distress) of animals. Therefore, the last 10-20 years have seen also a quest for a "theory of everything"-approach extended to biology, with researchers trying to propose mathematical modelling frameworks that can explain various biological phenomena ranging from ecology to developmental biology and medicine [1,2,6]. The basic idea behind this approach can be found in a few reviews on ecology and cell biology [6,7,9-11], where researchers suggested that due to the parallel between the micro-scale dynamics and the emerging macro-scale phenomena in both cell biology and in ecology, many mathematical methods used for ecological processes could be adapted to cancer modelling [7,9] or to modelling in immunology [11]. However, this approach generally involved the use of different models to describe different biological aspects (e.g., models for cell and animal movement, models for competition between cells or animals, etc.).

Capturing microscopic features of bone remodeling into a macroscopic model based on biological rationales of bone adaptation.

PubMed

Kim, Young Kwan; Kameo, Yoshitaka; Tanaka, Sakae; Adachi, Taiji

2017-10-01

To understand Wolff's law, bone adaptation by remodeling at the cellular and tissue levels has been discussed extensively through experimental and simulation studies. For the clinical application of a bone remodeling simulation, it is significant to establish a macroscopic model that incorporates clarified microscopic mechanisms. In this study, we proposed novel macroscopic models based on the microscopic mechanism of osteocytic mechanosensing, in which the flow of fluid in the lacuno-canalicular porosity generated by fluid pressure gradients plays an important role, and theoretically evaluated the proposed models, taking biological rationales of bone adaptation into account. The proposed models were categorized into two groups according to whether the remodeling equilibrium state was defined globally or locally, i.e., the global or local uniformity models. Each remodeling stimulus in the proposed models was quantitatively evaluated through image-based finite element analyses of a swine cancellous bone, according to two introduced criteria associated with the trabecular volume and orientation at remodeling equilibrium based on biological rationales. The evaluation suggested that nonuniformity of the mean stress gradient in the local uniformity model, one of the proposed stimuli, has high validity. Furthermore, the adaptive potential of each stimulus was discussed based on spatial distribution of a remodeling stimulus on the trabecular surface. The theoretical consideration of a remodeling stimulus based on biological rationales of bone adaptation would contribute to the establishment of a clinically applicable and reliable simulation model of bone remodeling.

How Can Biologically-Based Modeling of Arsenic Kinetics and Dynamics Inform the Risk Assessment Process? -- ETD

EPA Science Inventory

Quantitative biologically-based models describing key events in the continuum from arsenic exposure to the development of adverse health effects provide a framework to integrate information obtained across diverse research areas. For example, genetic polymorphisms in arsenic me...

A Model Inquiry-Based Genetics Experiment for Introductory Biology Students: Screening for Enhancers & Suppressors of Ptpmeg

ERIC Educational Resources Information Center

Setty, Sumana; Kosinski-Collins, Melissa S.

2015-01-01

It has been noted that undergraduate project-based laboratories lead to increased interest in scientific research and student understanding of biological concepts. We created a novel, inquiry-based, multiweek genetics research project studying Ptpmeg, for the Introductory Biology Laboratory course at Brandeis University. Ptpmeg is a protein…

Illustrations of mathematical modeling in biology: epigenetics, meiosis, and an outlook.

PubMed

Richards, D; Berry, S; Howard, M

2012-01-01

In the past few years, mathematical modeling approaches in biology have begun to fulfill their promise by assisting in the dissection of complex biological systems. Here, we review two recent examples of predictive mathematical modeling in plant biology. The first involves the quantitative epigenetic silencing of the floral repressor gene FLC in Arabidopsis, mediated by a Polycomb-based system. The second involves the spatiotemporal dynamics of telomere bouquet formation in wheat-rye meiosis. Although both the biology and the modeling framework of the two systems are different, both exemplify how mathematical modeling can help to accelerate discovery of the underlying mechanisms in complex biological systems. In both cases, the models that developed were relatively minimal, including only essential features, but both nevertheless yielded fundamental insights. We also briefly review the current state of mathematical modeling in biology, difficulties inherent in its application, and its potential future development.

A hybrid agent-based approach for modeling microbiological systems.

PubMed

Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

2008-11-21

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.

Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology

PubMed Central

Quillin, Kim; Thomas, Stephen

2015-01-01

The drawing of visual representations is important for learners and scientists alike, such as the drawing of models to enable visual model-based reasoning. Yet few biology instructors recognize drawing as a teachable science process skill, as reflected by its absence in the Vision and Change report’s Modeling and Simulation core competency. Further, the diffuse research on drawing can be difficult to access, synthesize, and apply to classroom practice. We have created a framework of drawing-to-learn that defines drawing, categorizes the reasons for using drawing in the biology classroom, and outlines a number of interventions that can help instructors create an environment conducive to student drawing in general and visual model-based reasoning in particular. The suggested interventions are organized to address elements of affect, visual literacy, and visual model-based reasoning, with specific examples cited for each. Further, a Blooming tool for drawing exercises is provided, as are suggestions to help instructors address possible barriers to implementing and assessing drawing-to-learn in the classroom. Overall, the goal of the framework is to increase the visibility of drawing as a skill in biology and to promote the research and implementation of best practices. PMID:25713094

Peer Review for EPA’s Biologically Based Dose-Response (BBDR) Model for Perchlorate

EPA Science Inventory

EPA is developing a regulation for perchlorate in drinking water. As part the regulatory process EPA must develop a Maximum Contaminant Level Goal (MCLG). FDA and EPA scientists developed a biologically based dose-response (BBDR) model to assist in deriving the MCLG. This mode...

EVALUATION OF BIOLOGICALLY BASED DOSE-RESPONSE MODELING FOR DEVELOPMENTAL TOXICITY: A WORKSHOP REPORT

EPA Science Inventory

Evaluation of biologically based dose-response modeling for developmental toxicity: a workshop report.

Lau C, Andersen ME, Crawford-Brown DJ, Kavlock RJ, Kimmel CA, Knudsen TB, Muneoka K, Rogers JM, Setzer RW, Smith G, Tyl R.

Reproductive Toxicology Division, NHEERL...

Development of a biologically based dose response (BBDR) model for arsenic induced cancer

EPA Science Inventory

We are developing a biologically based dose response (BBDR) model for arsenic carcinogenicity in order to reduce uncertainty in estimates of low dose risk by maximizing the use of relevant data on the mode of action. Expert consultation and literature review are being conducted t...

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

Completing and Adapting Models of Biological Processes

NASA Technical Reports Server (NTRS)

Margaria, Tiziana; Hinchey, Michael G.; Raffelt, Harald; Rash, James L.; Rouff, Christopher A.; Steffen, Bernhard

2006-01-01

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.

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.

Evaluation and modeling of HIV based on communication theory in biological systems.

PubMed

Dong, Miaowu; Li, Wenrong; Xu, Xi

2016-12-01

Some forms of communication are used in biological systems such as HIV transmission in human beings. In this paper, we plan to get a unique insight into biological communication systems generally through the analogy between HIV infection and electrical communication system. The model established in this paper can be used to test and simulate various communication systems since it provides researchers with an opportunity. We interpret biological communication systems by using telecommunications exemplification from a layered communication protocol developed before and use the model to indicate HIV spreading. We also implement a simulation of HIV infection based on the layered communication protocol to predict the development of this disease and the results prove the validity of the model. Copyright © 2016 Elsevier B.V. All rights reserved.

Hemojuvelin-hepcidin axis modeled and analyzed using Petri nets.

PubMed

Formanowicz, Dorota; Kozak, Adam; Głowacki, Tomasz; Radom, Marcin; Formanowicz, Piotr

2013-12-01

Systems biology approach to investigate biological phenomena seems to be very promising because it is capable to capture one of the fundamental properties of living organisms, i.e. their inherent complexity. It allows for analysis biological entities as complex systems of interacting objects. The first and necessary step of such an analysis is building a precise model of the studied biological system. This model is expressed in the language of some branch of mathematics, as for example, differential equations. During the last two decades the theory of Petri nets has appeared to be very well suited for building models of biological systems. The structure of these nets reflects the structure of interacting biological molecules and processes. Moreover, on one hand, Petri nets have intuitive graphical representation being very helpful in understanding the structure of the system and on the other hand, there is a lot of mathematical methods and software tools supporting an analysis of the properties of the nets. In this paper a Petri net based model of the hemojuvelin-hepcidin axis involved in the maintenance of the human body iron homeostasis is presented. The analysis based mainly on T-invariants of the model properties has been made and some biological conclusions have been drawn. Copyright © 2013 Elsevier Inc. All rights reserved.

Field Markup Language: biological field representation in XML.

PubMed

Chang, David; Lovell, Nigel H; Dokos, Socrates

2007-01-01

With an ever increasing number of biological models available on the internet, a standardized modeling framework is required to allow information to be accessed or visualized. Based on the Physiome Modeling Framework, the Field Markup Language (FML) is being developed to describe and exchange field information for biological models. In this paper, we describe the basic features of FML, its supporting application framework and its ability to incorporate CellML models to construct tissue-scale biological models. As a typical application example, we present a spatially-heterogeneous cardiac pacemaker model which utilizes both FML and CellML to describe and solve the underlying equations of electrical activation and propagation.

Drawing-to-learn: a framework for using drawings to promote model-based reasoning in biology.

PubMed

Quillin, Kim; Thomas, Stephen

2015-03-02

The drawing of visual representations is important for learners and scientists alike, such as the drawing of models to enable visual model-based reasoning. Yet few biology instructors recognize drawing as a teachable science process skill, as reflected by its absence in the Vision and Change report's Modeling and Simulation core competency. Further, the diffuse research on drawing can be difficult to access, synthesize, and apply to classroom practice. We have created a framework of drawing-to-learn that defines drawing, categorizes the reasons for using drawing in the biology classroom, and outlines a number of interventions that can help instructors create an environment conducive to student drawing in general and visual model-based reasoning in particular. The suggested interventions are organized to address elements of affect, visual literacy, and visual model-based reasoning, with specific examples cited for each. Further, a Blooming tool for drawing exercises is provided, as are suggestions to help instructors address possible barriers to implementing and assessing drawing-to-learn in the classroom. Overall, the goal of the framework is to increase the visibility of drawing as a skill in biology and to promote the research and implementation of best practices. © 2015 K. Quillin and S. Thomas. CBE—Life Sciences Education © 2015 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).

Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

PubMed

Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

2012-08-01

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

The Use of Bayesian Methods for Uncertainty Analysis and Evaluation of Biological Hypotheses in PBPK Models

EPA Science Inventory

Physiologically based pharmacokinetic (PBPK) models are compartmental models that describe the uptake and distribution of drugs and chemicals throughout the body. They can be structured so that model parameters (i.e., physiological and chemical-specific) reflect biological charac...

Two Project-Based Strategies in an Interdisciplinary Mathematical Modeling in Biology Course

ERIC Educational Resources Information Center

Ludwig, Patrice; Tongen, Anthony; Walton, Brian

2018-01-01

James Madison University faculty team-teach an interdisciplinary mathematical modeling course for mathematics and biology students. We have used two different project-based approaches to emphasize the mathematical concepts taught in class, while also exposing students to new areas of mathematics not formally covered in class. The first method…

Prototype Biology-Based Radiation Risk Module Project

NASA Technical Reports Server (NTRS)

Terrier, Douglas; Clayton, Ronald G.; Patel, Zarana; Hu, Shaowen; Huff, Janice

2015-01-01

Biological effects of space radiation and risk mitigation are strategic knowledge gaps for the Evolvable Mars Campaign. The current epidemiology-based NASA Space Cancer Risk (NSCR) model contains large uncertainties (HAT #6.5a) due to lack of information on the radiobiology of galactic cosmic rays (GCR) and lack of human data. The use of experimental models that most accurately replicate the response of human tissues is critical for precision in risk projections. Our proposed study will compare DNA damage, histological, and cell kinetic parameters after irradiation in normal 2D human cells versus 3D tissue models, and it will use a multi-scale computational model (CHASTE) to investigate various biological processes that may contribute to carcinogenesis, including radiation-induced cellular signaling pathways. This cross-disciplinary work, with biological validation of an evolvable mathematical computational model, will help reduce uncertainties within NSCR and aid risk mitigation for radiation-induced carcinogenesis.

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.

Mathematical model of biological order state or syndrome in traditional Chinese medicine: based on electromagnetic radiation within the human body.

PubMed

Han, Jinxiang; Huang, Jinzhao

2012-03-01

In this study, based on the resonator model and exciplex model of electromagnetic radiation within the human body, mathematical model of biological order state, also referred to as syndrome in traditional Chinese medicine, was established and expressed as: "Sy = v/ 1n(6I + 1)". This model provides the theoretical foundation for experimental research addressing the order state of living system, especially the quantitative research syndrome in traditional Chinese medicine.

Methodological challenges of optical tweezers-based X-ray fluorescence imaging of biological model organisms at synchrotron facilities.

PubMed

Vergucht, Eva; Brans, Toon; Beunis, Filip; Garrevoet, Jan; Bauters, Stephen; De Rijcke, Maarten; Deruytter, David; Janssen, Colin; Riekel, Christian; Burghammer, Manfred; Vincze, Laszlo

2015-07-01

Recently, a radically new synchrotron radiation-based elemental imaging approach for the analysis of biological model organisms and single cells in their natural in vivo state was introduced. The methodology combines optical tweezers (OT) technology for non-contact laser-based sample manipulation with synchrotron radiation confocal X-ray fluorescence (XRF) microimaging for the first time at ESRF-ID13. The optical manipulation possibilities and limitations of biological model organisms, the OT setup developments for XRF imaging and the confocal XRF-related challenges are reported. In general, the applicability of the OT-based setup is extended with the aim of introducing the OT XRF methodology in all research fields where highly sensitive in vivo multi-elemental analysis is of relevance at the (sub)micrometre spatial resolution level.

In Situ Biological Treatment Test at Kelly Air Force Base. Volume 2. Field Test Results and Cost Model

DTIC Science & Technology

1987-07-01

Groundwater." Developments in Industrial Microbiology, Volume 24, pp. 225-234. Society of Industrial Microbiology, Arlington, Virginia. 18. Product ...ESL-TR-85-52 cv) VOLUME II CN IN SITU BIOLOGICAL TREATMENT TEST AT KELLY AIR FORCE BASE, VOLUME !1: FIELD TEST RESULTS AND COST MODEL R.S. WETZEL...Kelly Air Force Base, Volume II: Field Test Results and Cost Model (UNCLASSIFIED) 12 PERSONAL AUTHOR(S) Roger S. Wetzel, Connie M. Durst, Donald H

Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Wei, Xile; Deng, Bin; Liu, Chen; Li, Huiyan; Wang, Jiang

2018-03-01

Balance between biological plausibility of dynamical activities and computational efficiency is one of challenging problems in computational neuroscience and neural system engineering. This paper proposes a set of efficient methods for the hardware realization of the conductance-based neuron model with relevant dynamics, targeting reproducing the biological behaviors with low-cost implementation on digital programmable platform, which can be applied in wide range of conductance-based neuron models. Modified GP neuron models for efficient hardware implementation are presented to reproduce reliable pallidal dynamics, which decode the information of basal ganglia and regulate the movement disorder related voluntary activities. Implementation results on a field-programmable gate array (FPGA) demonstrate that the proposed techniques and models can reduce the resource cost significantly and reproduce the biological dynamics accurately. Besides, the biological behaviors with weak network coupling are explored on the proposed platform, and theoretical analysis is also made for the investigation of biological characteristics of the structured pallidal oscillator and network. The implementation techniques provide an essential step towards the large-scale neural network to explore the dynamical mechanisms in real time. Furthermore, the proposed methodology enables the FPGA-based system a powerful platform for the investigation on neurodegenerative diseases and real-time control of bio-inspired neuro-robotics.

A Best-Practice Model for Academic Advising of University Biology Majors

ERIC Educational Resources Information Center

Heekin, Jonathan Ralph Calvin

2013-01-01

Biology faculty at an East Coast university believed their undergraduate students were not being well served by the existing academic advising program. The purpose of this mixed methods project study was to evaluate the effectiveness of the academic advising model in a biology department. Guided by system-based organizational theory, a learning…

OWL reasoning framework over big biological knowledge network.

PubMed

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

OWL Reasoning Framework over Big Biological Knowledge Network

PubMed Central

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

Zebrafish Whole-Adult-Organism Chemogenomics for Large-Scale Predictive and Discovery Chemical Biology

PubMed Central

Lam, Siew Hong; Mathavan, Sinnakarupan; Tong, Yan; Li, Haixia; Karuturi, R. Krishna Murthy; Wu, Yilian; Vega, Vinsensius B.; Liu, Edison T.; Gong, Zhiyuan

2008-01-01

The ability to perform large-scale, expression-based chemogenomics on whole adult organisms, as in invertebrate models (worm and fly), is highly desirable for a vertebrate model but its feasibility and potential has not been demonstrated. We performed expression-based chemogenomics on the whole adult organism of a vertebrate model, the zebrafish, and demonstrated its potential for large-scale predictive and discovery chemical biology. Focusing on two classes of compounds with wide implications to human health, polycyclic (halogenated) aromatic hydrocarbons [P(H)AHs] and estrogenic compounds (ECs), we generated robust prediction models that can discriminate compounds of the same class from those of different classes in two large independent experiments. The robust expression signatures led to the identification of biomarkers for potent aryl hydrocarbon receptor (AHR) and estrogen receptor (ER) agonists, respectively, and were validated in multiple targeted tissues. Knowledge-based data mining of human homologs of zebrafish genes revealed highly conserved chemical-induced biological responses/effects, health risks, and novel biological insights associated with AHR and ER that could be inferred to humans. Thus, our study presents an effective, high-throughput strategy of capturing molecular snapshots of chemical-induced biological states of a whole adult vertebrate that provides information on biomarkers of effects, deregulated signaling pathways, and possible affected biological functions, perturbed physiological systems, and increased health risks. These findings place zebrafish in a strategic position to bridge the wide gap between cell-based and rodent models in chemogenomics research and applications, especially in preclinical drug discovery and toxicology. PMID:18618001

Deliberative Pedagogy in a Nonmajors Biology Course: Active Learning That Promotes Student Engagement with Science Policy and Research

ERIC Educational Resources Information Center

Weasel, Lisa H.; Finkel, Liza

2016-01-01

Deliberative democracy, a consensus model of decision making, has been used in real-life policy making involving controversial, science-related issues to increase citizen participation and engagement. Here, we describe a pedagogical approach based on this model implemented in a large, lecture-based, nonmajors introductory biology course at an…

Modeling Co-evolution of Speech and Biology.

PubMed

de Boer, Bart

2016-04-01

Two computer simulations are investigated that model interaction of cultural evolution of language and biological evolution of adaptations to language. Both are agent-based models in which a population of agents imitates each other using realistic vowels. The agents evolve under selective pressure for good imitation. In one model, the evolution of the vocal tract is modeled; in the other, a cognitive mechanism for perceiving speech accurately is modeled. In both cases, biological adaptations to using and learning speech evolve, even though the system of speech sounds itself changes at a more rapid time scale than biological evolution. However, the fact that the available acoustic space is used maximally (a self-organized result of cultural evolution) is constant, and therefore biological evolution does have a stable target. This work shows that when cultural and biological traits are continuous, their co-evolution may lead to cognitive adaptations that are strong enough to detect empirically. Copyright © 2016 Cognitive Science Society, Inc.

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery.

PubMed

Bosl, William J

2007-02-15

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

Identifying biological concepts from a protein-related corpus with a probabilistic topic model

PubMed Central

Zheng, Bin; McLean, David C; Lu, Xinghua

2006-01-01

Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text. PMID:16466569

A versatile petri net based architecture for modeling and simulation of complex biological processes.

PubMed

Nagasaki, Masao; Doi, Atsushi; Matsuno, Hiroshi; Miyano, Satoru

2004-01-01

The research on modeling and simulation of complex biological systems is getting more important in Systems Biology. In this respect, we have developed Hybrid Function Petri net (HFPN) that was newly developed from existing Petri net because of their intuitive graphical representation and their capabilities for mathematical analyses. However, in the process of modeling metabolic, gene regulatory or signal transduction pathways with the architecture, we have realized three extensions of HFPN, (i) an entity should be extended to contain more than one value, (ii) an entity should be extended to handle other primitive types, e.g. boolean, string, (iii) an entity should be extended to handle more advanced type called object that consists of variables and methods, are necessary for modeling biological systems with Petri net based architecture. To deal with it, we define a new enhanced Petri net called hybrid functional Petri net with extension (HFPNe). To demonstrate the effectiveness of the enhancements, we model and simulate with HFPNe four biological processes that are diffcult to represent with the previous architecture HFPN.

Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

PubMed

Stoll, Gautier; Viara, Eric; Barillot, Emmanuel; Calzone, Laurence

2012-08-29

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions. Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.

Using a biology-based model (DEBtox) to analyze bioassays in ecotoxicology: opportunities and recommendations.

PubMed

Péry, Alexandre R R; Flammarion, Patrick; Vollat, Bernard; Bedaux, Jacques J M; Kooijman, Sebastiaan A L M; Garric, Jeanne

2002-02-01

The conventional analysis of bioassays does not account for biological significance. However, mathematical models do exist that are realistic from a biological point of view and describe toxicokinetics and effects on test organisms of chemical compounds. Here we studied a biology-based model (DEBtox) that provides an estimate of a no-effect concentration, and we demonstrated the ability of such a model to adapt to different situations. We showed that the basic model can be extended to deal with problems usually faced during bioassays like time-varying concentrations or unsuitable choices of initial concentrations. To reach this goal, we report experimental data from Daphnia magna exposed to zinc. These data also showed the potential benefit of the model in understanding the influence of food on toxicity. We finally make some recommendations about the choice of initial concentrations, and we propose a test with a depuration period to check the relevance and the predictive capacity of the DEBtox model. In our experiments, the model performed well and proved its usefulness as a tool in risk assessment.

Using the Unified Modelling Language (UML) to guide the systemic description of biological processes and systems.

PubMed

Roux-Rouquié, Magali; Caritey, Nicolas; Gaubert, Laurent; Rosenthal-Sabroux, Camille

2004-07-01

One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours.

Logic-based models in systems biology: a predictive and parameter-free network analysis method†

PubMed Central

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

2012-01-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

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

Modeling languages for biochemical network simulation: reaction vs equation based approaches.

PubMed

Wiechert, Wolfgang; Noack, Stephan; Elsheikh, Atya

2010-01-01

Biochemical network modeling and simulation is an essential task in any systems biology project. The systems biology markup language (SBML) was established as a standardized model exchange language for mechanistic models. A specific strength of SBML is that numerous tools for formulating, processing, simulation and analysis of models are freely available. Interestingly, in the field of multidisciplinary simulation, the problem of model exchange between different simulation tools occurred much earlier. Several general modeling languages like Modelica have been developed in the 1990s. Modelica enables an equation based modular specification of arbitrary hierarchical differential algebraic equation models. Moreover, libraries for special application domains can be rapidly developed. This contribution compares the reaction based approach of SBML with the equation based approach of Modelica and explains the specific strengths of both tools. Several biological examples illustrating essential SBML and Modelica concepts are given. The chosen criteria for tool comparison are flexibility for constraint specification, different modeling flavors, hierarchical, modular and multidisciplinary modeling. Additionally, support for spatially distributed systems, event handling and network analysis features is discussed. As a major result it is shown that the choice of the modeling tool has a strong impact on the expressivity of the specified models but also strongly depends on the requirements of the application context.

Problem-based learning through field investigation: Boosting questioning skill, biological literacy, and academic achievement

NASA Astrophysics Data System (ADS)

Suwono, Hadi; Wibowo, Agung

2018-01-01

Biology learning emphasizes problem-based learning as a learning strategy to develop students ability in identifying and solving problems in the surrounding environment. Problem identification skills are closely correlated with questioning skills. By holding this skill, students tend to deliver a procedural question instead of the descriptive one. Problem-based learning through field investigation is an instruction model which directly exposes the students to problems or phenomena that occur in the environment, and then the students design the field investigation activities to solve these problems. The purpose of this research was to describe the improvement of undergraduate biology students on questioning skills, biological literacy, and academic achievement through problem-based learning through field investigation (PBFI) compared with the lecture-based instruction (LBI). This research was a time series quasi-experimental design. The research was conducted on August - December 2015 and involved 26 undergraduate biology students at the State University of Malang on the Freshwater Ecology course. The data were collected during the learning with LBI and PBFI, in which questioning skills, biological literacy, and academic achievement were collected 3 times in each learning model. The data showed that the procedural correlative and causal types of questions are produced by the students to guide them in conducting investigations and problem-solving in PBFI. The biological literacy and academic achievement of the students at PBFI are significantly higher than those at LBI. The results show that PBFI increases the questioning skill, biological literacy, and the academic achievement of undergraduate biology students.

WE-B-304-02: Treatment Planning Evaluation and Optimization Should Be Biologically and Not Dose/volume Based

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

Deasy, J.

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

WE-B-304-01: Treatment Planning Evaluation and Optimization Should Be Dose/volume and Not Biologically Based

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

Mayo, C.

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

Agent-based re-engineering of ErbB signaling: a modeling pipeline for integrative systems biology.

PubMed

Das, Arya A; Ajayakumar Darsana, T; Jacob, Elizabeth

2017-03-01

Experiments in systems biology are generally supported by a computational model which quantitatively estimates the parameters of the system by finding the best fit to the experiment. Mathematical models have proved to be successful in reverse engineering the system. The data generated is interpreted to understand the dynamics of the underlying phenomena. The question we have sought to answer is that - is it possible to use an agent-based approach to re-engineer a biological process, making use of the available knowledge from experimental and modelling efforts? Can the bottom-up approach benefit from the top-down exercise so as to create an integrated modelling formalism for systems biology? We propose a modelling pipeline that learns from the data given by reverse engineering, and uses it for re-engineering the system, to carry out in-silico experiments. A mathematical model that quantitatively predicts co-expression of EGFR-HER2 receptors in activation and trafficking has been taken for this study. The pipeline architecture takes cues from the population model that gives the rates of biochemical reactions, to formulate knowledge-based rules for the particle model. Agent-based simulations using these rules, support the existing facts on EGFR-HER2 dynamics. We conclude that, re-engineering models, built using the results of reverse engineering, opens up the possibility of harnessing the power pack of data which now lies scattered in literature. Virtual experiments could then become more realistic when empowered with the findings of empirical cell biology and modelling studies. Implemented on the Agent Modelling Framework developed in-house. C ++ code templates available in Supplementary material . liz.csir@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A MODE-OF-ACTION-BASED QSAR APPROACH TO IMPROVE UNDERSTANDING OF DEVELOPMENTAL TOXICITY

EPA Science Inventory

QSAR models of developmental toxicity (devtox) have met with limited regulatory acceptance due to the use of ill-defined endpoints, lack of biological interpretability, and poor model performance. More generally, the lack of biological inference of many QSAR models is often due t...

Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling.

PubMed

Chen, Ran; Zhang, Yuntao; Sahneh, Faryad Darabi; Scoglio, Caterina M; Wohlleben, Wendel; Haase, Andrea; Monteiro-Riviere, Nancy A; Riviere, Jim E

2014-09-23

Quantitative characterization of nanoparticle interactions with their surrounding environment is vital for safe nanotechnological development and standardization. A recent quantitative measure, the biological surface adsorption index (BSAI), has demonstrated promising applications in nanomaterial surface characterization and biological/environmental prediction. This paper further advances the approach beyond the application of five descriptors in the original BSAI to address the concentration dependence of the descriptors, enabling better prediction of the adsorption profile and more accurate categorization of nanomaterials based on their surface properties. Statistical analysis on the obtained adsorption data was performed based on three different models: the original BSAI, a concentration-dependent polynomial model, and an infinite dilution model. These advancements in BSAI modeling showed a promising development in the application of quantitative predictive modeling in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

A Theme-Based Approach to Teaching Nonmajors Biology: Helping Students Connect Biology to Their Lives

ERIC Educational Resources Information Center

Chaplin, Susan B.; Manske, Jill M.

2005-01-01

This article describes the curriculum for a highly student-centered human biology course constructed around a series of themes that enables the integration of the same basic paradigms found in a traditional survey lecture course without sacrificing essential content. The theme-based model enhances student interest, ability to integrate knowledge,…

Clustering change patterns using Fourier transformation with time-course gene expression data.

PubMed

Kim, Jaehee

2011-01-01

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a period of time because biologically related gene groups can share the same change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. This work is aimed at discovering gene groups with similar change patterns which share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. We applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns.

Examining the Relationship between Students' Understanding of the Nature of Models and Conceptual Learning in Biology, Physics, and Chemistry

ERIC Educational Resources Information Center

Gobert, Janice D.; O'Dwyer, Laura; Horwitz, Paul; Buckley, Barbara C.; Levy, Sharona Tal; Wilensky, Uri

2011-01-01

This research addresses high school students' understandings of the nature of models, and their interaction with model-based software in three science domains, namely, biology, physics, and chemistry. Data from 736 high school students' understandings of models were collected using the Students' Understanding of Models in Science (SUMS) survey as…

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

PubMed

Neal, Maxwell L; Carlson, Brian E; Thompson, Christopher T; James, Ryan C; Kim, Karam G; Tran, Kenneth; Crampin, Edmund J; Cook, Daniel L; Gennari, John H

2015-01-01

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen's semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the "Pandit-Hinch-Niederer" (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases

PubMed Central

Neal, Maxwell L.; Carlson, Brian E.; Thompson, Christopher T.; James, Ryan C.; Kim, Karam G.; Tran, Kenneth; Crampin, Edmund J.; Cook, Daniel L.; Gennari, John H.

2015-01-01

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen’s semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the “Pandit-Hinch-Niederer” (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach. PMID:26716837

Using Petri Net Tools to Study Properties and Dynamics of Biological Systems

PubMed Central

Peleg, Mor; Rubin, Daniel; Altman, Russ B.

2005-01-01

Petri Nets (PNs) and their extensions are promising methods for modeling and simulating biological systems. We surveyed PN formalisms and tools and compared them based on their mathematical capabilities as well as by their appropriateness to represent typical biological processes. We measured the ability of these tools to model specific features of biological systems and answer a set of biological questions that we defined. We found that different tools are required to provide all capabilities that we assessed. We created software to translate a generic PN model into most of the formalisms and tools discussed. We have also made available three models and suggest that a library of such models would catalyze progress in qualitative modeling via PNs. Development and wide adoption of common formats would enable researchers to share models and use different tools to analyze them without the need to convert to proprietary formats. PMID:15561791

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

DOE PAGES

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting; ...

2018-03-28

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termedmore » network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.« less

Generalizing Gillespie’s Direct Method to Enable Network-Free Simulations

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

Suderman, Ryan T.; Mitra, Eshan David; Lin, Yen Ting

Gillespie’s direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie’s direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termedmore » network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie’s direct method for network-free simulation. Lastly, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.« less

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.

PubMed

Cannon, Robert C; Gleeson, Padraig; Crook, Sharon; Ganapathy, Gautham; Marin, Boris; Piasini, Eugenio; Silver, R Angus

2014-01-01

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2

PubMed Central

Cannon, Robert C.; Gleeson, Padraig; Crook, Sharon; Ganapathy, Gautham; Marin, Boris; Piasini, Eugenio; Silver, R. Angus

2014-01-01

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties. PMID:25309419

From biology to mathematical models and back: teaching modeling to biology students, and biology to math and engineering students.

PubMed

Chiel, Hillel J; McManus, Jeffrey M; Shaw, Kendrick M

2010-01-01

We describe the development of a course to teach modeling and mathematical analysis skills to students of biology and to teach biology to students with strong backgrounds in mathematics, physics, or engineering. The two groups of students have different ways of learning material and often have strong negative feelings toward the area of knowledge that they find difficult. To give students a sense of mastery in each area, several complementary approaches are used in the course: 1) a "live" textbook that allows students to explore models and mathematical processes interactively; 2) benchmark problems providing key skills on which students make continuous progress; 3) assignment of students to teams of two throughout the semester; 4) regular one-on-one interactions with instructors throughout the semester; and 5) a term project in which students reconstruct, analyze, extend, and then write in detail about a recently published biological model. Based on student evaluations and comments, an attitude survey, and the quality of the students' term papers, the course has significantly increased the ability and willingness of biology students to use mathematical concepts and modeling tools to understand biological systems, and it has significantly enhanced engineering students' appreciation of biology.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

PubMed

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

WE-B-304-03: Biological Treatment Planning

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

Orton, C.

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

Agent-Based Deterministic Modeling of the Bone Marrow Homeostasis.

PubMed

Kurhekar, Manish; Deshpande, Umesh

2016-01-01

Modeling of stem cells not only describes but also predicts how a stem cell's environment can control its fate. The first stem cell populations discovered were hematopoietic stem cells (HSCs). In this paper, we present a deterministic model of bone marrow (that hosts HSCs) that is consistent with several of the qualitative biological observations. This model incorporates stem cell death (apoptosis) after a certain number of cell divisions and also demonstrates that a single HSC can potentially populate the entire bone marrow. It also demonstrates that there is a production of sufficient number of differentiated cells (RBCs, WBCs, etc.). We prove that our model of bone marrow is biologically consistent and it overcomes the biological feasibility limitations of previously reported models. The major contribution of our model is the flexibility it allows in choosing model parameters which permits several different simulations to be carried out in silico without affecting the homeostatic properties of the model. We have also performed agent-based simulation of the model of bone marrow system proposed in this paper. We have also included parameter details and the results obtained from the simulation. The program of the agent-based simulation of the proposed model is made available on a publicly accessible website.

A new biologic prognostic model based on immunohistochemistry predicts survival in patients with diffuse large B-cell lymphoma.

PubMed

Perry, Anamarija M; Cardesa-Salzmann, Teresa M; Meyer, Paul N; Colomo, Luis; Smith, Lynette M; Fu, Kai; Greiner, Timothy C; Delabie, Jan; Gascoyne, Randy D; Rimsza, Lisa; Jaffe, Elaine S; Ott, German; Rosenwald, Andreas; Braziel, Rita M; Tubbs, Raymond; Cook, James R; Staudt, Louis M; Connors, Joseph M; Sehn, Laurie H; Vose, Julie M; López-Guillermo, Armando; Campo, Elias; Chan, Wing C; Weisenburger, Dennis D

2012-09-13

Biologic factors that predict the survival of patients with a diffuse large B-cell lymphoma, such as cell of origin and stromal signatures, have been discovered by gene expression profiling. We attempted to simulate these gene expression profiling findings and create a new biologic prognostic model based on immunohistochemistry. We studied 199 patients (125 in the training set, 74 in the validation set) with de novo diffuse large B-cell lymphoma treated with rituximab and CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or CHOP-like therapies, and immunohistochemical stains were performed on paraffin-embedded tissue microarrays. In the model, 1 point was awarded for each adverse prognostic factor: nongerminal center B cell-like subtype, SPARC (secreted protein, acidic, and rich in cysteine) < 5%, and microvascular density quartile 4. The model using these 3 biologic markers was highly predictive of overall survival and event-free survival in multivariate analysis after adjusting for the International Prognostic Index in both the training and validation sets. This new model delineates 2 groups of patients, 1 with a low biologic score (0-1) and good survival and the other with a high score (2-3) and poor survival. This new biologic prognostic model could be used with the International Prognostic Index to stratify patients for novel or risk-adapted therapies.

Enhancement of COPD biological networks using a web-based collaboration interface

PubMed Central

Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C.; Schlage, Walter K.; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V.; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C.; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

2015-01-01

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks. PMID:25767696

Enhancement of COPD biological networks using a web-based collaboration interface.

PubMed

Boue, Stephanie; Fields, Brett; Hoeng, Julia; Park, Jennifer; Peitsch, Manuel C; Schlage, Walter K; Talikka, Marja; Binenbaum, Ilona; Bondarenko, Vladimir; Bulgakov, Oleg V; Cherkasova, Vera; Diaz-Diaz, Norberto; Fedorova, Larisa; Guryanova, Svetlana; Guzova, Julia; Igorevna Koroleva, Galina; Kozhemyakina, Elena; Kumar, Rahul; Lavid, Noa; Lu, Qingxian; Menon, Swapna; Ouliel, Yael; Peterson, Samantha C; Prokhorov, Alexander; Sanders, Edward; Schrier, Sarah; Schwaitzer Neta, Golan; Shvydchenko, Irina; Tallam, Aravind; Villa-Fombuena, Gema; Wu, John; Yudkevich, Ilya; Zelikman, Mariya

2015-01-01

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.

Conceptual Model-Based Systems Biology: Mapping Knowledge and Discovering Gaps in the mRNA Transcription Cycle

PubMed Central

Somekh, Judith; Choder, Mordechai; Dori, Dov

2012-01-01

We propose a Conceptual Model-based Systems Biology framework for qualitative modeling, executing, and eliciting knowledge gaps in molecular biology systems. The framework is an adaptation of Object-Process Methodology (OPM), a graphical and textual executable modeling language. OPM enables concurrent representation of the system's structure—the objects that comprise the system, and behavior—how processes transform objects over time. Applying a top-down approach of recursively zooming into processes, we model a case in point—the mRNA transcription cycle. Starting with this high level cell function, we model increasingly detailed processes along with participating objects. Our modeling approach is capable of modeling molecular processes such as complex formation, localization and trafficking, molecular binding, enzymatic stimulation, and environmental intervention. At the lowest level, similar to the Gene Ontology, all biological processes boil down to three basic molecular functions: catalysis, binding/dissociation, and transporting. During modeling and execution of the mRNA transcription model, we discovered knowledge gaps, which we present and classify into various types. We also show how model execution enhances a coherent model construction. Identification and pinpointing knowledge gaps is an important feature of the framework, as it suggests where research should focus and whether conjectures about uncertain mechanisms fit into the already verified model. PMID:23308089

Psychological and biological responses to race-based social stress as pathways to disparities in educational outcomes.

PubMed

Levy, Dorainne J; Heissel, Jennifer A; Richeson, Jennifer A; Adam, Emma K

2016-09-01

We present the race-based disparities in stress and sleep in context model (RDSSC), which argues that racial/ethnic disparities in educational achievement and attainment are partially explained by the effects of race-based stressors, such as stereotype threat and perceived discrimination, on psychological and biological responses to stress, which, in turn, impact cognitive functioning and academic performance. Whereas the roles of psychological coping responses, such as devaluation and disidentification, have been theorized in previous work, the present model integrates the roles of biological stress responses, such as changes in stress hormones and sleep hours and quality, to this rich literature. We situate our model of the impact of race-based stress in the broader contexts of other stressors [e.g., stressors associated with socioeconomic status (SES)], developmental histories of stress, and individual and group differences in access to resources, opportunity and employment structures. Considering both psychological and biological responses to race-based stressors, in social contexts, will yield a more comprehensive understanding of the emergence of academic disparities between Whites and racial/ethnic minorities. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems

PubMed Central

2014-01-01

Background The kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development. Description KiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data. KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research. Conclusions KiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects. The web application implemented using Ruby on Rails framework is freely available for web access at http://kimosys.org, along with its full documentation. PMID:25115331

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.

PubMed

Misirli, Goksel; Cavaliere, Matteo; Waites, William; Pocock, Matthew; Madsen, Curtis; Gilfellon, Owen; Honorato-Zimmer, Ricardo; Zuliani, Paolo; Danos, Vincent; Wipat, Anil

2016-03-15

Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk. © The Author 2015. Published by Oxford University Press.

FRAMEWORK FOR EVALUATION OF PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELS FOR USE IN SAFETY OR RISK ASSESSMENT

EPA Science Inventory

ABSTRACT

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic (PBPK) models, raise the issue of how to evaluate whether the models are adequate for proposed uses including safety or risk ...

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology.

PubMed

Rao, Rohit T; Scherholz, Megerle L; Hartmanshenn, Clara; Bae, Seul-A; Androulakis, Ioannis P

2017-12-05

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

And So It Grows: Using a Computer-Based Simulation of a Population Growth Model to Integrate Biology & Mathematics

ERIC Educational Resources Information Center

Street, Garrett M.; Laubach, Timothy A.

2013-01-01

We provide a 5E structured-inquiry lesson so that students can learn more of the mathematics behind the logistic model of population biology. By using models and mathematics, students understand how population dynamics can be influenced by relatively simple changes in the environment.

Introductory Biology Students' Conceptual Models and Explanations of the Origin of Variation

ERIC Educational Resources Information Center

Bray Speth, Elena; Shaw, Neil; Momsen, Jennifer; Reinagel, Adam; Le, Paul; Taqieddin, Ranya; Long, Tammy

2014-01-01

Mutation is the key molecular mechanism generating phenotypic variation, which is the basis for evolution. In an introductory biology course, we used a model-based pedagogy that enabled students to integrate their understanding of genetics and evolution within multiple case studies. We used student-generated conceptual models to assess…

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.

Formal reasoning about systems biology using theorem proving

PubMed Central

Hasan, Osman; Siddique, Umair; Tahar, Sofiène

2017-01-01

System biology provides the basis to understand the behavioral properties of complex biological organisms at different levels of abstraction. Traditionally, analysing systems biology based models of various diseases have been carried out by paper-and-pencil based proofs and simulations. However, these methods cannot provide an accurate analysis, which is a serious drawback for the safety-critical domain of human medicine. In order to overcome these limitations, we propose a framework to formally analyze biological networks and pathways. In particular, we formalize the notion of reaction kinetics in higher-order logic and formally verify some of the commonly used reaction based models of biological networks using the HOL Light theorem prover. Furthermore, we have ported our earlier formalization of Zsyntax, i.e., a deductive language for reasoning about biological networks and pathways, from HOL4 to the HOL Light theorem prover to make it compatible with the above-mentioned formalization of reaction kinetics. To illustrate the usefulness of the proposed framework, we present the formal analysis of three case studies, i.e., the pathway leading to TP53 Phosphorylation, the pathway leading to the death of cancer stem cells and the tumor growth based on cancer stem cells, which is used for the prognosis and future drug designs to treat cancer patients. PMID:28671950

Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search

PubMed Central

2014-01-01

Background The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. Results We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB. Conclusion The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology. PMID:24917489

Teaching Real Data Interpretation with Models (TRIM): Analysis of Student Dialogue in a Large-Enrollment Cell and Developmental Biology Course

ERIC Educational Resources Information Center

Zagallo, Patricia; Meddleton, Shanice; Bolger, Molly S.

2016-01-01

We present our design for a cell biology course to integrate content with scientific practices, specifically data interpretation and model-based reasoning. A 2-year research project within this course allowed us to understand how students interpret authentic biological data in this setting. Through analysis of written work, we measured the extent…

Introducing memory and association mechanism into a biologically inspired visual model.

PubMed

Qiao, Hong; Li, Yinlin; Tang, Tang; Wang, Peng

2014-09-01

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

Symmetry structure in discrete models of biochemical systems: natural subsystems and the weak control hierarchy in a new model of computation driven by interactions.

PubMed

Nehaniv, Chrystopher L; Rhodes, John; Egri-Nagy, Attila; Dini, Paolo; Morris, Eric Rothstein; Horváth, Gábor; Karimi, Fariba; Schreckling, Daniel; Schilstra, Maria J

2015-07-28

Interaction computing is inspired by the observation that cell metabolic/regulatory systems construct order dynamically, through constrained interactions between their components and based on a wide range of possible inputs and environmental conditions. The goals of this work are to (i) identify and understand mathematically the natural subsystems and hierarchical relations in natural systems enabling this and (ii) use the resulting insights to define a new model of computation based on interactions that is useful for both biology and computation. The dynamical characteristics of the cellular pathways studied in systems biology relate, mathematically, to the computational characteristics of automata derived from them, and their internal symmetry structures to computational power. Finite discrete automata models of biological systems such as the lac operon, the Krebs cycle and p53-mdm2 genetic regulation constructed from systems biology models have canonically associated algebraic structures (their transformation semigroups). These contain permutation groups (local substructures exhibiting symmetry) that correspond to 'pools of reversibility'. These natural subsystems are related to one another in a hierarchical manner by the notion of 'weak control'. We present natural subsystems arising from several biological examples and their weak control hierarchies in detail. Finite simple non-Abelian groups are found in biological examples and can be harnessed to realize finitary universal computation. This allows ensembles of cells to achieve any desired finitary computational transformation, depending on external inputs, via suitably constrained interactions. Based on this, interaction machines that grow and change their structure recursively are introduced and applied, providing a natural model of computation driven by interactions.

The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints.

PubMed

Olivier, Brett G; Bergmann, Frank T

2015-09-04

Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state. The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).

The Systems Biology Markup Language (SBML) Level 3 Package: Flux Balance Constraints.

PubMed

Olivier, Brett G; Bergmann, Frank T

2015-06-01

Constraint-based modeling is a well established modelling methodology used to analyze and study biological networks on both a medium and genome scale. Due to their large size, genome scale models are typically analysed using constraint-based optimization techniques. One widely used method is Flux Balance Analysis (FBA) which, for example, requires a modelling description to include: the definition of a stoichiometric matrix, an objective function and bounds on the values that fluxes can obtain at steady state. The Flux Balance Constraints (FBC) Package extends SBML Level 3 and provides a standardized format for the encoding, exchange and annotation of constraint-based models. It includes support for modelling concepts such as objective functions, flux bounds and model component annotation that facilitates reaction balancing. The FBC package establishes a base level for the unambiguous exchange of genome-scale, constraint-based models, that can be built upon by the community to meet future needs (e. g. by extending it to cover dynamic FBC models).

Systems pharmacology - Towards the modeling of network interactions.

PubMed

Danhof, Meindert

2016-10-30

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior. Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

Gusakova, Valentina I.; Podladchikova, Lubov N.; Shaposhnikov, Dmitry G.; Markin, Sergey N.; Golovan, Alexander V.; Lee, Seong-Whan

1999-08-01

At present, in computer vision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world imags are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the result of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.

Applying systems biology methods to the study of human physiology in extreme environments

PubMed Central

2013-01-01

Systems biology is defined in this review as ‘an iterative process of computational model building and experimental model revision with the aim of understanding or simulating complex biological systems’. We propose that, in practice, systems biology rests on three pillars: computation, the omics disciplines and repeated experimental perturbation of the system of interest. The number of ethical and physiologically relevant perturbations that can be used in experiments on healthy humans is extremely limited and principally comprises exercise, nutrition, infusions (e.g. Intralipid), some drugs and altered environment. Thus, we argue that systems biology and environmental physiology are natural symbionts for those interested in a system-level understanding of human biology. However, despite excellent progress in high-altitude genetics and several proteomics studies, systems biology research into human adaptation to extreme environments is in its infancy. A brief description and overview of systems biology in its current guise is given, followed by a mini review of computational methods used for modelling biological systems. Special attention is given to high-altitude research, metabolic network reconstruction and constraint-based modelling. PMID:23849719

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

PubMed

Ahmed, Wamiq M; Lenz, Dominik; Liu, Jia; Paul Robinson, J; Ghafoor, Arif

2008-03-01

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

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

NONE

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

From Biology to Mathematical Models and Back: Teaching Modeling to Biology Students, and Biology to Math and Engineering Students

PubMed Central

McManus, Jeffrey M.; Shaw, Kendrick M.

2010-01-01

We describe the development of a course to teach modeling and mathematical analysis skills to students of biology and to teach biology to students with strong backgrounds in mathematics, physics, or engineering. The two groups of students have different ways of learning material and often have strong negative feelings toward the area of knowledge that they find difficult. To give students a sense of mastery in each area, several complementary approaches are used in the course: 1) a “live” textbook that allows students to explore models and mathematical processes interactively; 2) benchmark problems providing key skills on which students make continuous progress; 3) assignment of students to teams of two throughout the semester; 4) regular one-on-one interactions with instructors throughout the semester; and 5) a term project in which students reconstruct, analyze, extend, and then write in detail about a recently published biological model. Based on student evaluations and comments, an attitude survey, and the quality of the students' term papers, the course has significantly increased the ability and willingness of biology students to use mathematical concepts and modeling tools to understand biological systems, and it has significantly enhanced engineering students' appreciation of biology. PMID:20810957

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes.

PubMed

Arakawa, Kazuharu; Yamada, Yohei; Shinoda, Kosaku; Nakayama, Yoichi; Tomita, Masaru

2006-03-23

Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

A biologically plausible computational model for auditory object recognition.

PubMed

Larson, Eric; Billimoria, Cyrus P; Sen, Kamal

2009-01-01

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

Toward Clarifying the Meanings of "Gender" in Mathematics Education Research

ERIC Educational Resources Information Center

Damarin, Suzanne; Erchick, Diana B.

2010-01-01

The importance of clarity in definitions of gender is discussed and several conceptual models of gender are presented. Four of these models begin with biological sex differences but draw attention to other aspects of gender. Four models set biology aside and are based on social and cultural theories. Some of the advantages of the latter for…

Phenomenological and molecular-level Petri net modeling and simulation of long-term potentiation.

PubMed

Hardy, S; Robillard, P N

2005-10-01

Petri net-based modeling methods have been used in many research projects to represent biological systems. Among these, the hybrid functional Petri net (HFPN) was developed especially for biological modeling in order to provide biologists with a more intuitive Petri net-based method. In the literature, HFPNs are used to represent kinetic models at the molecular level. We present two models of long-term potentiation previously represented by differential equations which we have transformed into HFPN models: a phenomenological synapse model and a molecular-level model of the CaMKII regulation pathway. Through simulation, we obtained results similar to those of previous studies using these models. Our results open the way to a new type of modeling for systems biology where HFPNs are used to combine different levels of abstraction within one model. This approach can be useful in fully modeling a system at the molecular level when kinetic data is missing or when a full study of a system at the molecular level it is not within the scope of the research.

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

Biological intuition in alignment-free methods: response to Posada.

PubMed

Ragan, Mark A; Chan, Cheong Xin

2013-08-01

A recent editorial in Journal of Molecular Evolution highlights opportunities and challenges facing molecular evolution in the era of next-generation sequencing. Abundant sequence data should allow more-complex models to be fit at higher confidence, making phylogenetic inference more reliable and improving our understanding of evolution at the molecular level. However, concern that approaches based on multiple sequence alignment may be computationally infeasible for large datasets is driving the development of so-called alignment-free methods for sequence comparison and phylogenetic inference. The recent editorial characterized these approaches as model-free, not based on the concept of homology, and lacking in biological intuition. We argue here that alignment-free methods have not abandoned models or homology, and can be biologically intuitive.

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.

PubMed

Zi, Zhike

2011-04-01

Parameter estimation is crucial for the modeling and dynamic analysis of biological systems. However, implementing parameter estimation is time consuming and computationally demanding. Here, we introduced a parallel parameter estimation tool for Systems Biology Markup Language (SBML)-based models (SBML-PET-MPI). SBML-PET-MPI allows the user to perform parameter estimation and parameter uncertainty analysis by collectively fitting multiple experimental datasets. The tool is developed and parallelized using the message passing interface (MPI) protocol, which provides good scalability with the number of processors. SBML-PET-MPI is freely available for non-commercial use at http://www.bioss.uni-freiburg.de/cms/sbml-pet-mpi.html or http://sites.google.com/site/sbmlpetmpi/.

Generalized Beer-Lambert model for near-infrared light propagation in thick biological tissues

NASA Astrophysics Data System (ADS)

Bhatt, Manish; Ayyalasomayajula, Kalyan R.; Yalavarthy, Phaneendra K.

2016-07-01

The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modified the Beer-Lambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concentrations for NIR spectroscopic data analysis. Even though MBLL is effective in terms of providing qualitative comparison, it suffers from its applicability across tissue types and tissue dimensions. In this work, we introduce Lambert-W function-based modeling for light propagation in biological tissues, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coefficients μ0 and η. We validated our model against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. We included numerous human and animal tissues to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is first of its kind in providing accurate modeling of NIR light propagation in biological tissues.

An online model composition tool for system biology models

PubMed Central

2013-01-01

Background There are multiple representation formats for Systems Biology computational models, and the Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used to capture, store, and distribute computational models by Systems Biology data sources (e.g., the BioModels Database) and researchers. Therefore, there is a need for all-in-one web-based solutions that support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying, and browsing computational models. Results We present the design and implementation of the Model Composition Tool (Interface) within the PathCase-SB (PathCase Systems Biology) web portal. The tool helps users compose systems biology models to facilitate the complex process of merging systems biology models. We also present three tools that support the model composition tool, namely, (1) Model Simulation Interface that generates a visual plot of the simulation according to user’s input, (2) iModel Tool as a platform for users to upload their own models to compose, and (3) SimCom Tool that provides a side by side comparison of models being composed in the same pathway. Finally, we provide a web site that hosts BioModels Database models and a separate web site that hosts SBML Test Suite models. Conclusions Model composition tool (and the other three tools) can be used with little or no knowledge of the SBML document structure. For this reason, students or anyone who wants to learn about systems biology will benefit from the described functionalities. SBML Test Suite models will be a nice starting point for beginners. And, for more advanced purposes, users will able to access and employ models of the BioModels Database as well. PMID:24006914

Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

PubMed Central

Dwivedi, Dipankar; Mohanty, Binayak P.; Lesikar, Bruce J.

2013-01-01

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs. PMID:24511166

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.

State-transition diagrams for biologists.

PubMed

Bersini, Hugues; Klatzmann, David; Six, Adrien; Thomas-Vaslin, Véronique

2012-01-01

It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of state-transitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines.

State-Transition Diagrams for Biologists

PubMed Central

Bersini, Hugues; Klatzmann, David; Six, Adrien; Thomas-Vaslin, Véronique

2012-01-01

It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of state-transitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines. PMID:22844438

The short-lived African turquoise killifish: an emerging experimental model for ageing

PubMed Central

Kim, Yumi; Nam, Hong Gil; Valenzano, Dario Riccardo

2016-01-01

ABSTRACT Human ageing is a fundamental biological process that leads to functional decay, increased risk for various diseases and, ultimately, death. Some of the basic biological mechanisms underlying human ageing are shared with other organisms; thus, animal models have been invaluable in providing key mechanistic and molecular insights into the common bases of biological ageing. In this Review, we briefly summarise the major applications of the most commonly used model organisms adopted in ageing research and highlight their relevance in understanding human ageing. We compare the strengths and limitations of different model organisms and discuss in detail an emerging ageing model, the short-lived African turquoise killifish. We review the recent progress made in using the turquoise killifish to study the biology of ageing and discuss potential future applications of this promising animal model. PMID:26839399

Synthetic biology between challenges and risks: suggestions for a model of governance and a regulatory framework, based on fundamental rights.

PubMed

Colussi, Ilaria Anna

2013-01-01

This paper deals with the emerging synthetic biology, its challenges and risks, and tries to design a model for the governance and regulation of the field. The model is called of "prudent vigilance" (inspired by the report about synthetic biology, drafted by the U.S. Presidential Commission on Bioethics, 2010), and it entails (a) an ongoing and periodically revised process of assessment and management of all the risks and concerns, and (b) the adoption of policies - taken through "hard law" and "soft law" sources - that are based on the principle of proportionality (among benefits and risks), on a reasonable balancing between different interests and rights at stake, and are oriented by a constitutional frame, which is represented by the protection of fundamental human rights emerging in the field of synthetic biology (right to life, right to health, dignity, freedom of scientific research, right to environment). After the theoretical explanation of the model, its operability is "checked", by considering its application with reference to only one specific risk brought up by synthetic biology - biosecurity risk, i.e. the risk of bioterrorism.

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

Programming biological models in Python using PySB.

PubMed

Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

2013-01-01

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Programming biological models in Python using PySB

PubMed Central

Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

2013-01-01

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis. PMID:23423320

VirtualLeaf: an open-source framework for cell-based modeling of plant tissue growth and development.

PubMed

Merks, Roeland M H; Guravage, Michael; Inzé, Dirk; Beemster, Gerrit T S

2011-02-01

Plant organs, including leaves and roots, develop by means of a multilevel cross talk between gene regulation, patterned cell division and cell expansion, and tissue mechanics. The multilevel regulatory mechanisms complicate classic molecular genetics or functional genomics approaches to biological development, because these methodologies implicitly assume a direct relation between genes and traits at the level of the whole plant or organ. Instead, understanding gene function requires insight into the roles of gene products in regulatory networks, the conditions of gene expression, etc. This interplay is impossible to understand intuitively. Mathematical and computer modeling allows researchers to design new hypotheses and produce experimentally testable insights. However, the required mathematics and programming experience makes modeling poorly accessible to experimental biologists. Problem-solving environments provide biologically intuitive in silico objects ("cells", "regulation networks") required for setting up a simulation and present those to the user in terms of familiar, biological terminology. Here, we introduce the cell-based computer modeling framework VirtualLeaf for plant tissue morphogenesis. The current version defines a set of biologically intuitive C++ objects, including cells, cell walls, and diffusing and reacting chemicals, that provide useful abstractions for building biological simulations of developmental processes. We present a step-by-step introduction to building models with VirtualLeaf, providing basic example models of leaf venation and meristem development. VirtualLeaf-based models provide a means for plant researchers to analyze the function of developmental genes in the context of the biophysics of growth and patterning. VirtualLeaf is an ongoing open-source software project (http://virtualleaf.googlecode.com) that runs on Windows, Mac, and Linux.

USE OF BIOLOGICALLY BASED COMPUTATIONAL MODELING IN MODE OF ACTION-BASED RISK ASSESSMENT – AN EXAMPLE OF CHLOROFORM

EPA Science Inventory

The objective of current work is to develop a new cancer dose-response assessment for chloroform using a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model. The PBPK/PD model is based on a mode of action in which the cytolethality of chloroform occurs when the ...

Mathematical manipulative models: in defense of "beanbag biology".

PubMed

Jungck, John R; Gaff, Holly; Weisstein, Anton E

2010-01-01

Mathematical manipulative models have had a long history of influence in biological research and in secondary school education, but they are frequently neglected in undergraduate biology education. By linking mathematical manipulative models in a four-step process-1) use of physical manipulatives, 2) interactive exploration of computer simulations, 3) derivation of mathematical relationships from core principles, and 4) analysis of real data sets-we demonstrate a process that we have shared in biological faculty development workshops led by staff from the BioQUEST Curriculum Consortium over the past 24 yr. We built this approach based upon a broad survey of literature in mathematical educational research that has convincingly demonstrated the utility of multiple models that involve physical, kinesthetic learning to actual data and interactive simulations. Two projects that use this approach are introduced: The Biological Excel Simulations and Tools in Exploratory, Experiential Mathematics (ESTEEM) Project (http://bioquest.org/esteem) and Numerical Undergraduate Mathematical Biology Education (NUMB3R5 COUNT; http://bioquest.org/numberscount). Examples here emphasize genetics, ecology, population biology, photosynthesis, cancer, and epidemiology. Mathematical manipulative models help learners break through prior fears to develop an appreciation for how mathematical reasoning informs problem solving, inference, and precise communication in biology and enhance the diversity of quantitative biology education.

Improving Conceptual Understanding and Representation Skills Through Excel-Based Modeling

NASA Astrophysics Data System (ADS)

Malone, Kathy L.; Schunn, Christian D.; Schuchardt, Anita M.

2018-02-01

The National Research Council framework for science education and the Next Generation Science Standards have developed a need for additional research and development of curricula that is both technologically model-based and includes engineering practices. This is especially the case for biology education. This paper describes a quasi-experimental design study to test the effectiveness of a model-based curriculum focused on the concepts of natural selection and population ecology that makes use of Excel modeling tools (Modeling Instruction in Biology with Excel, MBI-E). The curriculum revolves around the bio-engineering practice of controlling an invasive species. The study takes place in the Midwest within ten high schools teaching a regular-level introductory biology class. A post-test was designed that targeted a number of common misconceptions in both concept areas as well as representational usage. The results of a post-test demonstrate that the MBI-E students significantly outperformed the traditional classes in both natural selection and population ecology concepts, thus overcoming a number of misconceptions. In addition, implementing students made use of more multiple representations as well as demonstrating greater fascination for science.

Investigating How German Biology Teachers Use Three-Dimensional Physical Models in Classroom Instruction: a Video Study

NASA Astrophysics Data System (ADS)

Werner, Sonja; Förtsch, Christian; Boone, William; von Kotzebue, Lena; Neuhaus, Birgit J.

2017-07-01

To obtain a general understanding of science, model use as part of National Education Standards is important for instruction. Model use can be characterized by three aspects: (1) the characteristics of the model, (2) the integration of the model into instruction, and (3) the use of models to foster scientific reasoning. However, there were no empirical results describing the implementation of National Education Standards in science instruction concerning the use of models. Therefore, the present study investigated the implementation of different aspects of model use in German biology instruction. Two biology lessons on the topic neurobiology in grade nine of 32 biology teachers were videotaped (N = 64 videos). These lessons were analysed using an event-based coding manual according to three aspects of model described above. Rasch analysis of the coded categories was conducted and showed reliable measurement. In the first analysis, we identified 68 lessons where a total of 112 different models were used. The in-depth analysis showed that special aspects of an elaborate model use according to several categories of scientific reasoning were rarely implemented in biology instruction. A critical reflection of the used model (N = 25 models; 22.3%) and models to demonstrate scientific reasoning (N = 26 models; 23.2%) were seldom observed. Our findings suggest that pre-service biology teacher education and professional development initiatives in Germany have to focus on both aspects.

A Collective Case Study of Secondary Students' Model-Based Inquiry on Natural Selection through Programming in an Agent-Based Modeling Environment

ERIC Educational Resources Information Center

Xiang, Lin

2011-01-01

This is a collective case study seeking to develop detailed descriptions of how programming an agent-based simulation influences a group of 8th grade students' model-based inquiry (MBI) by examining students' agent-based programmable modeling (ABPM) processes and the learning outcomes. The context of the present study was a biology unit on…

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks.

PubMed

He, Jieyue; Wang, Chunyan; Qiu, Kunpu; Zhong, Wei

2014-01-01

Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies.

An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

PubMed Central

2014-01-01

Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism evaluation based on circuit simulation method excludes most of subgraphs which are not probability isomorphism and reduces the search space of the probability isomorphism subgraphs using the mismatch values in the node voltage set. It is an innovative way to find the frequent probability patterns, which can be efficiently applied to probability motif discovery problems in the further studies. PMID:25350277

Evolutionary game based control for biological systems with applications in drug delivery.

PubMed

Li, Xiaobo; Lenaghan, Scott C; Zhang, Mingjun

2013-06-07

Control engineering and analysis of biological systems have become increasingly important for systems and synthetic biology. Unfortunately, no widely accepted control framework is currently available for these systems, especially at the cell and molecular levels. This is partially due to the lack of appropriate mathematical models to describe the unique dynamics of biological systems, and the lack of implementation techniques, such as ultra-fast and ultra-small devices and corresponding control algorithms. This paper proposes a control framework for biological systems subject to dynamics that exhibit adaptive behavior under evolutionary pressures. The control framework was formulated based on evolutionary game based modeling, which integrates both the internal dynamics and the population dynamics. In the proposed control framework, the adaptive behavior was characterized as an internal dynamic, and the external environment was regarded as an external control input. The proposed open-interface control framework can be integrated with additional control algorithms for control of biological systems. To demonstrate the effectiveness of the proposed framework, an optimal control strategy was developed and validated for drug delivery using the pathogen Giardia lamblia as a test case. In principle, the proposed control framework can be applied to any biological system exhibiting adaptive behavior under evolutionary pressures. Copyright © 2013 Elsevier Ltd. All rights reserved.

Clustering of change patterns using Fourier coefficients.

PubMed

Kim, Jaehee; Kim, Haseong

2008-01-15

To understand the behavior of genes, it is important to explore how the patterns of gene expression change over a time period because biologically related gene groups can share the same change patterns. Many clustering algorithms have been proposed to group observation data. However, because of the complexity of the underlying functions there have not been many studies on grouping data based on change patterns. In this study, the problem of finding similar change patterns is induced to clustering with the derivative Fourier coefficients. The sample Fourier coefficients not only provide information about the underlying functions, but also reduce the dimension. In addition, as their limiting distribution is a multivariate normal, a model-based clustering method incorporating statistical properties would be appropriate. This work is aimed at discovering gene groups with similar change patterns that share similar biological properties. We developed a statistical model using derivative Fourier coefficients to identify similar change patterns of gene expression. We used a model-based method to cluster the Fourier series estimation of derivatives. The model-based method is advantageous over other methods in our proposed model because the sample Fourier coefficients asymptotically follow the multivariate normal distribution. Change patterns are automatically estimated with the Fourier representation in our model. Our model was tested in simulations and on real gene data sets. The simulation results showed that the model-based clustering method with the sample Fourier coefficients has a lower clustering error rate than K-means clustering. Even when the number of repeated time points was small, the same results were obtained. We also applied our model to cluster change patterns of yeast cell cycle microarray expression data with alpha-factor synchronization. It showed that, as the method clusters with the probability-neighboring data, the model-based clustering with our proposed model yielded biologically interpretable results. We expect that our proposed Fourier analysis with suitably chosen smoothing parameters could serve as a useful tool in classifying genes and interpreting possible biological change patterns. The R program is available upon the request.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

Data Integration and Mining for Synthetic Biology Design.

PubMed

Mısırlı, Göksel; Hallinan, Jennifer; Pocock, Matthew; Lord, Phillip; McLaughlin, James Alastair; Sauro, Herbert; Wipat, Anil

2016-10-21

One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterized parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterize biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread among these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single data set, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modeling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle.

SynBioSS designer: a web-based tool for the automated generation of kinetic models for synthetic biological constructs

PubMed Central

Weeding, Emma; Houle, Jason

2010-01-01

Modeling tools can play an important role in synthetic biology the same way modeling helps in other engineering disciplines: simulations can quickly probe mechanisms and provide a clear picture of how different components influence the behavior of the whole. We present a brief review of available tools and present SynBioSS Designer. The Synthetic Biology Software Suite (SynBioSS) is used for the generation, storing, retrieval and quantitative simulation of synthetic biological networks. SynBioSS consists of three distinct components: the Desktop Simulator, the Wiki, and the Designer. SynBioSS Designer takes as input molecular parts involved in gene expression and regulation (e.g. promoters, transcription factors, ribosome binding sites, etc.), and automatically generates complete networks of reactions that represent transcription, translation, regulation, induction and degradation of those parts. Effectively, Designer uses DNA sequences as input and generates networks of biomolecular reactions as output. In this paper we describe how Designer uses universal principles of molecular biology to generate models of any arbitrary synthetic biological system. These models are useful as they explain biological phenotypic complexity in mechanistic terms. In turn, such mechanistic explanations can assist in designing synthetic biological systems. We also discuss, giving practical guidance to users, how Designer interfaces with the Registry of Standard Biological Parts, the de facto compendium of parts used in synthetic biology applications. PMID:20639523

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Discovering the Power of Individual-Based Modelling in Teaching and Learning: The Study of a Predator-Prey System

ERIC Educational Resources Information Center

Ginovart, Marta

2014-01-01

The general aim is to promote the use of individual-based models (biological agent-based models) in teaching and learning contexts in life sciences and to make their progressive incorporation into academic curricula easier, complementing other existing modelling strategies more frequently used in the classroom. Modelling activities for the study…

The short-lived African turquoise killifish: an emerging experimental model for ageing.

PubMed

Kim, Yumi; Nam, Hong Gil; Valenzano, Dario Riccardo

2016-02-01

Human ageing is a fundamental biological process that leads to functional decay, increased risk for various diseases and, ultimately, death. Some of the basic biological mechanisms underlying human ageing are shared with other organisms; thus, animal models have been invaluable in providing key mechanistic and molecular insights into the common bases of biological ageing. In this Review, we briefly summarise the major applications of the most commonly used model organisms adopted in ageing research and highlight their relevance in understanding human ageing. We compare the strengths and limitations of different model organisms and discuss in detail an emerging ageing model, the short-lived African turquoise killifish. We review the recent progress made in using the turquoise killifish to study the biology of ageing and discuss potential future applications of this promising animal model. © 2016. Published by The Company of Biologists Ltd.

Electrophoretic separator for purifying biologicals, part 1

NASA Technical Reports Server (NTRS)

Mccreight, L. R.

1978-01-01

A program to develop an engineering model of an electrophoretic separator for purifying biologicals is summarized. An extensive mathematical modeling study and numerous ground based tests were included. Focus was placed on developing an actual electrophoretic separator of the continuous flow type, configured and suitable for flight testing as a space processing applications rocket payload.

Virtual Transgenics: Using a Molecular Biology Simulation to Impact Student Academic Achievement and Attitudes

ERIC Educational Resources Information Center

Shegog, Ross; Lazarus, Melanie M.; Murray, Nancy G.; Diamond, Pamela M.; Sessions, Nathalie; Zsigmond, Eva

2012-01-01

The transgenic mouse model is useful for studying the causes and potential cures for human genetic diseases. Exposing high school biology students to laboratory experience in developing transgenic animal models is logistically prohibitive. Computer-based simulation, however, offers this potential in addition to advantages of fidelity and reach.…

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

Dynamic biological exposure indexes for n-hexane and 2,5-hexanedione, suggested by a physiologically based pharmacokinetic model

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

Perbellini, L.; Mozzo, P.; Olivato, D.

Biological exposure index (BEI) of n-hexane was studied for accuracy using a physiologically based pharmacokinetic (PB-PK) model. The kinetics of n-hexane in alveolar air, blood, urine, and other tissues were simulated for different values of alveolar ventilations and also for constant and variable exposures. The kinetics of 2,5-hexanedione, the toxic n-hexane metabolite, were also simulated. The ranges of n-hexane concentrations in biological media and the urinary concentrations of 2,5-hexanedione are discussed in connection with a mean n-hexane exposure of 180 mg/m3 (50 ppm) (threshold limit value (TLV) suggested by American Conference of Governmental Industrial Hygienists (ACGIH) for 1988-89). The experimentalmore » and field data as well as those predicted by simulation with the PB-PK model were comparable. The physiological-pharmacokinetic simulations are used to propose the dynamic BEIs of n-hexane and 2,5-hexanedione. The use of simulation with PB-PK models enables a better understanding of the limits, advantages, and issues associated with biological monitoring of exposures to industrial solvents.« less

Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics

ERIC Educational Resources Information Center

Kim, Beaumie; Pathak, Suneeta A.; Jacobson, Michael J.; Zhang, Baohui; Gobert, Janice D.

2015-01-01

Model-based reasoning has been introduced as an authentic way of learning science, and many researchers have developed technological tools for learning with models. This paper describes how a model-based tool, "BioLogica"™, was used to facilitate genetics learning in secondary 3-level biology in Singapore. The research team co-designed…

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

DOE PAGES

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; ...

2014-10-16

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.« less

Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

PubMed Central

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.

2014-01-01

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface. PMID:25329157

Integrating interactive computational modeling in biology curricula.

PubMed

Helikar, Tomáš; Cutucache, Christine E; Dahlquist, Lauren M; Herek, Tyler A; Larson, Joshua J; Rogers, Jim A

2015-03-01

While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes. Students can create models of cells, diseases, or pathways themselves or explore existing models. This technology was implemented in both undergraduate and graduate courses as a pilot study to determine the feasibility of such software at the university level. First, a new (In Silico Biology) class was developed to enable students to learn biology by "building and breaking it" via computer models and their simulations. This class and technology also provide a non-intimidating way to incorporate mathematical and computational concepts into a class with students who have a limited mathematical background. Second, we used the technology to mediate the use of simulations and modeling modules as a learning tool for traditional biological concepts, such as T cell differentiation or cell cycle regulation, in existing biology courses. Results of this pilot application suggest that there is promise in the use of computational modeling and software tools such as Cell Collective to provide new teaching methods in biology and contribute to the implementation of the "Vision and Change" call to action in undergraduate biology education by providing a hands-on approach to biology.

Invited review liquid crystal models of biological materials and silk spinning.

PubMed

Rey, Alejandro D; Herrera-Valencia, Edtson E

2012-06-01

A review of thermodynamic, materials science, and rheological liquid crystal models is presented and applied to a wide range of biological liquid crystals, including helicoidal plywoods, biopolymer solutions, and in vivo liquid crystals. The distinguishing characteristics of liquid crystals (self-assembly, packing, defects, functionalities, processability) are discussed in relation to biological materials and the strong correspondence between different synthetic and biological materials is established. Biological polymer processing based on liquid crystalline precursors includes viscoelastic flow to form and shape fibers. Viscoelastic models for nematic and chiral nematics are reviewed and discussed in terms of key parameters that facilitate understanding and quantitative information from optical textures and rheometers. It is shown that viscoelastic modeling the silk spinning process using liquid crystal theories sheds light on textural transitions in the duct of spiders and silk worms as well as on tactoidal drops and interfacial structures. The range and consistency of the predictions demonstrates that the use of mesoscopic liquid crystal models is another tool to develop the science and biomimetic applications of mesogenic biological soft matter. Copyright © 2011 Wiley Periodicals, Inc.

Biologically optimized helium ion plans: calculation approach and its in vitro validation

NASA Astrophysics Data System (ADS)

Mairani, A.; Dokic, I.; Magro, G.; Tessonnier, T.; Kamp, F.; Carlson, D. J.; Ciocca, M.; Cerutti, F.; Sala, P. R.; Ferrari, A.; Böhlen, T. T.; Jäkel, O.; Parodi, K.; Debus, J.; Abdollahi, A.; Haberer, T.

2016-06-01

Treatment planning studies on the biological effect of raster-scanned helium ion beams should be performed, together with their experimental verification, before their clinical application at the Heidelberg Ion Beam Therapy Center (HIT). For this purpose, we introduce a novel calculation approach based on integrating data-driven biological models in our Monte Carlo treatment planning (MCTP) tool. Dealing with a mixed radiation field, the biological effect of the primary 4He ion beams, of the secondary 3He and 4He (Z  =  2) fragments and of the produced protons, deuterons and tritons (Z  =  1) has to be taken into account. A spread-out Bragg peak (SOBP) in water, representative of a clinically-relevant scenario, has been biologically optimized with the MCTP and then delivered at HIT. Predictions of cell survival and RBE for a tumor cell line, characterized by {{(α /β )}\\text{ph}}=5.4 Gy, have been successfully compared against measured clonogenic survival data. The mean absolute survival variation ({μΔ \\text{S}} ) between model predictions and experimental data was 5.3%  ±  0.9%. A sensitivity study, i.e. quantifying the variation of the estimations for the studied plan as a function of the applied phenomenological modelling approach, has been performed. The feasibility of a simpler biological modelling based on dose-averaged LET (linear energy transfer) has been tested. Moreover, comparisons with biophysical models such as the local effect model (LEM) and the repair-misrepair-fixation (RMF) model were performed. {μΔ \\text{S}} values for the LEM and the RMF model were, respectively, 4.5%  ±  0.8% and 5.8%  ±  1.1%. The satisfactorily agreement found in this work for the studied SOBP, representative of clinically-relevant scenario, suggests that the introduced approach could be applied for an accurate estimation of the biological effect for helium ion radiotherapy.

LTI system order reduction approach based on asymptotical equivalence and the Co-operation of biology-related algorithms

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.; Akhmedova, Sh A.

2017-02-01

A novel order reduction method for linear time invariant systems is described. The method is based on reducing the initial problem to an optimization one, using the proposed model representation, and solving the problem with an efficient optimization algorithm. The proposed method of determining the model allows all the parameters of the model with lower order to be identified and by definition, provides the model with the required steady-state. As a powerful optimization tool, the meta-heuristic Co-Operation of Biology-Related Algorithms was used. Experimental results proved that the proposed approach outperforms other approaches and that the reduced order model achieves a high level of accuracy.

Value of biologic therapy: a forecasting model in three disease areas.

PubMed

Paramore, L Clark; Hunter, Craig A; Luce, Bryan R; Nordyke, Robert J; Halbert, R J

2010-01-01

Forecast the return on investment (ROI) for advances in biologic therapies in years 2015 and 2030, based upon impact on disease prevalence, morbidity, and mortality for asthma, diabetes, and colorectal cancer. A deterministic, spreadsheet-based, forecasting model was developed based on trends in demographics and disease epidemiology. 'Return' was defined as reductions in disease burden (prevalence, morbidity, mortality) translated into monetary terms; 'investment' was defined as the incremental costs of biologic therapy advances. Data on disease prevalence, morbidity, mortality, and associated costs were obtained from government survey statistics or published literature. Expected impact of advances in biologic therapies was based on expert opinion. Gains in quality-adjusted life years (QALYs) were valued at $100,000 per QALY. The base case analysis, in which reductions in disease prevalence and mortality predicted by the expert panel are not considered, shows the resulting ROIs remain positive for asthma and diabetes but fall below $1 for colorectal cancer. Analysis involving expert panel predictions indicated positive ROI results for all three diseases at both time points, ranging from $207 for each incremental dollar spent on biologic therapies to treat asthma in 2030, to $4 for each incremental dollar spent on biologic therapies to treat colorectal cancer in 2015. If QALYs are not considered, the resulting ROIs remain positive for all three diseases at both time points. Society may expect substantial returns from investments in innovative biologic therapies. These benefits are most likely to be realized in an environment of appropriate use of new molecules. The potential variance between forecasted (from expert opinion) and actual future health outcomes could be significant. Similarly, the forecasted growth in use of biologic therapies relied upon unvalidated market forecasts.

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.

Reinvigorating Introductory Biology: A Theme-based, Investigative Approach To Teaching Biology Majors.

ERIC Educational Resources Information Center

Norton, Cynthia G.; Gildensoph, Lynne H.; Phillips, Martha M.; Wygal, Deborah D.; Olson, Kurt H.; Pellegrini, John J.; Tweeten, Kathleen A.

1997-01-01

Describes the reform of an introductory biology curriculum to reverse high attrition rates. Objectives include fostering self-directed learning, emphasizing process over content, and offering laboratory experiences that model the way to acquire scientific knowledge. Teaching methods include discussion, group mentoring, laboratory sections, and…

Multidimensional Computerized Adaptive Testing for Indonesia Junior High School Biology

ERIC Educational Resources Information Center

Kuo, Bor-Chen; Daud, Muslem; Yang, Chih-Wei

2015-01-01

This paper describes a curriculum-based multidimensional computerized adaptive test that was developed for Indonesia junior high school Biology. In adherence to the Indonesian curriculum of different Biology dimensions, 300 items was constructed, and then tested to 2238 students. A multidimensional random coefficients multinomial logit model was…

Computer retina that models the primate retina

NASA Astrophysics Data System (ADS)

Shah, Samir; Levine, Martin D.

1994-06-01

At the retinal level, the strategies utilized by biological visual systems allow them to outperform machine vision systems, serving to motivate the design of electronic or `smart' sensors based on similar principles. Design of such sensors in silicon first requires a model of retinal information processing which captures the essential features exhibited by biological retinas. In this paper, a simple retinal model is presented, which qualitatively accounts for the achromatic information processing in the primate cone system. The model exhibits many of the properties found in biological retina such as data reduction through nonuniform sampling, adaptation to a large dynamic range of illumination levels, variation of visual acuity with illumination level, and enhancement of spatio temporal contrast information. The model is validated by replicating experiments commonly performed by electrophysiologists on biological retinas and comparing the response of the computer retina to data from experiments in monkeys. In addition, the response of the model to synthetic images is shown. The experiments demonstrate that the model behaves in a manner qualitatively similar to biological retinas and thus may serve as a basis for the development of an `artificial retina.'

Functional networks inference from rule-based machine learning models.

PubMed

Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

2016-01-01

Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The implementation of our network inference protocol is available at: http://ico2s.org/software/funel.html.

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

Assessing anthropogenic impact on boreal lakes with historical fish species distribution data and hydrogeochemical modeling.

PubMed

Valinia, Salar; Englund, Göran; Moldan, Filip; Futter, Martyn N; Köhler, Stephan J; Bishop, Kevin; Fölster, Jens

2014-09-01

Quantifying the effects of human activity on the natural environment is dependent on credible estimates of reference conditions to define the state of the environment before the onset of adverse human impacts. In Europe, emission controls that aimed at restoring ecological status were based on hindcasts from process-based models or paleolimnological reconstructions. For instance, 1860 is used in Europe as the target for restoration from acidification concerning biological and chemical parameters. A more practical problem is that the historical states of ecosystems and their function cannot be observed directly. Therefore, we (i) compare estimates of acidification based on long-term observations of roach (Rutilus rutilus) populations with hindcast pH from the hydrogeochemical model MAGIC; (ii) discuss policy implications and possible scope for use of long-term archival data for assessing human impacts on the natural environment and (iii) present a novel conceptual model for interpreting the importance of physico-chemical and ecological deviations from reference conditions. Of the 85 lakes studied, 78 were coherently classified by both methods. In 1980, 28 lakes were classified as acidified with the MAGIC model, however, roach was present in 14 of these. In 2010, MAGIC predicted chemical recovery in 50% of the lakes, however roach only recolonized in five lakes after 1990, showing a lag between chemical and biological recovery. Our study is the first study of its kind to use long-term archival biological data in concert with hydrogeochemical modeling for regional assessments of anthropogenic acidification. Based on our results, we show how the conceptual model can be used to understand and prioritize management of physico-chemical and ecological effects of anthropogenic stressors on surface water quality. © 2014 The Authors Global Change Biology Published by John Wiley & Sons Ltd.

Quantum biological channel modeling and capacity calculation.

PubMed

Djordjevic, Ivan B

2012-12-10

Quantum mechanics has an important role in photosynthesis, magnetoreception, and evolution. There were many attempts in an effort to explain the structure of genetic code and transfer of information from DNA to protein by using the concepts of quantum mechanics. The existing biological quantum channel models are not sufficiently general to incorporate all relevant contributions responsible for imperfect protein synthesis. Moreover, the problem of determination of quantum biological channel capacity is still an open problem. To solve these problems, we construct the operator-sum representation of biological channel based on codon basekets (basis vectors), and determine the quantum channel model suitable for study of the quantum biological channel capacity and beyond. The transcription process, DNA point mutations, insertions, deletions, and translation are interpreted as the quantum noise processes. The various types of quantum errors are classified into several broad categories: (i) storage errors that occur in DNA itself as it represents an imperfect storage of genetic information, (ii) replication errors introduced during DNA replication process, (iii) transcription errors introduced during DNA to mRNA transcription, and (iv) translation errors introduced during the translation process. By using this model, we determine the biological quantum channel capacity and compare it against corresponding classical biological channel capacity. We demonstrate that the quantum biological channel capacity is higher than the classical one, for a coherent quantum channel model, suggesting that quantum effects have an important role in biological systems. The proposed model is of crucial importance towards future study of quantum DNA error correction, developing quantum mechanical model of aging, developing the quantum mechanical models for tumors/cancer, and study of intracellular dynamics in general.

Models to study gravitational biology of Mammalian reproduction

NASA Technical Reports Server (NTRS)

Tou, Janet; Ronca, April; Grindeland, Richard; Wade, Charles

2002-01-01

Mammalian reproduction evolved within Earth's 1-g gravitational field. As we move closer to the reality of space habitation, there is growing scientific interest in how different gravitational states influence reproduction in mammals. Habitation of space and extended spaceflight missions require prolonged exposure to decreased gravity (hypogravity, i.e., weightlessness). Lift-off and re-entry of the spacecraft are associated with exposure to increased gravity (hypergravity). Existing data suggest that spaceflight is associated with a constellation of changes in reproductive physiology and function. However, limited spaceflight opportunities and confounding effects of various nongravitational factors associated with spaceflight (i.e., radiation, stress) have led to the development of ground-based models for studying the effects of altered gravity on biological systems. Human bed rest and rodent hindlimb unloading paradigms are used to study exposure to hypogravity. Centrifugation is used to study hypergravity. Here, we review the results of spaceflight and ground-based models of altered gravity on reproductive physiology. Studies utilizing ground-based models that simulate hyper- and hypogravity have produced reproductive results similar to those obtained from spaceflight and are contributing new information on biological responses across the gravity continuum, thereby confirming the appropriateness of these models for studying reproductive responses to altered gravity and the underlying mechanisms of these responses. Together, these unique tools are yielding new insights into the gravitational biology of reproduction in mammals.

A method to identify and analyze biological programs through automated reasoning

PubMed Central

Yordanov, Boyan; Dunn, Sara-Jane; Kugler, Hillel; Smith, Austin; Martello, Graziano; Emmott, Stephen

2016-01-01

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function. PMID:27668090

Comparative systems biology between human and animal models based on next-generation sequencing methods.

PubMed

Zhao, Yu-Qi; Li, Gong-Hua; Huang, Jing-Fei

2013-04-01

Animal models provide myriad benefits to both experimental and clinical research. Unfortunately, in many situations, they fall short of expected results or provide contradictory results. In part, this can be the result of traditional molecular biological approaches that are relatively inefficient in elucidating underlying molecular mechanism. To improve the efficacy of animal models, a technological breakthrough is required. The growing availability and application of the high-throughput methods make systematic comparisons between human and animal models easier to perform. In the present study, we introduce the concept of the comparative systems biology, which we define as "comparisons of biological systems in different states or species used to achieve an integrated understanding of life forms with all their characteristic complexity of interactions at multiple levels". Furthermore, we discuss the applications of RNA-seq and ChIP-seq technologies to comparative systems biology between human and animal models and assess the potential applications for this approach in the future studies.

Mathematical Manipulative Models: In Defense of “Beanbag Biology”

PubMed Central

Gaff, Holly; Weisstein, Anton E.

2010-01-01

Mathematical manipulative models have had a long history of influence in biological research and in secondary school education, but they are frequently neglected in undergraduate biology education. By linking mathematical manipulative models in a four-step process—1) use of physical manipulatives, 2) interactive exploration of computer simulations, 3) derivation of mathematical relationships from core principles, and 4) analysis of real data sets—we demonstrate a process that we have shared in biological faculty development workshops led by staff from the BioQUEST Curriculum Consortium over the past 24 yr. We built this approach based upon a broad survey of literature in mathematical educational research that has convincingly demonstrated the utility of multiple models that involve physical, kinesthetic learning to actual data and interactive simulations. Two projects that use this approach are introduced: The Biological Excel Simulations and Tools in Exploratory, Experiential Mathematics (ESTEEM) Project (http://bioquest.org/esteem) and Numerical Undergraduate Mathematical Biology Education (NUMB3R5 COUNT; http://bioquest.org/numberscount). Examples here emphasize genetics, ecology, population biology, photosynthesis, cancer, and epidemiology. Mathematical manipulative models help learners break through prior fears to develop an appreciation for how mathematical reasoning informs problem solving, inference, and precise communication in biology and enhance the diversity of quantitative biology education. PMID:20810952

Combining correlative and mechanistic habitat suitability models to improve ecological compensation.

PubMed

Meineri, Eric; Deville, Anne-Sophie; Grémillet, David; Gauthier-Clerc, Michel; Béchet, Arnaud

2015-02-01

Only a few studies have shown positive impacts of ecological compensation on species dynamics affected by human activities. We argue that this is due to inappropriate methods used to forecast required compensation in environmental impact assessments. These assessments are mostly descriptive and only valid at limited spatial and temporal scales. However, habitat suitability models developed to predict the impacts of environmental changes on potential species' distributions should provide rigorous science-based tools for compensation planning. Here we describe the two main classes of predictive models: correlative models and individual-based mechanistic models. We show how these models can be used alone or synoptically to improve compensation planning. While correlative models are easier to implement, they tend to ignore underlying ecological processes and lack accuracy. On the contrary, individual-based mechanistic models can integrate biological interactions, dispersal ability and adaptation. Moreover, among mechanistic models, those considering animal energy balance are particularly efficient at predicting the impact of foraging habitat loss. However, mechanistic models require more field data compared to correlative models. Hence we present two approaches which combine both methods for compensation planning, especially in relation to the spatial scale considered. We show how the availability of biological databases and software enabling fast and accurate population projections could be advantageously used to assess ecological compensation requirement efficiently in environmental impact assessments. © 2014 The Authors. Biological Reviews © 2014 Cambridge Philosophical Society.

Payao: a community platform for SBML pathway model curation

PubMed Central

Matsuoka, Yukiko; Ghosh, Samik; Kikuchi, Norihiro; Kitano, Hiroaki

2010-01-01

Summary: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members. Availability and implementation: Freely available for model curation service at http://www.payaologue.org. Web site implemented in Seaser Framework 2.0 with S2Flex2, MySQL 5.0 and Tomcat 5.5, with all major browsers supported. Contact: kitano@sbi.jp PMID:20371497

Incorporating biologically based models into assessments of risk from chemical contaminants

NASA Technical Reports Server (NTRS)

Bull, R. J.; Conolly, R. B.; De Marini, D. M.; MacPhail, R. C.; Ohanian, E. V.; Swenberg, J. A.

1993-01-01

The general approach to assessment of risk from chemical contaminants in drinking water involves three steps: hazard identification, exposure assessment, and dose-response assessment. Traditionally, the risks to humans associated with different levels of a chemical have been derived from the toxic responses observed in animals. It is becoming increasingly clear, however, that further information is needed if risks to humans are to be assessed accurately. Biologically based models help clarify the dose-response relationship and reduce uncertainty.

A program code generator for multiphysics biological simulation using markup languages.

PubMed

Amano, Akira; Kawabata, Masanari; Yamashita, Yoshiharu; Rusty Punzalan, Florencio; Shimayoshi, Takao; Kuwabara, Hiroaki; Kunieda, Yoshitoshi

2012-01-01

To cope with the complexity of the biological function simulation models, model representation with description language is becoming popular. However, simulation software itself becomes complex in these environment, thus, it is difficult to modify the simulation conditions, target computation resources or calculation methods. In the complex biological function simulation software, there are 1) model equations, 2) boundary conditions and 3) calculation schemes. Use of description model file is useful for first point and partly second point, however, third point is difficult to handle for various calculation schemes which is required for simulation models constructed from two or more elementary models. We introduce a simulation software generation system which use description language based description of coupling calculation scheme together with cell model description file. By using this software, we can easily generate biological simulation code with variety of coupling calculation schemes. To show the efficiency of our system, example of coupling calculation scheme with three elementary models are shown.

A Computational Model Predicting Disruption of Blood Vessel Development

PubMed Central

Kleinstreuer, Nicole; Dix, David; Rountree, Michael; Baker, Nancy; Sipes, Nisha; Reif, David; Spencer, Richard; Knudsen, Thomas

2013-01-01

Vascular development is a complex process regulated by dynamic biological networks that vary in topology and state across different tissues and developmental stages. Signals regulating de novo blood vessel formation (vasculogenesis) and remodeling (angiogenesis) come from a variety of biological pathways linked to endothelial cell (EC) behavior, extracellular matrix (ECM) remodeling and the local generation of chemokines and growth factors. Simulating these interactions at a systems level requires sufficient biological detail about the relevant molecular pathways and associated cellular behaviors, and tractable computational models that offset mathematical and biological complexity. Here, we describe a novel multicellular agent-based model of vasculogenesis using the CompuCell3D (http://www.compucell3d.org/) modeling environment supplemented with semi-automatic knowledgebase creation. The model incorporates vascular endothelial growth factor signals, pro- and anti-angiogenic inflammatory chemokine signals, and the plasminogen activating system of enzymes and proteases linked to ECM interactions, to simulate nascent EC organization, growth and remodeling. The model was shown to recapitulate stereotypical capillary plexus formation and structural emergence of non-coded cellular behaviors, such as a heterologous bridging phenomenon linking endothelial tip cells together during formation of polygonal endothelial cords. Molecular targets in the computational model were mapped to signatures of vascular disruption derived from in vitro chemical profiling using the EPA's ToxCast high-throughput screening (HTS) dataset. Simulating the HTS data with the cell-agent based model of vascular development predicted adverse effects of a reference anti-angiogenic thalidomide analog, 5HPP-33, on in vitro angiogenesis with respect to both concentration-response and morphological consequences. These findings support the utility of cell agent-based models for simulating a morphogenetic series of events and for the first time demonstrate the applicability of these models for predictive toxicology. PMID:23592958

Students' Usability Evaluation of a Web-Based Tutorial Program for College Biology Problem Solving

ERIC Educational Resources Information Center

Kim, H. S.; Prevost, L.; Lemons, P. P.

2015-01-01

The understanding of core concepts and processes of science in solving problems is important to successful learning in biology. We have designed and developed a Web-based, self-directed tutorial program, "SOLVEIT," that provides various scaffolds (e.g., prompts, expert models, visual guidance) to help college students enhance their…

Agent-Based Phytoplankton Models of Cellular and Population Processes: Fostering Individual-Based Learning in Undergraduate Research

NASA Astrophysics Data System (ADS)

Berges, J. A.; Raphael, T.; Rafa Todd, C. S.; Bate, T. C.; Hellweger, F. L.

2016-02-01

Engaging undergraduate students in research projects that require expertise in multiple disciplines (e.g. cell biology, population ecology, and mathematical modeling) can be challenging because they have often not developed the expertise that allows them to participate at a satisfying level. Use of agent-based modeling can allow exploration of concepts at more intuitive levels, and encourage experimentation that emphasizes processes over computational skills. Over the past several years, we have involved undergraduate students in projects examining both ecological and cell biological aspects of aquatic microbial biology, using the freely-downloadable, agent-based modeling environment NetLogo (https://ccl.northwestern.edu/netlogo/). In Netlogo, actions of large numbers of individuals can be simulated, leading to complex systems with emergent behavior. The interface features appealing graphics, monitors, and control structures. In one example, a group of sophomores in a BioMathematics program developed an agent-based model of phytoplankton population dynamics in a pond ecosystem, motivated by observed macroscopic changes in cell numbers (due to growth and death), and driven by responses to irradiance, temperature and a limiting nutrient. In a second example, junior and senior undergraduates conducting Independent Studies created a model of the intracellular processes governing stress and cell death for individual phytoplankton cells (based on parameters derived from experiments using single-cell culturing and flow cytometry), and then this model was embedded in the agents in the pond ecosystem model. In our experience, students with a range of mathematical abilities learned to code quickly and could use the software with varying degrees of sophistication, for example, creation of spatially-explicit two and three-dimensional models. Skills developed quickly and transferred readily to other platforms (e.g. Matlab).

Modeling biochemical transformation processes and information processing with Narrator.

PubMed

Mandel, Johannes J; Fuss, Hendrik; Palfreyman, Niall M; Dubitzky, Werner

2007-03-27

Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from http://www.narrator-tool.org.

Modeling biochemical transformation processes and information processing with Narrator

PubMed Central

Mandel, Johannes J; Fuß, Hendrik; Palfreyman, Niall M; Dubitzky, Werner

2007-01-01

Background Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. Results Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. Conclusion Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from . PMID:17389034

Translational Systems Biology and Voice Pathophysiology

PubMed Central

Li, Nicole Y. K.; Abbott, Katherine Verdolini; Rosen, Clark; An, Gary; Hebda, Patricia A.; Vodovotz, Yoram

2011-01-01

Objectives/Hypothesis Personalized medicine has been called upon to tailor healthcare to an individual's needs. Evidence-based medicine (EBM) has advocated using randomized clinical trials with large populations to evaluate treatment effects. However, due to large variations across patients, the results are likely not to apply to an individual patient. We suggest that a complementary, systems biology approach using computational modeling may help tackle biological complexity in order to improve ultimate patient care. The purpose of the article is: 1) to review the pros and cons of EBM, and 2) to discuss the alternative systems biology method and present its utility in clinical voice research. Study Design Tutorial Methods Literature review and discussion. Results We propose that translational systems biology can address many of the limitations of EBM pertinent to voice and other health care domains, and thus complement current health research models. In particular, recent work using mathematical modeling suggests that systems biology has the ability to quantify the highly complex biologic processes underlying voice pathophysiology. Recent data support the premise that this approach can be applied specifically in the case of phonotrauma and surgically induced vocal fold trauma, and may have particular power to address personalized medicine. Conclusions We propose that evidence around vocal health and disease be expanded beyond a population-based method to consider more fully issues of complexity and systems interactions, especially in implementing personalized medicine in voice care and beyond. PMID:20025041

In silico model-based inference: a contemporary approach for hypothesis testing in network biology

PubMed Central

Klinke, David J.

2014-01-01

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900’s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. PMID:25139179

In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

PubMed

Klinke, David J

2014-01-01

Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. © 2014 American Institute of Chemical Engineers.

Module-based multiscale simulation of angiogenesis in skeletal muscle

PubMed Central

2011-01-01

Background Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem. Results We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis. Conclusions This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions. PMID:21463529

SynBioSS-aided design of synthetic biological constructs.

PubMed

Kaznessis, Yiannis N

2011-01-01

We present walkthrough examples of using SynBioSS to design, model, and simulate synthetic gene regulatory networks. SynBioSS stands for Synthetic Biology Software Suite, a platform that is publicly available with Open Licenses at www.synbioss.org. An important aim of computational synthetic biology is the development of a mathematical modeling formalism that is applicable to a wide variety of simple synthetic biological constructs. SynBioSS-based modeling of biomolecular ensembles that interact away from the thermodynamic limit and not necessarily at steady state affords for a theoretical framework that is generally applicable to known synthetic biological systems, such as bistable switches, AND gates, and oscillators. Here, we discuss how SynBioSS creates links between DNA sequences and targeted dynamic phenotypes of these simple systems. Copyright © 2011 Elsevier Inc. All rights reserved.

[Construction of individual-based ecological model for Scomber japonicas at its early growth stages in East China Sea].

PubMed

Li, Yue-Song; Chen, Xin-Jun; Yang, Hong

2012-06-01

By adopting FVCOM-simulated 3-D physical field and based on the biological processes of chub mackerel (Scomber japonicas) in its early life history from the individual-based biological model, the individual-based ecological model for S. japonicas at its early growth stages in the East China Sea was constructed through coupling the physical field in March-July with the biological model by the method of Lagrange particle tracking. The model constructed could well simulate the transport process and abundance distribution of S. japonicas eggs and larvae. The Taiwan Warm Current, Kuroshio, and Tsushima Strait Warm Current directly affected the transport process and distribution of the eggs and larvae, and indirectly affected the growth and survive of the eggs and larvae through the transport to the nursery grounds with different water temperature and foods. The spawning grounds in southern East China Sea made more contributions to the recruitment to the fishing grounds in northeast East China Sea, but less to the Yangtze estuary and Zhoushan Island. The northwestern and southwestern parts of spawning grounds had strong connectivity with the nursery grounds of Cheju and Tsushima Straits, whereas the northeastern and southeastern parts of the spawning ground had strong connectivity with the nursery grounds of Kyushu and Pacific Ocean.

Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

PubMed Central

Shahlaei, M.; Saghaie, L.

2014-01-01

A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

A sense of life: computational and experimental investigations with models of biochemical and evolutionary processes.

PubMed

Mishra, Bud; Daruwala, Raoul-Sam; Zhou, Yi; Ugel, Nadia; Policriti, Alberto; Antoniotti, Marco; Paxia, Salvatore; Rejali, Marc; Rudra, Archisman; Cherepinsky, Vera; Silver, Naomi; Casey, William; Piazza, Carla; Simeoni, Marta; Barbano, Paolo; Spivak, Marina; Feng, Jiawu; Gill, Ofer; Venkatesh, Mysore; Cheng, Fang; Sun, Bing; Ioniata, Iuliana; Anantharaman, Thomas; Hubbard, E Jane Albert; Pnueli, Amir; Harel, David; Chandru, Vijay; Hariharan, Ramesh; Wigler, Michael; Park, Frank; Lin, Shih-Chieh; Lazebnik, Yuri; Winkler, Franz; Cantor, Charles R; Carbone, Alessandra; Gromov, Mikhael

2003-01-01

We collaborate in a research program aimed at creating a rigorous framework, experimental infrastructure, and computational environment for understanding, experimenting with, manipulating, and modifying a diverse set of fundamental biological processes at multiple scales and spatio-temporal modes. The novelty of our research is based on an approach that (i) requires coevolution of experimental science and theoretical techniques and (ii) exploits a certain universality in biology guided by a parsimonious model of evolutionary mechanisms operating at the genomic level and manifesting at the proteomic, transcriptomic, phylogenic, and other higher levels. Our current program in "systems biology" endeavors to marry large-scale biological experiments with the tools to ponder and reason about large, complex, and subtle natural systems. To achieve this ambitious goal, ideas and concepts are combined from many different fields: biological experimentation, applied mathematical modeling, computational reasoning schemes, and large-scale numerical and symbolic simulations. From a biological viewpoint, the basic issues are many: (i) understanding common and shared structural motifs among biological processes; (ii) modeling biological noise due to interactions among a small number of key molecules or loss of synchrony; (iii) explaining the robustness of these systems in spite of such noise; and (iv) cataloging multistatic behavior and adaptation exhibited by many biological processes.

Modeling small cell lung cancer (SCLC) biology through deterministic and stochastic mathematical models.

PubMed

Salgia, Ravi; Mambetsariev, Isa; Hewelt, Blake; Achuthan, Srisairam; Li, Haiqing; Poroyko, Valeriy; Wang, Yingyu; Sattler, Martin

2018-05-25

Mathematical cancer models are immensely powerful tools that are based in part on the fractal nature of biological structures, such as the geometry of the lung. Cancers of the lung provide an opportune model to develop and apply algorithms that capture changes and disease phenotypes. We reviewed mathematical models that have been developed for biological sciences and applied them in the context of small cell lung cancer (SCLC) growth, mutational heterogeneity, and mechanisms of metastasis. The ultimate goal is to develop the stochastic and deterministic nature of this disease, to link this comprehensive set of tools back to its fractalness and to provide a platform for accurate biomarker development. These techniques may be particularly useful in the context of drug development research, such as combination with existing omics approaches. The integration of these tools will be important to further understand the biology of SCLC and ultimately develop novel therapeutics.

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.

Dietary antioxidant synergy in chemical and biological systems.

PubMed

Wang, Sunan; Zhu, Fan

2017-07-24

Antioxidant (AOX) synergies have been much reported in chemical ("test-tube" based assays focusing on pure chemicals), biological (tissue culture, animal and clinical models), and food systems during the past decade. Tentative synergies differ from each other due to the composition of AOX and the quantification methods. Regeneration mechanism responsible for synergy in chemical systems has been discussed. Solvent effects could contribute to the artifacts of synergy observed in the chemical models. Synergy in chemical models may hardly be relevant to biological systems that have been much less studied. Apparent discrepancies exist in understanding the molecular mechanisms in both chemical and biological systems. This review discusses diverse variables associated with AOX synergy and molecular scenarios for explanation. Future research to better utilize the synergy is suggested.

A Computational Approach to Increase Time Scales in Brownian Dynamics–Based Reaction-Diffusion Modeling

PubMed Central

Frazier, Zachary

2012-01-01

Abstract Particle-based Brownian dynamics simulations offer the opportunity to not only simulate diffusion of particles but also the reactions between them. They therefore provide an opportunity to integrate varied biological data into spatially explicit models of biological processes, such as signal transduction or mitosis. However, particle based reaction-diffusion methods often are hampered by the relatively small time step needed for accurate description of the reaction-diffusion framework. Such small time steps often prevent simulation times that are relevant for biological processes. It is therefore of great importance to develop reaction-diffusion methods that tolerate larger time steps while maintaining relatively high accuracy. Here, we provide an algorithm, which detects potential particle collisions prior to a BD-based particle displacement and at the same time rigorously obeys the detailed balance rule of equilibrium reactions. We can show that for reaction-diffusion processes of particles mimicking proteins, the method can increase the typical BD time step by an order of magnitude while maintaining similar accuracy in the reaction diffusion modelling. PMID:22697237

The Case for Cyberlearning: Genomics (and Dragons!) in the High School Biology Classroom

ERIC Educational Resources Information Center

Southworth, Meghan; Mokros, Jan; Dorsey, Chad; Smith, Randy

2010-01-01

GENIQUEST is a cyberlearning computer program that allows students to investigate biological data using a research-based instructional model. In this article, the authors make the case for using cyberlearning to teach students about the rapidly growing fields of genomics and computational biology. (Contains 2 figures and 1 online resource.)

Primary Literature as a Basis for a High-School Biology Curriculum.

ERIC Educational Resources Information Center

Yarden, Anat; Brill, Gilat; Falk, Hedda

2001-01-01

Adopts primary literature as a means of developing scientific literacy among high-school biology majors. Reports on the development and implementation of a primary literature-based curriculum in developmental biology. Discusses the process of adapting original research articles to the high-school level, as well as a conversational model developed…

Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology.

PubMed

Koutinas, Michalis; Kiparissides, Alexandros; Pistikopoulos, Efstratios N; Mantalaris, Athanasios

2012-01-01

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals.

Bioprocess systems engineering: transferring traditional process engineering principles to industrial biotechnology

PubMed Central

Koutinas, Michalis; Kiparissides, Alexandros; Pistikopoulos, Efstratios N.; Mantalaris, Athanasios

2013-01-01

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals. PMID:24688682

Cancer systems biology: signal processing for cancer research

PubMed Central

Yli-Harja, Olli; Ylipää, Antti; Nykter, Matti; Zhang, Wei

2011-01-01

In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts. PMID:21439242

Toward the First Data Acquisition Standard in Synthetic Biology.

PubMed

Sainz de Murieta, Iñaki; Bultelle, Matthieu; Kitney, Richard I

2016-08-19

This paper describes the development of a new data acquisition standard for synthetic biology. This comprises the creation of a methodology that is designed to capture all the data, metadata, and protocol information associated with biopart characterization experiments. The new standard, called DICOM-SB, is based on the highly successful Digital Imaging and Communications in Medicine (DICOM) standard in medicine. A data model is described which has been specifically developed for synthetic biology. The model is a modular, extensible data model for the experimental process, which can optimize data storage for large amounts of data. DICOM-SB also includes services orientated toward the automatic exchange of data and information between modalities and repositories. DICOM-SB has been developed in the context of systematic design in synthetic biology, which is based on the engineering principles of modularity, standardization, and characterization. The systematic design approach utilizes the design, build, test, and learn design cycle paradigm. DICOM-SB has been designed to be compatible with and complementary to other standards in synthetic biology, including SBOL. In this regard, the software provides effective interoperability. The new standard has been tested by experiments and data exchange between Nanyang Technological University in Singapore and Imperial College London.

Biocellion: accelerating computer simulation of multicellular biological system models

PubMed Central

Kang, Seunghwa; Kahan, Simon; McDermott, Jason; Flann, Nicholas; Shmulevich, Ilya

2014-01-01

Motivation: Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. Results: We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Availability and implementation: Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information. Contact: seunghwa.kang@pnnl.gov PMID:25064572

Rule-Based Simulation of Multi-Cellular Biological Systems—A Review of Modeling Techniques

PubMed Central

Hwang, Minki; Garbey, Marc; Berceli, Scott A.; Tran-Son-Tay, Roger

2011-01-01

Emergent behaviors of multi-cellular biological systems (MCBS) result from the behaviors of each individual cells and their interactions with other cells and with the environment. Modeling MCBS requires incorporating these complex interactions among the individual cells and the environment. Modeling approaches for MCBS can be grouped into two categories: continuum models and cell-based models. Continuum models usually take the form of partial differential equations, and the model equations provide insight into the relationship among the components in the system. Cell-based models simulate each individual cell behavior and interactions among them enabling the observation of the emergent system behavior. This review focuses on the cell-based models of MCBS, and especially, the technical aspect of the rule-based simulation method for MCBS is reviewed. How to implement the cell behaviors and the interactions with other cells and with the environment into the computational domain is discussed. The cell behaviors reviewed in this paper are division, migration, apoptosis/necrosis, and differentiation. The environmental factors such as extracellular matrix, chemicals, microvasculature, and forces are also discussed. Application examples of these cell behaviors and interactions are presented. PMID:21369345

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales. PMID:23248599

Modeling of contact theories for the manipulation of biological micro/nanoparticles in the form of circular crowned rollers based on the atomic force microscope

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

Korayem, M. H.; Khaksar, H.; Taheri, M.

2013-11-14

This article has dealt with the development and modeling of various contact theories for biological nanoparticles shaped as cylinders and circular crowned rollers for application in the manipulation of different biological micro/nanoparticles based on Atomic Force Microscope. First, the effective contact forces were simulated, and their impact on contact mechanics simulation was investigated. In the next step, the Hertz contact model was simulated and compared for gold and DNA nanoparticles with the three types of spherical, cylindrical, and circular crowned roller type contact geometries. Then by reducing the length of the cylindrical section in the circular crowned roller geometry, themore » geometry of the body was made to approach that of a sphere, and the results were compared for DNA nanoparticles. To anticipatory validate the developed theories, the results of the cylindrical and the circular crowned roller contacts were compared with the results of the existing spherical contact simulations. Following the development of these contact models for the manipulation of various biological micro/nanoparticles, the cylindrical and the circular crowned roller type contact theories were modeled based on the theories of Lundberg, Dowson, Nikpur, Heoprich, and Hertz for the manipulation of biological micro/nanoparticles. Then, for a more accurate validation, the results obtained from the simulations were compared with those obtained by the finite element method and with the experimental results available in previous articles. The previous research works on the simulation of nanomanipulation have mainly investigated the contact theories used in the manipulation of spherical micro/nanoparticles. However since in real biomanipulation situations, biological micro/nanoparticles of more complex shapes need to be displaced in biological environments, this article therefore has modeled and compared, for the first time, different contact theories for use in the biomanipulation of cylindrical and circular crowned roller shaped micro/nanoparticles. The results of models indicate that the contact model of Hertz achieves the largest amount of deformation for the DNA nanoparticle in cylindrical form and the contact model of Heoprich achieves the largest deformation for the circular crowned roller shaped DNA. Of course, this finding is not always true for the other nanoparticles; and considering the mechanical and environmental characteristics, different results can be obtained. Also, by comparing the deformations of different types of nanoparticles, it was determined that the platelet type nanoparticles display the highest degree of deformation in all the considered models, due to their particular mechanical characteristics.« less

Modelling of pathologies of the nervous system by the example of computational and electronic models of elementary nervous systems

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

Shumilov, V. N., E-mail: vnshumilov@rambler.ru; Syryamkin, V. I., E-mail: maximus70sir@gmail.com; Syryamkin, M. V., E-mail: maximus70sir@gmail.com

The paper puts forward principles of action of devices operating similarly to the nervous system and the brain of biological systems. We propose an alternative method of studying diseases of the nervous system, which may significantly influence prevention, medical treatment, or at least retardation of development of these diseases. This alternative is to use computational and electronic models of the nervous system. Within this approach, we represent the brain in the form of a huge electrical circuit composed of active units, namely, neuron-like units and connections between them. As a result, we created computational and electronic models of elementary nervousmore » systems, which are based on the principles of functioning of biological nervous systems that we have put forward. Our models demonstrate reactions to external stimuli and their change similarly to the behavior of simplest biological organisms. The models possess the ability of self-training and retraining in real time without human intervention and switching operation/training modes. In our models, training and memorization take place constantly under the influence of stimuli on the organism. Training is without any interruption and switching operation modes. Training and formation of new reflexes occur by means of formation of new connections between excited neurons, between which formation of connections is physically possible. Connections are formed without external influence. They are formed under the influence of local causes. Connections are formed between outputs and inputs of two neurons, when the difference between output and input potentials of excited neurons exceeds a value sufficient to form a new connection. On these grounds, we suggest that the proposed principles truly reflect mechanisms of functioning of biological nervous systems and the brain. In order to confirm the correspondence of the proposed principles to biological nature, we carry out experiments for the study of processes of formation of connections between neurons in simplest biological objects. Based on the correspondence of function of the created models to function of biological nervous systems we suggest the use of computational and electronic models of the brain for the study of its function under normal and pathological conditions, because operating principles of the models are built on principles imitating the function of biological nervous systems and the brain.« less

Modelling of pathologies of the nervous system by the example of computational and electronic models of elementary nervous systems

NASA Astrophysics Data System (ADS)

Shumilov, V. N.; Syryamkin, V. I.; Syryamkin, M. V.

2015-11-01

The paper puts forward principles of action of devices operating similarly to the nervous system and the brain of biological systems. We propose an alternative method of studying diseases of the nervous system, which may significantly influence prevention, medical treatment, or at least retardation of development of these diseases. This alternative is to use computational and electronic models of the nervous system. Within this approach, we represent the brain in the form of a huge electrical circuit composed of active units, namely, neuron-like units and connections between them. As a result, we created computational and electronic models of elementary nervous systems, which are based on the principles of functioning of biological nervous systems that we have put forward. Our models demonstrate reactions to external stimuli and their change similarly to the behavior of simplest biological organisms. The models possess the ability of self-training and retraining in real time without human intervention and switching operation/training modes. In our models, training and memorization take place constantly under the influence of stimuli on the organism. Training is without any interruption and switching operation modes. Training and formation of new reflexes occur by means of formation of new connections between excited neurons, between which formation of connections is physically possible. Connections are formed without external influence. They are formed under the influence of local causes. Connections are formed between outputs and inputs of two neurons, when the difference between output and input potentials of excited neurons exceeds a value sufficient to form a new connection. On these grounds, we suggest that the proposed principles truly reflect mechanisms of functioning of biological nervous systems and the brain. In order to confirm the correspondence of the proposed principles to biological nature, we carry out experiments for the study of processes of formation of connections between neurons in simplest biological objects. Based on the correspondence of function of the created models to function of biological nervous systems we suggest the use of computational and electronic models of the brain for the study of its function under normal and pathological conditions, because operating principles of the models are built on principles imitating the function of biological nervous systems and the brain.

Terrestrial Slugs as a Model Organism for Inquiry-Based Experimentation in a Majors General Biology Laboratory

ERIC Educational Resources Information Center

Peters, Brenda J.; Blair, Amy C.

2013-01-01

Many biology educators at the undergraduate level are revamping their laboratory curricula to incorporate inquiry-based research experiences so that students can directly participate in the process of science and improve their scientific reasoning skills. Slugs are an ideal organism for use in such a student-directed, hypothesis-driven experience.…

Beyond the Central Dogma: Model-Based Learning of How Genes Determine Phenotypes

ERIC Educational Resources Information Center

Reinagel, Adam; Speth, Elena Bray

2016-01-01

In an introductory biology course, we implemented a learner-centered, model-based pedagogy that frequently engaged students in building conceptual models to explain how genes determine phenotypes. Model-building tasks were incorporated within case studies and aimed at eliciting students' understanding of 1) the origin of variation in a population…

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Treesearch

Louis R. Iverson; Frank R. Thompson; Stephen Matthews; Matthew Peters; Anantha Prasad; William D. Dijak; Jacob Fraser; Wen J. Wang; Brice Hanberry; Hong He; Maria Janowiak; Patricia Butler; Leslie Brandt; Chris Swanston

2016-01-01

Context. Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process...

FALCON: a toolbox for the fast contextualization of logical networks

PubMed Central

De Landtsheer, Sébastien; Trairatphisan, Panuwat; Lucarelli, Philippe; Sauter, Thomas

2017-01-01

Abstract Motivation Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. Results We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. Availability and implementation FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. Contact thomas.sauter@uni.lu Supplementary information Supplementary data are available at Bioinformatics online. PMID:28673016

FALCON: a toolbox for the fast contextualization of logical networks.

PubMed

De Landtsheer, Sébastien; Trairatphisan, Panuwat; Lucarelli, Philippe; Sauter, Thomas

2017-11-01

Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. thomas.sauter@uni.lu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Emerging systems biology approaches in nanotoxicology: Towards a mechanism-based understanding of nanomaterial hazard and risk

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

Costa, Pedro M.; Fadeel, Bengt, E-mail: Bengt.Fade

Engineered nanomaterials are being developed for a variety of technological applications. However, the increasing use of nanomaterials in society has led to concerns about their potential adverse effects on human health and the environment. During the first decade of nanotoxicological research, the realization has emerged that effective risk assessment of the multitudes of new nanomaterials would benefit from a comprehensive understanding of their toxicological mechanisms, which is difficult to achieve with traditional, low-throughput, single end-point oriented approaches. Therefore, systems biology approaches are being progressively applied within the nano(eco)toxicological sciences. This novel paradigm implies that the study of biological systems shouldmore » be integrative resulting in quantitative and predictive models of nanomaterial behaviour in a biological system. To this end, global ‘omics’ approaches with which to assess changes in genes, proteins, metabolites, etc. are deployed allowing for computational modelling of the biological effects of nanomaterials. Here, we highlight omics and systems biology studies in nanotoxicology, aiming towards the implementation of a systems nanotoxicology and mechanism-based risk assessment of nanomaterials. - Highlights: • Systems nanotoxicology is a multi-disciplinary approach to quantitative modelling. • Transcriptomics, proteomics and metabolomics remain the most common methods. • Global “omics” techniques should be coupled to computational modelling approaches. • The discovery of nano-specific toxicity pathways and biomarkers is a prioritized goal. • Overall, experimental nanosafety research must endeavour reproducibility and relevance.« less

Mathematical biology modules based on modern molecular biology and modern discrete mathematics.

PubMed

Robeva, Raina; Davies, Robin; Hodge, Terrell; Enyedi, Alexander

2010-01-01

We describe an ongoing collaborative curriculum materials development project between Sweet Briar College and Western Michigan University, with support from the National Science Foundation. We present a collection of modules under development that can be used in existing mathematics and biology courses, and we address a critical national need to introduce students to mathematical methods beyond the interface of biology with calculus. Based on ongoing research, and designed to use the project-based-learning approach, the modules highlight applications of modern discrete mathematics and algebraic statistics to pressing problems in molecular biology. For the majority of projects, calculus is not a required prerequisite and, due to the modest amount of mathematical background needed for some of the modules, the materials can be used for an early introduction to mathematical modeling. At the same time, most modules are connected with topics in linear and abstract algebra, algebraic geometry, and probability, and they can be used as meaningful applied introductions into the relevant advanced-level mathematics courses. Open-source software is used to facilitate the relevant computations. As a detailed example, we outline a module that focuses on Boolean models of the lac operon network.

Mathematical Biology Modules Based on Modern Molecular Biology and Modern Discrete Mathematics

PubMed Central

Davies, Robin; Hodge, Terrell; Enyedi, Alexander

2010-01-01

We describe an ongoing collaborative curriculum materials development project between Sweet Briar College and Western Michigan University, with support from the National Science Foundation. We present a collection of modules under development that can be used in existing mathematics and biology courses, and we address a critical national need to introduce students to mathematical methods beyond the interface of biology with calculus. Based on ongoing research, and designed to use the project-based-learning approach, the modules highlight applications of modern discrete mathematics and algebraic statistics to pressing problems in molecular biology. For the majority of projects, calculus is not a required prerequisite and, due to the modest amount of mathematical background needed for some of the modules, the materials can be used for an early introduction to mathematical modeling. At the same time, most modules are connected with topics in linear and abstract algebra, algebraic geometry, and probability, and they can be used as meaningful applied introductions into the relevant advanced-level mathematics courses. Open-source software is used to facilitate the relevant computations. As a detailed example, we outline a module that focuses on Boolean models of the lac operon network. PMID:20810955

Design of synthetic biological logic circuits based on evolutionary algorithm.

PubMed

Chuang, Chia-Hua; Lin, Chun-Liang; Chang, Yen-Chang; Jennawasin, Tanagorn; Chen, Po-Kuei

2013-08-01

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose.

Multi-level and hybrid modelling approaches for systems biology.

PubMed

Bardini, R; Politano, G; Benso, A; Di Carlo, S

2017-01-01

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

Evaluating and improving count-based population inference: A case study from 31 years of monitoring Sandhill Cranes

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.

2017-01-01

Monitoring animal populations can be difficult. Limited resources often force monitoring programs to rely on unadjusted or smoothed counts as an index of abundance. Smoothing counts is commonly done using a moving-average estimator to dampen sampling variation. These indices are commonly used to inform management decisions, although their reliability is often unknown. We outline a process to evaluate the biological plausibility of annual changes in population counts and indices from a typical monitoring scenario and compare results with a hierarchical Bayesian time series (HBTS) model. We evaluated spring and fall counts, fall indices, and model-based predictions for the Rocky Mountain population (RMP) of Sandhill Cranes (Antigone canadensis) by integrating juvenile recruitment, harvest, and survival into a stochastic stage-based population model. We used simulation to evaluate population indices from the HBTS model and the commonly used 3-yr moving average estimator. We found counts of the RMP to exhibit biologically unrealistic annual change, while the fall population index was largely biologically realistic. HBTS model predictions suggested that the RMP changed little over 31 yr of monitoring, but the pattern depended on assumptions about the observational process. The HBTS model fall population predictions were biologically plausible if observed crane harvest mortality was compensatory up to natural mortality, as empirical evidence suggests. Simulations indicated that the predicted mean of the HBTS model was generally a more reliable estimate of the true population than population indices derived using a moving 3-yr average estimator. Practitioners could gain considerable advantages from modeling population counts using a hierarchical Bayesian autoregressive approach. Advantages would include: (1) obtaining measures of uncertainty; (2) incorporating direct knowledge of the observational and population processes; (3) accommodating missing years of data; and (4) forecasting population size.

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Concepts of Mental Disorders in Trainee Clinical Psychologists.

PubMed

Read, R; Moberly, N J; Salter, D; Broome, M R

2017-03-01

The models of mental disorders held by all mental health professionals are implicit in their attitudes and inform all aspects of theory and practice. The present study aims to explore the attitudes of trainee clinical psychologists towards mental disorders by building on a study conducted by Harland et al. () with psychiatrists. In so doing, the present study contributes to an evidence base that can inform the development of clinical training programs and multidisciplinary working. The Maudsley Attitude Questionnaire was administered in an online survey of trainee clinical psychologists (n = 289). Analyses of variance revealed main effects of model, and of diagnostic category, and a significant interaction effect between model and diagnostic category. Principal component analysis revealed a biological-psychosocial continuum and cognitive/behavioural and psychodynamic/spiritual dimensions. Comparisons with Harland et al.'s () psychiatrists revealed large differences, particularly in biological and social constructionist model endorsement. Results suggest that the attitudes of psychologists and psychiatrists continue to sit at opposite ends of a biological-psychosocial continuum. However, an area of consensus regarding psychotherapeutic models was indicated. Training courses can be reassured that strong opinions tended to reflect the evidence base. Future research with similarly large representative samples from different disciplines would allow findings of the current study to be better contextualized. Copyright © 2016 John Wiley & Sons, Ltd. The models of mental disorders held by clinical psychologists are implicit in their attitudes and inform all aspects of theory and practice. We found that trainee clinical psychologists continue to favour psychosocial over biological understandings of mental disorders, giving the cognitive, behavioural and psychodynamic models equal value overall, and stronger attitudes were supported by the evidence base. We found that trainee clinical psychologists organized their attitudes around a biological-psychosocial continuum and cognitive/behavioural and psychodynamic/spiritual dimensions. These findings may be useful for those involved in developing clinical training programs and multidisciplinary working because they provide an insight into the attitudes of emerging clinical psychologists. Copyright © 2016 John Wiley & Sons, Ltd.

Development of Turbulent Biological Closure Parameterizations

DTIC Science & Technology

2011-09-30

LONG-TERM GOAL: The long-term goals of this project are: (1) to develop a theoretical framework to quantify turbulence induced NPZ interactions. (2) to apply the theory to develop parameterizations to be used in realistic environmental physical biological coupling numerical models. OBJECTIVES: Connect the Goodman and Robinson (2008) statistically based pdf theory to Advection Diffusion Reaction (ADR) modeling of NPZ interaction.

A basis for a visual language for describing, archiving and analyzing functional models of complex biological systems

PubMed Central

Cook, Daniel L; Farley, Joel F; Tapscott, Stephen J

2001-01-01

Background: We propose that a computerized, internet-based graphical description language for systems biology will be essential for describing, archiving and analyzing complex problems of biological function in health and disease. Results: We outline here a conceptual basis for designing such a language and describe BioD, a prototype language that we have used to explore the utility and feasibility of this approach to functional biology. Using example models, we demonstrate that a rather limited lexicon of icons and arrows suffices to describe complex cell-biological systems as discrete models that can be posted and linked on the internet. Conclusions: Given available computer and internet technology, BioD may be implemented as an extensible, multidisciplinary language that can be used to archive functional systems knowledge and be extended to support both qualitative and quantitative functional analysis. PMID:11305940

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

PubMed

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.

Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

Najafi, Ali; Bidkhori, Gholamreza; Bozorgmehr, Joseph H.; Koch, Ina; Masoudi-Nejad, Ali

2014-01-01

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics. PMID:24822031

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES

PubMed Central

Somogyi, Endre; Glazier, James A.

2017-01-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment. PMID:29303160

SBMLmod: a Python-based web application and web service for efficient data integration and model simulation.

PubMed

Schäuble, Sascha; Stavrum, Anne-Kristin; Bockwoldt, Mathias; Puntervoll, Pål; Heiland, Ines

2017-06-24

Systems Biology Markup Language (SBML) is the standard model representation and description language in systems biology. Enriching and analysing systems biology models by integrating the multitude of available data, increases the predictive power of these models. This may be a daunting task, which commonly requires bioinformatic competence and scripting. We present SBMLmod, a Python-based web application and service, that automates integration of high throughput data into SBML models. Subsequent steady state analysis is readily accessible via the web service COPASIWS. We illustrate the utility of SBMLmod by integrating gene expression data from different healthy tissues as well as from a cancer dataset into a previously published model of mammalian tryptophan metabolism. SBMLmod is a user-friendly platform for model modification and simulation. The web application is available at http://sbmlmod.uit.no , whereas the WSDL definition file for the web service is accessible via http://sbmlmod.uit.no/SBMLmod.wsdl . Furthermore, the entire package can be downloaded from https://github.com/MolecularBioinformatics/sbml-mod-ws . We envision that SBMLmod will make automated model modification and simulation available to a broader research community.

A MODELING AND SIMULATION LANGUAGE FOR BIOLOGICAL CELLS WITH COUPLED MECHANICAL AND CHEMICAL PROCESSES.

PubMed

Somogyi, Endre; Glazier, James A

2017-04-01

Biological cells are the prototypical example of active matter. Cells sense and respond to mechanical, chemical and electrical environmental stimuli with a range of behaviors, including dynamic changes in morphology and mechanical properties, chemical uptake and secretion, cell differentiation, proliferation, death, and migration. Modeling and simulation of such dynamic phenomena poses a number of computational challenges. A modeling language describing cellular dynamics must naturally represent complex intra and extra-cellular spatial structures and coupled mechanical, chemical and electrical processes. Domain experts will find a modeling language most useful when it is based on concepts, terms and principles native to the problem domain. A compiler must then be able to generate an executable model from this physically motivated description. Finally, an executable model must efficiently calculate the time evolution of such dynamic and inhomogeneous phenomena. We present a spatial hybrid systems modeling language, compiler and mesh-free Lagrangian based simulation engine which will enable domain experts to define models using natural, biologically motivated constructs and to simulate time evolution of coupled cellular, mechanical and chemical processes acting on a time varying number of cells and their environment.

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

Inter-institutional Development of a Poster-Based Cancer Biology Learning Tool

PubMed Central

Andraos-Selim, Cecile; Modzelewski, Ruth A.; Steinman, Richard A.

2010-01-01

There is a paucity of African-American Cancer researchers. To help address this, an educational collaboration was developed between a Comprehensive Cancer Center and a distant undergraduate biology department at a minority institution that sought to teach students introductory cancer biology while modeling research culture. A student-centered active learning curriculum was established that incorporated scientific poster presentations and simulated research exercises to foster learning of cancer biology. Students successfully mined primary literature for supportive data to test cancer-related hypotheses. Student feedback indicated that the poster project substantially enhanced depth of understanding of cancer biology and laid the groundwork for subsequent laboratory work. This inter-institutional collaboration modeled the research process while conveying facts and concepts about cancer. PMID:20237886

Agent-Based Models in Empirical Social Research

ERIC Educational Resources Information Center

Bruch, Elizabeth; Atwell, Jon

2015-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first…

40 CFR Appendix E to Part 63 - Monitoring Procedure for Nonthoroughly Mixed Open Biological Treatment Systems at Kraft Pulp...

Code of Federal Regulations, 2012 CFR

2012-07-01

.... II. Definitions Biological treatment unit = wastewater treatment unit designed and operated to... last zone in the series and ending with the first zone. B. Data Collection Requirements This method is based upon modeling the nonthoroughly mixed open biological treatment unit as a series of well-mixed...

Determining the transport mechanism of an enzyme-catalytic complex metabolic network based on biological robustness.

PubMed

Wang, Lei

2013-04-01

Understanding the transport mechanism of 1,3-propanediol (1,3-PD) is of critical importance to do further research on gene regulation. Due to the lack of intracellular information, on the basis of enzyme-catalytic system, using biological robustness as performance index, we present a system identification model to infer the most possible transport mechanism of 1,3-PD, in which the performance index consists of the relative error of the extracellular substance concentrations and biological robustness of the intracellular substance concentrations. We will not use a Boolean framework but prefer a model description based on ordinary differential equations. Among other advantages, this also facilitates the robustness analysis, which is the main goal of this paper. An algorithm is constructed to seek the solution of the identification model. Numerical results show that the most possible transport way is active transport coupled with passive diffusion.

Bioengineered silk scaffolds in 3D tissue modeling with focus on mammary tissues.

PubMed

Maghdouri-White, Yas; Bowlin, Gary L; Lemmon, Christopher A; Dréau, Didier

2016-02-01

In vitro generation of three-dimensional (3D) biological tissues and organ-like structures is a promising strategy to study and closely model complex aspects of the molecular, cellular, and physiological interactions of tissue. In particular, in vitro 3D tissue modeling holds promises to further our understanding of breast development. Indeed, biologically relevant 3D structures that combine mammary cells and engineered matrices have improved our knowledge of mammary tissue growth, organization, and differentiation. Several polymeric biomaterials have been used as scaffolds to engineer 3D mammary tissues. Among those, silk fibroin-based biomaterials have many biologically relevant properties and have been successfully used in multiple medical applications. Here, we review the recent advances in engineered scaffolds with an emphasis on breast-like tissue generation and the benefits of modified silk-based scaffolds. Copyright © 2015 Elsevier B.V. All rights reserved.

Re-thinking organisms: The impact of databases on model organism biology.

PubMed

Leonelli, Sabina; Ankeny, Rachel A

2012-03-01

Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. We conclude that the increasing reliance on community databases as vehicles to circulate data is having a major impact on how researchers conduct and communicate their research, which affects how they understand the biology of model organisms and its relation to the biology of other species. Copyright © 2011 Elsevier Ltd. All rights reserved.

Development of the biology card sorting task to measure conceptual expertise in biology.

PubMed

Smith, Julia I; Combs, Elijah D; Nagami, Paul H; Alto, Valerie M; Goh, Henry G; Gourdet, Muryam A A; Hough, Christina M; Nickell, Ashley E; Peer, Adrian G; Coley, John D; Tanner, Kimberly D

2013-01-01

There are widespread aspirations to focus undergraduate biology education on teaching students to think conceptually like biologists; however, there is a dearth of assessment tools designed to measure progress from novice to expert biological conceptual thinking. We present the development of a novel assessment tool, the Biology Card Sorting Task, designed to probe how individuals organize their conceptual knowledge of biology. While modeled on tasks from cognitive psychology, this task is unique in its design to test two hypothesized conceptual frameworks for the organization of biological knowledge: 1) a surface feature organization focused on organism type and 2) a deep feature organization focused on fundamental biological concepts. In this initial investigation of the Biology Card Sorting Task, each of six analytical measures showed statistically significant differences when used to compare the card sorting results of putative biological experts (biology faculty) and novices (non-biology major undergraduates). Consistently, biology faculty appeared to sort based on hypothesized deep features, while non-biology majors appeared to sort based on either surface features or nonhypothesized organizational frameworks. Results suggest that this novel task is robust in distinguishing populations of biology experts and biology novices and may be an adaptable tool for tracking emerging biology conceptual expertise.

A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference

PubMed Central

Zenil, Hector; Kiani, Narsis A.; Ball, Gordon; Gomez-Cabrero, David

2016-01-01

Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’. PMID:27698038

Inferring Biological Structures from Super-Resolution Single Molecule Images Using Generative Models

PubMed Central

Maji, Suvrajit; Bruchez, Marcel P.

2012-01-01

Localization-based super resolution imaging is presently limited by sampling requirements for dynamic measurements of biological structures. Generating an image requires serial acquisition of individual molecular positions at sufficient density to define a biological structure, increasing the acquisition time. Efficient analysis of biological structures from sparse localization data could substantially improve the dynamic imaging capabilities of these methods. Using a feature extraction technique called the Hough Transform simple biological structures are identified from both simulated and real localization data. We demonstrate that these generative models can efficiently infer biological structures in the data from far fewer localizations than are required for complete spatial sampling. Analysis at partial data densities revealed efficient recovery of clathrin vesicle size distributions and microtubule orientation angles with as little as 10% of the localization data. This approach significantly increases the temporal resolution for dynamic imaging and provides quantitatively useful biological information. PMID:22629348

Modelling language evolution: Examples and predictions

NASA Astrophysics Data System (ADS)

Gong, Tao; Shuai, Lan; Zhang, Menghan

2014-06-01

We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

No correlation discerned between the periods of rise and dominance of simulated species in a model of biological evolution

NASA Astrophysics Data System (ADS)

Kuhnle, Alan

2009-11-01

In [1], Liow et al. discern a general feature of the occurrence trajectories of biological species: the periods of rise and fall of a typical species are about as long as the period of dominance. In this work, an individual-based model of biological evolution that was developed by Rikvold and Zia in [2] is investigated, but no analogous feature is observed in the simulated species populations. Instead, the periods of rise and fall of a simulated species cannot always be sensibly defined; when it does make sense to define these quantities, they are quite short and independent of the period of dominance. [4pt] [1] Liow, L. H., Skaug, H. J., Ergon, T., Schweder, T.: Global occurence trajectories of microfossils: Is the rise and persistence of species influenced by environmental volatility? Manuscript for Paleobiology, 5 Dec 2008 [0pt] [2] Rikvold, P.A., Zia, R.K.P.: Punctuated equilibria and 1/f noise in a biological coevolution model with individual-based dynamics. Physical Review E 68, 031913 (2003)

Organizing Community-Based Data Standards: Lessons from Developing a Successful Open Standard in Systems Biology

NASA Astrophysics Data System (ADS)

Hucka, M.

2015-09-01

In common with many fields, including astronomy, a vast number of software tools for computational modeling and simulation are available today in systems biology. This wealth of resources is a boon to researchers, but it also presents interoperability problems. Despite working with different software tools, researchers want to disseminate their work widely as well as reuse and extend the models of other researchers. This situation led in the year 2000 to an effort to create a tool-independent, machine-readable file format for representing models: SBML, the Systems Biology Markup Language. SBML has since become the de facto standard for its purpose. Its success and general approach has inspired and influenced other community-oriented standardization efforts in systems biology. Open standards are essential for the progress of science in all fields, but it is often difficult for academic researchers to organize successful community-based standards. I draw on personal experiences from the development of SBML and summarize some of the lessons learned, in the hope that this may be useful to other groups seeking to develop open standards in a community-oriented fashion.

Perspective: Reaches of chemical physics in biology.

PubMed

Gruebele, Martin; Thirumalai, D

2013-09-28

Chemical physics as a discipline contributes many experimental tools, algorithms, and fundamental theoretical models that can be applied to biological problems. This is especially true now as the molecular level and the systems level descriptions begin to connect, and multi-scale approaches are being developed to solve cutting edge problems in biology. In some cases, the concepts and tools got their start in non-biological fields, and migrated over, such as the idea of glassy landscapes, fluorescence spectroscopy, or master equation approaches. In other cases, the tools were specifically developed with biological physics applications in mind, such as modeling of single molecule trajectories or super-resolution laser techniques. In this introduction to the special topic section on chemical physics of biological systems, we consider a wide range of contributions, all the way from the molecular level, to molecular assemblies, chemical physics of the cell, and finally systems-level approaches, based on the contributions to this special issue. Chemical physicists can look forward to an exciting future where computational tools, analytical models, and new instrumentation will push the boundaries of biological inquiry.

Perspective: Reaches of chemical physics in biology

PubMed Central

Gruebele, Martin; Thirumalai, D.

2013-01-01

Chemical physics as a discipline contributes many experimental tools, algorithms, and fundamental theoretical models that can be applied to biological problems. This is especially true now as the molecular level and the systems level descriptions begin to connect, and multi-scale approaches are being developed to solve cutting edge problems in biology. In some cases, the concepts and tools got their start in non-biological fields, and migrated over, such as the idea of glassy landscapes, fluorescence spectroscopy, or master equation approaches. In other cases, the tools were specifically developed with biological physics applications in mind, such as modeling of single molecule trajectories or super-resolution laser techniques. In this introduction to the special topic section on chemical physics of biological systems, we consider a wide range of contributions, all the way from the molecular level, to molecular assemblies, chemical physics of the cell, and finally systems-level approaches, based on the contributions to this special issue. Chemical physicists can look forward to an exciting future where computational tools, analytical models, and new instrumentation will push the boundaries of biological inquiry. PMID:24089712

The Center for Computational Biology: resources, achievements, and challenges

PubMed Central

Dinov, Ivo D; Thompson, Paul M; Woods, Roger P; Van Horn, John D; Shattuck, David W; Parker, D Stott

2011-01-01

The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains. PMID:22081221

The Center for Computational Biology: resources, achievements, and challenges.

PubMed

Toga, Arthur W; Dinov, Ivo D; Thompson, Paul M; Woods, Roger P; Van Horn, John D; Shattuck, David W; Parker, D Stott

2012-01-01

The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains.

Linking Adverse Outcome Pathways to Dynamic Energy Budgets: A Conceptual Model

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

Murphy, Cheryl; Nisbet, Roger; Antczak, Philipp

Ecological risk assessment quantifies the likelihood of undesirable impacts of stressors, primarily at high levels of biological organization. Data used to inform ecological risk assessments come primarily from tests on individual organisms or from suborganismal studies, indicating a disconnect between primary data and protection goals. We know how to relate individual responses to population dynamics using individual-based models, and there are emerging ideas on how to make connections to ecosystem services. However, there is no established methodology to connect effects seen at higher levels of biological organization with suborganismal dynamics, despite progress made in identifying Adverse Outcome Pathways (AOPs) thatmore » link molecular initiating events to ecologically relevant key events. This chapter is a product of a working group at the National Center for Mathematical and Biological Synthesis (NIMBioS) that assessed the feasibility of using dynamic energy budget (DEB) models of individual organisms as a “pivot” connecting suborganismal processes to higher level ecological processes. AOP models quantify explicit molecular, cellular or organ-level processes, but do not offer a route to linking sub-organismal damage to adverse effects on individual growth, reproduction, and survival, which can be propagated to the population level through individual-based models. DEB models describe these processes, but use abstract variables with undetermined connections to suborganismal biology. We propose linking DEB and quantitative AOP models by interpreting AOP key events as measures of damage-inducing processes in a DEB model. Here, we present a conceptual model for linking AOPs to DEB models and review existing modeling tools available for both AOP and DEB.« less

Computational modeling of in vitro biological responses on polymethacrylate surfaces

PubMed Central

Ghosh, Jayeeta; Lewitus, Dan Y; Chandra, Prafulla; Joy, Abraham; Bushman, Jared; Knight, Doyle; Kohn, Joachim

2011-01-01

The objective of this research was to examine the capabilities of QSPR (Quantitative Structure Property Relationship) modeling to predict specific biological responses (fibrinogen adsorption, cell attachment and cell proliferation index) on thin films of different polymethacrylates. Using 33 commercially available monomers it is theoretically possible to construct a library of over 40,000 distinct polymer compositions. A subset of these polymers were synthesized and solvent cast surfaces were prepared in 96 well plates for the measurement of fibrinogen adsorption. NIH 3T3 cell attachment and proliferation index were measured on spin coated thin films of these polymers. Based on the experimental results of these polymers, separate models were built for homo-, co-, and terpolymers in the library with good correlation between experiment and predicted values. The ability to predict biological responses by simple QSPR models for large numbers of polymers has important implications in designing biomaterials for specific biological or medical applications. PMID:21779132

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.

PubMed

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2016-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

Bacteriophage-based synthetic biology for the study of infectious diseases

PubMed Central

Lu, Timothy K.

2014-01-01

Since their discovery, bacteriophages have contributed enormously to our understanding of molecular biology as model systems. Furthermore, bacteriophages have provided many tools that have advanced the fields of genetic engineering and synthetic biology. Here, we discuss bacteriophage-based technologies and their application to the study of infectious diseases. New strategies for engineering genomes have the potential to accelerate the design of novel phages as therapies, diagnostics, and tools. Though almost a century has elapsed since their discovery, bacteriophages continue to have a major impact on modern biological sciences, especially with the growth of multidrug-resistant bacteria and interest in the microbiome. PMID:24997401

Radiation Quality Effects on Transcriptome Profiles in 3-D Cultures After Charged Particle Irradiation

NASA Technical Reports Server (NTRS)

Patel, Zarana S.; Kidane, Yared H.; Huff, Janice L.

2014-01-01

In this work, we evaluated the differential effects of low- and high-LET radiation on 3-D organotypic cultures in order to investigate radiation quality impacts on gene expression and cellular responses. Current risk models for assessment of space radiation-induced cancer have large uncertainties because the models for adverse health effects following radiation exposure are founded on epidemiological analyses of human populations exposed to low-LET radiation. Reducing these uncertainties requires new knowledge on the fundamental differences in biological responses (the so-called radiation quality effects) triggered by heavy ion particle radiation versus low-LET radiation associated with Earth-based exposures. In order to better quantify these radiation quality effects in biological systems, we are utilizing novel 3-D organotypic human tissue models for space radiation research. These models hold promise for risk assessment as they provide a format for study of human cells within a realistic tissue framework, thereby bridging the gap between 2-D monolayer culture and animal models for risk extrapolation to humans. To identify biological pathway signatures unique to heavy ion particle exposure, functional gene set enrichment analysis (GSEA) was used with whole transcriptome profiling. GSEA has been used extensively as a method to garner biological information in a variety of model systems but has not been commonly used to analyze radiation effects. It is a powerful approach for assessing the functional significance of radiation quality-dependent changes from datasets where the changes are subtle but broad, and where single gene based analysis using rankings of fold-change may not reveal important biological information.

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy.

PubMed

Haworth, Annette; Mears, Christopher; Betts, John M; Reynolds, Hayley M; Tack, Guido; Leo, Kevin; Williams, Scott; Ebert, Martin A

2016-01-07

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The 'biological optimisation' considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy

NASA Astrophysics Data System (ADS)

Haworth, Annette; Mears, Christopher; Betts, John M.; Reynolds, Hayley M.; Tack, Guido; Leo, Kevin; Williams, Scott; Ebert, Martin A.

2016-01-01

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The ‘biological optimisation’ considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.

Hands-on-Entropy, Energy Balance with Biological Relevance

NASA Astrophysics Data System (ADS)

Reeves, Mark

2015-03-01

Entropy changes underlie the physics that dominates biological interactions. Indeed, introductory biology courses often begin with an exploration of the qualities of water that are important to living systems. However, one idea that is not explicitly addressed in most introductory physics or biology textbooks is important contribution of the entropy in driving fundamental biological processes towards equilibrium. From diffusion to cell-membrane formation, to electrostatic binding in protein folding, to the functioning of nerve cells, entropic effects often act to counterbalance deterministic forces such as electrostatic attraction and in so doing, allow for effective molecular signaling. A small group of biology, biophysics and computer science faculty have worked together for the past five years to develop curricular modules (based on SCALEUP pedagogy). This has enabled students to create models of stochastic and deterministic processes. Our students are first-year engineering and science students in the calculus-based physics course and they are not expected to know biology beyond the high-school level. In our class, they learn to reduce complex biological processes and structures in order model them mathematically to account for both deterministic and probabilistic processes. The students test these models in simulations and in laboratory experiments that are biologically relevant such as diffusion, ionic transport, and ligand-receptor binding. Moreover, the students confront random forces and traditional forces in problems, simulations, and in laboratory exploration throughout the year-long course as they move from traditional kinematics through thermodynamics to electrostatic interactions. This talk will present a number of these exercises, with particular focus on the hands-on experiments done by the students, and will give examples of the tangible material that our students work with throughout the two-semester sequence of their course on introductory physics with a bio focus. Supported by NSF DUE.

An instructional design process based on expert knowledge for teaching students how mechanisms are explained.

PubMed

Trujillo, Caleb M; Anderson, Trevor R; Pelaez, Nancy J

2016-06-01

In biology and physiology courses, students face many difficulties when learning to explain mechanisms, a topic that is demanding due to the immense complexity and abstract nature of molecular and cellular mechanisms. To overcome these difficulties, we asked the following question: how does an instructor transform their understanding of biological mechanisms and other difficult-to-learn topics so that students can comprehend them? To address this question, we first reviewed a model of the components used by biologists to explain molecular and cellular mechanisms: the MACH model, with the components of methods (M), analogies (A), context (C), and how (H). Next, instructional materials were developed and the teaching activities were piloted with a physical MACH model. Students who used the MACH model to guide their explanations of mechanisms exhibited both improvements and some new difficulties. Third, a series of design-based research cycles was applied to bring the activities with an improved physical MACH model into biology and biochemistry courses. Finally, a useful rubric was developed to address prevalent student difficulties. Here, we present, for physiology and biology instructors, the knowledge and resources for explaining molecular and cellular mechanisms in undergraduate courses with an instructional design process aimed at realizing pedagogical content knowledge for teaching. Our four-stage process could be adapted to advance instruction with a range of models in the life sciences. Copyright © 2016 The American Physiological Society.

An instructional design process based on expert knowledge for teaching students how mechanisms are explained

PubMed Central

Anderson, Trevor R.; Pelaez, Nancy J.

2016-01-01

In biology and physiology courses, students face many difficulties when learning to explain mechanisms, a topic that is demanding due to the immense complexity and abstract nature of molecular and cellular mechanisms. To overcome these difficulties, we asked the following question: how does an instructor transform their understanding of biological mechanisms and other difficult-to-learn topics so that students can comprehend them? To address this question, we first reviewed a model of the components used by biologists to explain molecular and cellular mechanisms: the MACH model, with the components of methods (M), analogies (A), context (C), and how (H). Next, instructional materials were developed and the teaching activities were piloted with a physical MACH model. Students who used the MACH model to guide their explanations of mechanisms exhibited both improvements and some new difficulties. Third, a series of design-based research cycles was applied to bring the activities with an improved physical MACH model into biology and biochemistry courses. Finally, a useful rubric was developed to address prevalent student difficulties. Here, we present, for physiology and biology instructors, the knowledge and resources for explaining molecular and cellular mechanisms in undergraduate courses with an instructional design process aimed at realizing pedagogical content knowledge for teaching. Our four-stage process could be adapted to advance instruction with a range of models in the life sciences. PMID:27231262

A mechanistic Individual-based Model of microbial communities.

PubMed

Jayathilake, Pahala Gedara; Gupta, Prashant; Li, Bowen; Madsen, Curtis; Oyebamiji, Oluwole; González-Cabaleiro, Rebeca; Rushton, Steve; Bridgens, Ben; Swailes, David; Allen, Ben; McGough, A Stephen; Zuliani, Paolo; Ofiteru, Irina Dana; Wilkinson, Darren; Chen, Jinju; Curtis, Tom

2017-01-01

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for "bottom up" prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup.

A mechanistic Individual-based Model of microbial communities

PubMed Central

Gupta, Prashant; Li, Bowen; Madsen, Curtis; Oyebamiji, Oluwole; González-Cabaleiro, Rebeca; Rushton, Steve; Bridgens, Ben; Swailes, David; Allen, Ben; McGough, A. Stephen; Zuliani, Paolo; Ofiteru, Irina Dana; Wilkinson, Darren; Chen, Jinju; Curtis, Tom

2017-01-01

Accurate predictive modelling of the growth of microbial communities requires the credible representation of the interactions of biological, chemical and mechanical processes. However, although biological and chemical processes are represented in a number of Individual-based Models (IbMs) the interaction of growth and mechanics is limited. Conversely, there are mechanically sophisticated IbMs with only elementary biology and chemistry. This study focuses on addressing these limitations by developing a flexible IbM that can robustly combine the biological, chemical and physical processes that dictate the emergent properties of a wide range of bacterial communities. This IbM is developed by creating a microbiological adaptation of the open source Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). This innovation should provide the basis for “bottom up” prediction of the emergent behaviour of entire microbial systems. In the model presented here, bacterial growth, division, decay, mechanical contact among bacterial cells, and adhesion between the bacteria and extracellular polymeric substances are incorporated. In addition, fluid-bacteria interaction is implemented to simulate biofilm deformation and erosion. The model predicts that the surface morphology of biofilms becomes smoother with increased nutrient concentration, which agrees well with previous literature. In addition, the results show that increased shear rate results in smoother and more compact biofilms. The model can also predict shear rate dependent biofilm deformation, erosion, streamer formation and breakup. PMID:28771505

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Unity and disunity in evolutionary sciences: process-based analogies open common research avenues for biology and linguistics.

PubMed

List, Johann-Mattis; Pathmanathan, Jananan Sylvestre; Lopez, Philippe; Bapteste, Eric

2016-08-20

For a long time biologists and linguists have been noticing surprising similarities between the evolution of life forms and languages. Most of the proposed analogies have been rejected. Some, however, have persisted, and some even turned out to be fruitful, inspiring the transfer of methods and models between biology and linguistics up to today. Most proposed analogies were based on a comparison of the research objects rather than the processes that shaped their evolution. Focusing on process-based analogies, however, has the advantage of minimizing the risk of overstating similarities, while at the same time reflecting the common strategy to use processes to explain the evolution of complexity in both fields. We compared important evolutionary processes in biology and linguistics and identified processes specific to only one of the two disciplines as well as processes which seem to be analogous, potentially reflecting core evolutionary processes. These new process-based analogies support novel methodological transfer, expanding the application range of biological methods to the field of historical linguistics. We illustrate this by showing (i) how methods dealing with incomplete lineage sorting offer an introgression-free framework to analyze highly mosaic word distributions across languages; (ii) how sequence similarity networks can be used to identify composite and borrowed words across different languages; (iii) how research on partial homology can inspire new methods and models in both fields; and (iv) how constructive neutral evolution provides an original framework for analyzing convergent evolution in languages resulting from common descent (Sapir's drift). Apart from new analogies between evolutionary processes, we also identified processes which are specific to either biology or linguistics. This shows that general evolution cannot be studied from within one discipline alone. In order to get a full picture of evolution, biologists and linguists need to complement their studies, trying to identify cross-disciplinary and discipline-specific evolutionary processes. The fact that we found many process-based analogies favoring transfer from biology to linguistics further shows that certain biological methods and models have a broader scope than previously recognized. This opens fruitful paths for collaboration between the two disciplines. This article was reviewed by W. Ford Doolittle and Eugene V. Koonin.

Analytical performance specifications for changes in assay bias (Δbias) for data with logarithmic distributions as assessed by effects on reference change values.

PubMed

Petersen, Per H; Lund, Flemming; Fraser, Callum G; Sölétormos, György

2016-11-01

Background The distributions of within-subject biological variation are usually described as coefficients of variation, as are analytical performance specifications for bias, imprecision and other characteristics. Estimation of specifications required for reference change values is traditionally done using relationship between the batch-related changes during routine performance, described as Δbias, and the coefficients of variation for analytical imprecision (CV A ): the original theory is based on standard deviations or coefficients of variation calculated as if distributions were Gaussian. Methods The distribution of between-subject biological variation can generally be described as log-Gaussian. Moreover, recent analyses of within-subject biological variation suggest that many measurands have log-Gaussian distributions. In consequence, we generated a model for the estimation of analytical performance specifications for reference change value, with combination of Δbias and CV A based on log-Gaussian distributions of CV I as natural logarithms. The model was tested using plasma prolactin and glucose as examples. Results Analytical performance specifications for reference change value generated using the new model based on log-Gaussian distributions were practically identical with the traditional model based on Gaussian distributions. Conclusion The traditional and simple to apply model used to generate analytical performance specifications for reference change value, based on the use of coefficients of variation and assuming Gaussian distributions for both CV I and CV A , is generally useful.

Computational Systems Biology in Cancer: Modeling Methods and Applications

PubMed Central

Materi, Wayne; Wishart, David S.

2007-01-01

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy. PMID:19936081

BioASF: a framework for automatically generating executable pathway models specified in BioPAX.

PubMed

Haydarlou, Reza; Jacobsen, Annika; Bonzanni, Nicola; Feenstra, K Anton; Abeln, Sanne; Heringa, Jaap

2016-06-15

Biological pathways play a key role in most cellular functions. To better understand these functions, diverse computational and cell biology researchers use biological pathway data for various analysis and modeling purposes. For specifying these biological pathways, a community of researchers has defined BioPAX and provided various tools for creating, validating and visualizing BioPAX models. However, a generic software framework for simulating BioPAX models is missing. Here, we attempt to fill this gap by introducing a generic simulation framework for BioPAX. The framework explicitly separates the execution model from the model structure as provided by BioPAX, with the advantage that the modelling process becomes more reproducible and intrinsically more modular; this ensures natural biological constraints are satisfied upon execution. The framework is based on the principles of discrete event systems and multi-agent systems, and is capable of automatically generating a hierarchical multi-agent system for a given BioPAX model. To demonstrate the applicability of the framework, we simulated two types of biological network models: a gene regulatory network modeling the haematopoietic stem cell regulators and a signal transduction network modeling the Wnt/β-catenin signaling pathway. We observed that the results of the simulations performed using our framework were entirely consistent with the simulation results reported by the researchers who developed the original models in a proprietary language. The framework, implemented in Java, is open source and its source code, documentation and tutorial are available at http://www.ibi.vu.nl/programs/BioASF CONTACT: j.heringa@vu.nl. © The Author 2016. Published by Oxford University Press.

SEEK: a systems biology data and model management platform.

PubMed

Wolstencroft, Katherine; Owen, Stuart; Krebs, Olga; Nguyen, Quyen; Stanford, Natalie J; Golebiewski, Martin; Weidemann, Andreas; Bittkowski, Meik; An, Lihua; Shockley, David; Snoep, Jacky L; Mueller, Wolfgang; Goble, Carole

2015-07-11

Systems biology research typically involves the integration and analysis of heterogeneous data types in order to model and predict biological processes. Researchers therefore require tools and resources to facilitate the sharing and integration of data, and for linking of data to systems biology models. There are a large number of public repositories for storing biological data of a particular type, for example transcriptomics or proteomics, and there are several model repositories. However, this silo-type storage of data and models is not conducive to systems biology investigations. Interdependencies between multiple omics datasets and between datasets and models are essential. Researchers require an environment that will allow the management and sharing of heterogeneous data and models in the context of the experiments which created them. The SEEK is a suite of tools to support the management, sharing and exploration of data and models in systems biology. The SEEK platform provides an access-controlled, web-based environment for scientists to share and exchange data and models for day-to-day collaboration and for public dissemination. A plug-in architecture allows the linking of experiments, their protocols, data, models and results in a configurable system that is available 'off the shelf'. Tools to run model simulations, plot experimental data and assist with data annotation and standardisation combine to produce a collection of resources that support analysis as well as sharing. Underlying semantic web resources additionally extract and serve SEEK metadata in RDF (Resource Description Format). SEEK RDF enables rich semantic queries, both within SEEK and between related resources in the web of Linked Open Data. The SEEK platform has been adopted by many systems biology consortia across Europe. It is a data management environment that has a low barrier of uptake and provides rich resources for collaboration. This paper provides an update on the functions and features of the SEEK software, and describes the use of the SEEK in the SysMO consortium (Systems biology for Micro-organisms), and the VLN (virtual Liver Network), two large systems biology initiatives with different research aims and different scientific communities.

CBB Portal @ PNNL

Science.gov Websites

Search PNNL Home About Research Publications Jobs News Contacts Computational Biology and Bioinformatics , and engineering to transform the data into knowledge. This new quantitative, predictive biology is to empirical modeling and physics-based simulations. CBB research seeks to: Understand. Understanding

Biocellion: accelerating computer simulation of multicellular biological system models.

PubMed

Kang, Seunghwa; Kahan, Simon; McDermott, Jason; Flann, Nicholas; Shmulevich, Ilya

2014-11-01

Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra

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

Kamp, Florian; Department of Radiation Oncology, Technische Universität München, Klinikum Rechts der Isar, München; Physik-Department, Technische Universität München, Garching

2015-11-01

Purpose: The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Methods and Materials: Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damagemore » simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. Results: We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β){sub X} = 2 Gy. Conclusions: These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization.« less

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra.

PubMed

Kamp, Florian; Cabal, Gonzalo; Mairani, Andrea; Parodi, Katia; Wilkens, Jan J; Carlson, David J

2015-11-01

The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damage simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β)X = 2 Gy. These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization. Copyright © 2015 Elsevier Inc. All rights reserved.

Spatial landscape model to characterize biological diversity using R statistical computing environment.

PubMed

Singh, Hariom; Garg, R D; Karnatak, Harish C; Roy, Arijit

2018-01-15

Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output. Copyright © 2017 Elsevier Ltd. All rights reserved.

Agent-based modeling: case study in cleavage furrow models

PubMed Central

Mogilner, Alex; Manhart, Angelika

2016-01-01

The number of studies in cell biology in which quantitative models accompany experiments has been growing steadily. Roughly, mathematical and computational techniques of these models can be classified as “differential equation based” (DE) or “agent based” (AB). Recently AB models have started to outnumber DE models, but understanding of AB philosophy and methodology is much less widespread than familiarity with DE techniques. Here we use the history of modeling a fundamental biological problem—positioning of the cleavage furrow in dividing cells—to explain how and why DE and AB models are used. We discuss differences, advantages, and shortcomings of these two approaches. PMID:27811328

The transmission of finite amplitude sound beam in multi-layered biological media

NASA Astrophysics Data System (ADS)

Liu, Xiaozhou; Li, Junlun; Yin, Chang; Gong, Xiufen; Zhang, Dong; Xue, Honghui

2007-02-01

Based on the Khokhlov Zabolotskaya Kuznetsov (KZK) equation, a model in the frequency domain is given to describe the transmission of finite amplitude sound beam in multi-layered biological media. Favorable agreement between the theoretical analyses and the measured results shows this approach could effectively describe the transmission of finite amplitude sound wave in multi-layered biological media.

First-Year Biology Students' Understandings of Meiosis: An Investigation Using a Structural Theoretical Framework

ERIC Educational Resources Information Center

Quinn, Frances; Pegg, John; Panizzon, Debra

2009-01-01

Meiosis is a biological concept that is both complex and important for students to learn. This study aims to explore first-year biology students' explanations of the process of meiosis, using an explicit theoretical framework provided by the Structure of the Observed Learning Outcome (SOLO) model. The research was based on responses of 334…

Challenges in structural approaches to cell modeling

PubMed Central

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A.

2016-01-01

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. PMID:27255863

Physiologically based pharmacokinetic (PBPK) modeling considering methylated trivalent arsenicals

EPA Science Inventory

PBPK modeling provides a quantitative biologically-based framework to integrate diverse types of information for application to risk analysis. For example, genetic polymorphisms in arsenic metabolizing enzymes (AS3MT) can lead to differences in target tissue dosimetry for key tri...

Thermal injury models for optical treatment of biological tissues: a comparative study.

PubMed

Fanjul-Velez, Felix; Ortega-Quijano, Noe; Salas-Garcia, Irene; Arce-Diego, Jose L

2010-01-01

The interaction of optical radiation with biological tissues causes an increase in the temperature that, depending on its magnitude, can provoke a thermal injury process in the tissue. The establishment of laser irradiation pathological limits constitutes an essential task, as long as it enables to fix and delimit a range of parameters that ensure a safe treatment in laser therapies. These limits can be appropriately described by kinetic models of the damage processes. In this work, we present and compare several models for the study of thermal injury in biological tissues under optical illumination, particularly the Arrhenius thermal damage model and the thermal dosimetry model based on CEM (Cumulative Equivalent Minutes) 43°C. The basic concepts that link the temperature and exposition time with the tissue injury or cellular death are presented, and it will be shown that they enable to establish predictive models for the thermal damage in laser therapies. The results obtained by both models will be compared and discussed, highlighting the main advantages of each one and proposing the most adequate one for optical treatment of biological tissues.

A generic framework for individual-based modelling and physical-biological interaction

PubMed Central

2018-01-01

The increased availability of high-resolution ocean data globally has enabled more detailed analyses of physical-biological interactions and their consequences to the ecosystem. We present IBMlib, which is a versatile, portable and computationally effective framework for conducting Lagrangian simulations in the marine environment. The purpose of the framework is to handle complex individual-level biological models of organisms, combined with realistic 3D oceanographic model of physics and biogeochemistry describing the environment of the organisms without assumptions about spatial or temporal scales. The open-source framework features a minimal robust interface to facilitate the coupling between individual-level biological models and oceanographic models, and we provide application examples including forward/backward simulations, habitat connectivity calculations, assessing ocean conditions, comparison of physical circulation models, model ensemble runs and recently posterior Eulerian simulations using the IBMlib framework. We present the code design ideas behind the longevity of the code, our implementation experiences, as well as code performance benchmarking. The framework may contribute substantially to progresses in representing, understanding, predicting and eventually managing marine ecosystems. PMID:29351280

Application of Biologically Based Lumping To Investigate the Toxicokinetic Interactions of a Complex Gasoline Mixture.

PubMed

Jasper, Micah N; Martin, Sheppard A; Oshiro, Wendy M; Ford, Jermaine; Bushnell, Philip J; El-Masri, Hisham

2016-03-15

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate the performance of our PBPK model and chemical lumping method. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course toxicokinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 nontarget chemicals. The same biologically based lumping approach can be used to simplify any complex mixture with tens, hundreds, or thousands of constituents.

Virtual Transgenics: Using a Molecular Biology Simulation to Impact Student Academic Achievement and Attitudes

NASA Astrophysics Data System (ADS)

Shegog, Ross; Lazarus, Melanie M.; Murray, Nancy G.; Diamond, Pamela M.; Sessions, Nathalie; Zsigmond, Eva

2012-10-01

The transgenic mouse model is useful for studying the causes and potential cures for human genetic diseases. Exposing high school biology students to laboratory experience in developing transgenic animal models is logistically prohibitive. Computer-based simulation, however, offers this potential in addition to advantages of fidelity and reach. This study describes and evaluates a computer-based simulation to train advanced placement high school science students in laboratory protocols, a transgenic mouse model was produced. A simulation module on preparing a gene construct in the molecular biology lab was evaluated using a randomized clinical control design with advanced placement high school biology students in Mercedes, Texas ( n = 44). Pre-post tests assessed procedural and declarative knowledge, time on task, attitudes toward computers for learning and towards science careers. Students who used the simulation increased their procedural and declarative knowledge regarding molecular biology compared to those in the control condition (both p < 0.005). Significant increases continued to occur with additional use of the simulation ( p < 0.001). Students in the treatment group became more positive toward using computers for learning ( p < 0.001). The simulation did not significantly affect attitudes toward science in general. Computer simulation of complex transgenic protocols have potential to provide a "virtual" laboratory experience as an adjunct to conventional educational approaches.

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

Hartmann, Anja, E-mail: hartmann@ipk-gatersleben.de; Schreiber, Falk; Martin-Luther-University Halle-Wittenberg, Halle

The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the contextmore » of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework (SBM{sup 2} – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.« less

Multiscale Cancer Modeling

PubMed Central

Macklin, Paul; Cristini, Vittorio

2013-01-01

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insight on the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community. PMID:21529163

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Elemental Education.

ERIC Educational Resources Information Center

Daniel, Esther Gnanamalar Sarojini; Saat, Rohaida Mohd.

2001-01-01

Introduces a learning module integrating three disciplines--physics, chemistry, and biology--and based on four elements: carbon, oxygen, hydrogen, and silicon. Includes atomic model and silicon-based life activities. (YDS)

Modelling and simulation techniques for membrane biology.

PubMed

Burrage, Kevin; Hancock, John; Leier, André; Nicolau, Dan V

2007-07-01

One of the most important aspects of Computational Cell Biology is the understanding of the complicated dynamical processes that take place on plasma membranes. These processes are often so complicated that purely temporal models cannot always adequately capture the dynamics. On the other hand, spatial models can have large computational overheads. In this article, we review some of these issues with respect to chemistry, membrane microdomains and anomalous diffusion and discuss how to select appropriate modelling and simulation paradigms based on some or all the following aspects: discrete, continuous, stochastic, delayed and complex spatial processes.

Identification of Biokinetic Models Using the Concept of Extents.

PubMed

Mašić, Alma; Srinivasan, Sriniketh; Billeter, Julien; Bonvin, Dominique; Villez, Kris

2017-07-05

The development of a wide array of process technologies to enable the shift from conventional biological wastewater treatment processes to resource recovery systems is matched by an increasing demand for predictive capabilities. Mathematical models are excellent tools to meet this demand. However, obtaining reliable and fit-for-purpose models remains a cumbersome task due to the inherent complexity of biological wastewater treatment processes. In this work, we present a first study in the context of environmental biotechnology that adopts and explores the use of extents as a way to simplify and streamline the dynamic process modeling task. In addition, the extent-based modeling strategy is enhanced by optimal accounting for nonlinear algebraic equilibria and nonlinear measurement equations. Finally, a thorough discussion of our results explains the benefits of extent-based modeling and its potential to turn environmental process modeling into a highly automated task.

Biology, Philosophy, and Scientific Method.

ERIC Educational Resources Information Center

Hill, L.

1985-01-01

The limits of falsification are discussed and the historically based models of science described by Lakatos and Kuhn are shown to offer greater insights into the practice of science. The theory of natural selection is used to relate biology to philosophy and scientific method. (Author/JN)

EMBAYMENT CHARACTERISTIC TIME AND BIOLOGY VIA TIDAL PRISM MODEL

EPA Science Inventory

Transport time scales in water bodies are classically based on their physical and chemical aspects rather than on their ecological and biological character. The direct connection between a physical time scale and ecological effects has to be investigated in order to quantitativel...

Application of Petri net based analysis techniques to signal transduction pathways.

PubMed

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-11-02

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.

Application of Petri net based analysis techniques to signal transduction pathways

PubMed Central

Sackmann, Andrea; Heiner, Monika; Koch, Ina

2006-01-01

Background Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed. Methods We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study. Results We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units. Conclusion The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules. PMID:17081284

[Biology and culture: a dimension of collaboration between anthropology and epidemiology].

PubMed

Song, Leiming; Wang, Ning

2016-01-01

Biology is the important basis of epidemiological study. Based on biology, psychology, social and cultural factors can influence human's health and disease incidence. The medical mode has changed from "biomedical mode" to "bio-psycho-social medical model" , but culture factor was neglected somewhat during this process, so paying attention to culture factor in anthropologic study and using it as biologic basis in epidemiologic study might be a dimension of collaboration between of anthropology and epidemiology.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

PubMed Central

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Intelligence-based anti-doping from an equine biological passport.

PubMed

Cawley, Adam T; Keledjian, John

2017-09-01

The move towards personalized medicine derived from individually focused clinical chemistry measurements has been translated by the human anti-doping movement over the past decade into developing the athlete biological passport. There is considerable potential for animal sports to adapt this model to facilitate an intelligence-based anti-doping system. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Games network and application to PAs system.

PubMed

Chettaoui, C; Delaplace, F; Manceny, M; Malo, M

2007-02-01

In this article, we present a game theory based framework, named games network, for modeling biological interactions. After introducing the theory, we more precisely describe the methodology to model biological interactions. Then we apply it to the plasminogen activator system (PAs) which is a signal transduction pathway involved in cancer cell migration. The games network theory extends game theory by including the locality of interactions. Each game in a games network represents local interactions between biological agents. The PAs system is implicated in cytoskeleton modifications via regulation of actin and microtubules, which in turn favors cell migration. The games network model has enabled us a better understanding of the regulation involved in the PAs system.

Ionization of biomolecular targets by ion impact: input data for radiobiological applications

NASA Astrophysics Data System (ADS)

de Vera, Pablo; Abril, Isabel; Garcia-Molina, Rafael; Solov'yov, Andrey V.

2013-06-01

In this work we review and further develop a semiempirical model recently proposed for the ion impact ionization of complex biological media. The model is based on the dielectric formalism, and makes use of a semiempirical parametrization of the optical energy-loss function of bioorganic compounds, allowing the calculation of single and total ionization cross sections and related quantities for condensed biological targets, such as liquid water, DNA and its components, proteins, lipids, carbohydrates or cell constituents. The model shows a very good agreement with experimental data for water, adenine and uracil, and allows the comparison of the ionization efficiency of different biological targets, and also the average kinetic energy of the ejected secondary electrons.

Biological Bases for Radiation Adaptive Responses in the Lung

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

Scott, Bobby R.; Lin, Yong; Wilder, Julie

2015-03-01

Our main research objective was to determine the biological bases for low-dose, radiation-induced adaptive responses in the lung, and use the knowledge gained to produce an improved risk model for radiation-induced lung cancer that accounts for activated natural protection, genetic influences, and the role of epigenetic regulation (epiregulation). Currently, low-dose radiation risk assessment is based on the linear-no-threshold hypothesis, which now is known to be unsupported by a large volume of data.

Agent-Based Multicellular Modeling for Predictive Toxicology

EPA Science Inventory

Biological modeling is a rapidly growing field that has benefited significantly from recent technological advances, expanding traditional methods with greater computing power, parameter-determination algorithms, and the development of novel computational approaches to modeling bi...

CRISPR editing in biological and biomedical investigation.

PubMed

Huang, Jiaojiao; Wang, Yanfang; Zhao, Jianguo

2018-05-01

Recently, clustered regularly interspaced short palindromic repeats (CRISPR) based genomic editing technologies have armed researchers with powerful new tools to biological and biomedical investigations. To further improve and expand its functionality, natural, and engineered CRISPR associated nine proteins (Cas9s) have been investigated, various CRISPR delivery strategies have been tested and optimized, and multiple schemes have been developed to ensure precise mammalian genome editing. Benefiting from those in-depth understanding and further development of CRISPR, versatile CRISPR-based platforms for genome editing have been rapidly developed to advance investigations in biology and biomedicine. In biological research area, CRISPR has been widely adopted in both fundamental and applied research fields, such as accurate base editing, transcriptional regulation, and genome-wide screening. In biomedical research area, CRISPR has also shown its extensive applicability in the establishment of animal models for genetic disorders especially those large animals and non-human primates models, and gene therapy to combat virus infectious diseases, to correct monogenic disorders in vivo or in pluripotent cells. In this prospect article, after highlighting recent developments of CRISPR systems, we outline different applications and current limitations of CRISPR use in biological and biomedical investigation. Finally, we provide a perspective for future development and potential risks of this multifunctional technology. © 2017 Wiley Periodicals, Inc.

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.

Metabolic systems biology: a brief primer.

PubMed

Edwards, Lindsay M

2017-05-01

In the early to mid-20th century, reductionism as a concept in biology was challenged by key thinkers, including Ludwig von Bertalanffy. He proposed that living organisms were specific examples of complex systems and, as such, they should display characteristics including hierarchical organisation and emergent behaviour. Yet the true study of complete biological systems (for example, metabolism) was not possible until technological advances that occurred 60 years later. Technology now exists that permits the measurement of complete levels of the biological hierarchy, for example the genome and transcriptome. The complexity and scale of these data require computational models for their interpretation. The combination of these - systems thinking, high-dimensional data and computation - defines systems biology, typically accompanied by some notion of iterative model refinement. Only sequencing-based technologies, however, offer full coverage. Other 'omics' platforms trade coverage for sensitivity, although the densely connected nature of biological networks suggests that full coverage may not be necessary. Systems biology models are often characterised as either 'bottom-up' (mechanistic) or 'top-down' (statistical). This distinction can mislead, as all models rely on data and all are, to some degree, 'middle-out'. Systems biology has matured as a discipline, and its methods are commonplace in many laboratories. However, many challenges remain, especially those related to large-scale data integration. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

A mathematical function for the description of nutrient-response curve

PubMed Central

Ahmadi, Hamed

2017-01-01

Several mathematical equations have been proposed to modeling nutrient-response curve for animal and human justified on the goodness of fit and/or on the biological mechanism. In this paper, a functional form of a generalized quantitative model based on Rayleigh distribution principle for description of nutrient-response phenomena is derived. The three parameters governing the curve a) has biological interpretation, b) may be used to calculate reliable estimates of nutrient response relationships, and c) provide the basis for deriving relationships between nutrient and physiological responses. The new function was successfully applied to fit the nutritional data obtained from 6 experiments including a wide range of nutrients and responses. An evaluation and comparison were also done based simulated data sets to check the suitability of new model and four-parameter logistic model for describing nutrient responses. This study indicates the usefulness and wide applicability of the new introduced, simple and flexible model when applied as a quantitative approach to characterizing nutrient-response curve. This new mathematical way to describe nutritional-response data, with some useful biological interpretations, has potential to be used as an alternative approach in modeling nutritional responses curve to estimate nutrient efficiency and requirements. PMID:29161271

Modelling protein functional domains in signal transduction using Maude

NASA Technical Reports Server (NTRS)

Sriram, M. G.

2003-01-01

Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.

Modular analysis of biological networks.

PubMed

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

Application of Biotic Ligand and Toxic Unit modeling approaches to predict improvements in zooplankton species richness in smelter-damaged lakes near Sudbury, Ontario.

PubMed

Khan, Farhan R; Keller, W Bill; Yan, Norman D; Welsh, Paul G; Wood, Chris M; McGeer, James C

2012-02-07

Using a 30-year record of biological and water chemistry data collected from seven lakes near smelters in Sudbury (Ontario, Canada) we examined the link between reductions of Cu, Ni, and Zn concentrations and zooplankton species richness. The toxicity of the metal mixtures was assessed using an additive Toxic Unit (TU) approach. Four TU models were developed based on total metal concentrations (TM-TU); free ion concentrations (FI-TU); acute LC50s calculated from the Biotic Ligand Model (BLM-TU); and chronic LC50s (acute LC50s adjusted by metal-specific acute-to-chronic ratios, cBLM-TU). All models significantly correlated reductions in metal concentrations to increased zooplankton species richness over time (p < 0.01) with a rank based on r(2) values of cBLM-TU > BLM-TU = FI-TU > TM-TU. Lake-wise comparisons within each model showed that the BLM-TU and cBLM-TU models provided the best description of recovery across all seven lakes. These two models were used to calculate thresholds for chemical and biological recovery using data from reference lakes in the same region. A threshold value of TU = 1 derived from the cBLM-TU provided the most accurate description of recovery. Overall, BLM-based TU models that integrate site-specific water chemistry-derived estimates of toxicity offer a useful predictor of biological recovery.

Development of the Biology Card Sorting Task to Measure Conceptual Expertise in Biology

PubMed Central

Smith, Julia I.; Combs, Elijah D.; Nagami, Paul H.; Alto, Valerie M.; Goh, Henry G.; Gourdet, Muryam A. A.; Hough, Christina M.; Nickell, Ashley E.; Peer, Adrian G.; Coley, John D.; Tanner, Kimberly D.

2013-01-01

There are widespread aspirations to focus undergraduate biology education on teaching students to think conceptually like biologists; however, there is a dearth of assessment tools designed to measure progress from novice to expert biological conceptual thinking. We present the development of a novel assessment tool, the Biology Card Sorting Task, designed to probe how individuals organize their conceptual knowledge of biology. While modeled on tasks from cognitive psychology, this task is unique in its design to test two hypothesized conceptual frameworks for the organization of biological knowledge: 1) a surface feature organization focused on organism type and 2) a deep feature organization focused on fundamental biological concepts. In this initial investigation of the Biology Card Sorting Task, each of six analytical measures showed statistically significant differences when used to compare the card sorting results of putative biological experts (biology faculty) and novices (non–biology major undergraduates). Consistently, biology faculty appeared to sort based on hypothesized deep features, while non–biology majors appeared to sort based on either surface features or nonhypothesized organizational frameworks. Results suggest that this novel task is robust in distinguishing populations of biology experts and biology novices and may be an adaptable tool for tracking emerging biology conceptual expertise. PMID:24297290

Revolution of Alzheimer Precision Neurology Passageway of Systems Biology and Neurophysiology.

PubMed

Hampel, Harald; Toschi, Nicola; Babiloni, Claudio; Baldacci, Filippo; Black, Keith L; Bokde, Arun L W; Bun, René S; Cacciola, Francesco; Cavedo, Enrica; Chiesa, Patrizia A; Colliot, Olivier; Coman, Cristina-Maria; Dubois, Bruno; Duggento, Andrea; Durrleman, Stanley; Ferretti, Maria-Teresa; George, Nathalie; Genthon, Remy; Habert, Marie-Odile; Herholz, Karl; Koronyo, Yosef; Koronyo-Hamaoui, Maya; Lamari, Foudil; Langevin, Todd; Lehéricy, Stéphane; Lorenceau, Jean; Neri, Christian; Nisticò, Robert; Nyasse-Messene, Francis; Ritchie, Craig; Rossi, Simone; Santarnecchi, Emiliano; Sporns, Olaf; Verdooner, Steven R; Vergallo, Andrea; Villain, Nicolas; Younesi, Erfan; Garaci, Francesco; Lista, Simone

2018-03-16

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Revolution of Alzheimer Precision Neurology: Passageway of Systems Biology and Neurophysiology

PubMed Central

Hampel, Harald; Toschi, Nicola; Babiloni, Claudio; Baldacci, Filippo; Black, Keith L.; Bokde, Arun L.W.; Bun, René S.; Cacciola, Francesco; Cavedo, Enrica; Chiesa, Patrizia A.; Colliot, Olivier; Coman, Cristina-Maria; Dubois, Bruno; Duggento, Andrea; Durrleman, Stanley; Ferretti, Maria-Teresa; George, Nathalie; Genthon, Remy; Habert, Marie-Odile; Herholz, Karl; Koronyo, Yosef; Koronyo-Hamaoui, Maya; Lamari, Foudil; Langevin, Todd; Lehéricy, Stéphane; Lorenceau, Jean; Neri, Christian; Nisticò, Robert; Nyasse-Messene, Francis; Ritchie, Craig; Rossi, Simone; Santarnecchi, Emiliano; Sporns, Olaf; Verdooner, Steven R.; Vergallo, Andrea; Villain, Nicolas; Younesi, Erfan; Garaci, Francesco; Lista, Simone

2018-01-01

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an “omics”-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer’s disease (AD). The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group “Alzheimer Precision Medicine” (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development towards breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND. PMID:29562524

Predicting biological condition in southern California streams

USGS Publications Warehouse

Brown, Larry R.; May, Jason T.; Rehn, Andrew C.; Ode, Peter R.; Waite, Ian R.; Kennen, Jonathan G.

2012-01-01

As understanding of the complex relations among environmental stressors and biological responses improves, a logical next step is predictive modeling of biological condition at unsampled sites. We developed a boosted regression tree (BRT) model of biological condition, as measured by a benthic macroinvertebrate index of biotic integrity (BIBI), for streams in urbanized Southern Coastal California. We also developed a multiple linear regression (MLR) model as a benchmark for comparison with the BRT model. The BRT model explained 66% of the variance in B-IBI, identifying watershed population density and combined percentage agricultural and urban land cover in the riparian buffer as the most important predictors of B-IBI, but with watershed mean precipitation and watershed density of manmade channels also important. The MLR model explained 48% of the variance in B-IBI and included watershed population density and combined percentage agricultural and urban land cover in the riparian buffer. For a verification data set, the BRT model correctly classified 75% of impaired sites (B-IBI < 40) and 78% of unimpaired sites (B-IBI = 40). For the same verification data set, the MLR model correctly classified 69% of impaired sites and 87% of unimpaired sites. The BRT model should not be used to predict B-IBI for specific sites; however, the model can be useful for general applications such as identifying and prioritizing regions for monitoring, remediation or preservation, stratifying new bioassessments according to anticipated biological condition, or assessing the potential for change in stream biological condition based on anticipated changes in population density and development in stream buffers.

Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6

USGS Publications Warehouse

Nevers, Meredith; Byappanahalli, Muruleedhara; Phanikumar, Mantha S.; Whitman, Richard L.

2016-01-01

Mathematical models have been widely applied to surface waters to estimate rates of settling, resuspension, flow, dispersion, and advection in order to calculate movement of particles that influence water quality. Of particular interest are the movement, survival, and persistence of microbial pathogens or their surrogates, which may contaminate recreational water, drinking water, or shellfish. Most models devoted to microbial water quality have been focused on fecal indicator organisms (FIO), which act as a surrogate for pathogens and viruses. Process-based modeling and statistical modeling have been used to track contamination events to source and to predict future events. The use of these two types of models require different levels of expertise and input; process-based models rely on theoretical physical constructs to explain present conditions and biological distribution while data-based, statistical models use extant paired data to do the same. The selection of the appropriate model and interpretation of results is critical to proper use of these tools in microbial source tracking. Integration of the modeling approaches could provide insight for tracking and predicting contamination events in real time. A review of modeling efforts reveals that process-based modeling has great promise for microbial source tracking efforts; further, combining the understanding of physical processes influencing FIO contamination developed with process-based models and molecular characterization of the population by gene-based (i.e., biological) or chemical markers may be an effective approach for locating sources and remediating contamination in order to protect human health better.

Models in biology: ‘accurate descriptions of our pathetic thinking’

PubMed Central

2014-01-01

In this essay I will sketch some ideas for how to think about models in biology. I will begin by trying to dispel the myth that quantitative modeling is somehow foreign to biology. I will then point out the distinction between forward and reverse modeling and focus thereafter on the former. Instead of going into mathematical technicalities about different varieties of models, I will focus on their logical structure, in terms of assumptions and conclusions. A model is a logical machine for deducing the latter from the former. If the model is correct, then, if you believe its assumptions, you must, as a matter of logic, also believe its conclusions. This leads to consideration of the assumptions underlying models. If these are based on fundamental physical laws, then it may be reasonable to treat the model as ‘predictive’, in the sense that it is not subject to falsification and we can rely on its conclusions. However, at the molecular level, models are more often derived from phenomenology and guesswork. In this case, the model is a test of its assumptions and must be falsifiable. I will discuss three models from this perspective, each of which yields biological insights, and this will lead to some guidelines for prospective model builders. PMID:24886484

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

2017-02-01

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

Activated sludge model (ASM) based modelling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities.

PubMed

Fenu, A; Guglielmi, G; Jimenez, J; Spèrandio, M; Saroj, D; Lesjean, B; Brepols, C; Thoeye, C; Nopens, I

2010-08-01

Membrane bioreactors (MBRs) have been increasingly employed for municipal and industrial wastewater treatment in the last decade. The efforts for modelling of such wastewater treatment systems have always targeted either the biological processes (treatment quality target) as well as the various aspects of engineering (cost effective design and operation). The development of Activated Sludge Models (ASM) was an important evolution in the modelling of Conventional Activated Sludge (CAS) processes and their use is now very well established. However, although they were initially developed to describe CAS processes, they have simply been transferred and applied to MBR processes. Recent studies on MBR biological processes have reported several crucial specificities: medium to very high sludge retention times, high mixed liquor concentration, accumulation of soluble microbial products (SMP) rejected by the membrane filtration step, and high aeration rates for scouring purposes. These aspects raise the question as to what extent the ASM framework is applicable to MBR processes. Several studies highlighting some of the aforementioned issues are scattered through the literature. Hence, through a concise and structured overview of the past developments and current state-of-the-art in biological modelling of MBR, this review explores ASM-based modelling applied to MBR processes. The work aims to synthesize previous studies and differentiates between unmodified and modified applications of ASM to MBR. Particular emphasis is placed on influent fractionation, biokinetics, and soluble microbial products (SMPs)/exo-polymeric substances (EPS) modelling, and suggestions are put forward as to good modelling practice with regard to MBR modelling both for end-users and academia. A last section highlights shortcomings and future needs for improved biological modelling of MBR processes. (c) 2010 Elsevier Ltd. All rights reserved.

PathCase-SB architecture and database design

PubMed Central

2011-01-01

Background Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks. Description PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web (currently, BioModels database and KEGG), and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database. Conclusions PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast (real-time) access to the data in the database. PathCase-SB itself is already being used by researchers across the world. PMID:22070889

Model Uncertainty via the Integration of Hormesis and LNT as the Default in Cancer Risk Assessment.

PubMed

Calabrese, Edward J

2015-01-01

On June 23, 2015, the US Nuclear Regulatory Commission (NRC) issued a formal notice in the Federal Register that it would consider whether "it should amend its 'Standards for Protection Against Radiation' regulations from the linear non-threshold (LNT) model of radiation protection to the hormesis model." The present commentary supports this recommendation based on the (1) flawed and deceptive history of the adoption of LNT by the US National Academy of Sciences (NAS) in 1956; (2) the documented capacity of hormesis to make more accurate predictions of biological responses for diverse biological end points in the low-dose zone; (3) the occurrence of extensive hormetic data from the peer-reviewed biomedical literature that revealed hormetic responses are highly generalizable, being independent of biological model, end point measured, inducing agent, level of biological organization, and mechanism; and (4) the integration of hormesis and LNT models via a model uncertainty methodology that optimizes public health responses at 10(-4). Thus, both LNT and hormesis can be integratively used for risk assessment purposes, and this integration defines the so-called "regulatory sweet spot."

Noninvasive Assessment of Cell Fate and Biology in Transplanted Mesenchymal Stem Cells.

PubMed

Franchi, Federico; Rodriguez-Porcel, Martin

2017-01-01

Recently, molecular imaging has become a conditio sine qua non for cell-based regenerative medicine. Developments in molecular imaging techniques, such as reporter gene technology, have increasingly enabled the noninvasive assessment of the fate and biology of cells after cardiovascular applications. In this context, bioluminescence imaging is the most commonly used imaging modality in small animal models of preclinical studies. Here, we present a detailed protocol of a reporter gene imaging approach for monitoring the viability and biology of Mesenchymal Stem Cells transplanted in a mouse model of myocardial ischemia reperfusion injury.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

PubMed

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Modular, Semantics-Based Composition of Biosimulation Models

ERIC Educational Resources Information Center

Neal, Maxwell Lewis

2010-01-01

Biosimulation models are valuable, versatile tools used for hypothesis generation and testing, codification of biological theory, education, and patient-specific modeling. Driven by recent advances in computational power and the accumulation of systems-level experimental data, modelers today are creating models with an unprecedented level of…

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

PubMed Central

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database. PMID:25427176

Toward computational cumulative biology by combining models of biological datasets.

PubMed

Faisal, Ali; Peltonen, Jaakko; Georgii, Elisabeth; Rung, Johan; Kaski, Samuel

2014-01-01

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations-for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

An advanced environment for hybrid modeling of biological systems based on modelica.

PubMed

Pross, Sabrina; Bachmann, Bernhard

2011-01-20

Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of continuous differential equations. A special sub-library contains so-called wrappers for specific reactions to simplify the modeling process. The Modelica-models can be connected to Simulink-models for parameter optimization, sensitivity analysis and stochastic simulation in Matlab. The present paper illustrates the implementation of the Petri Net component models, their usage within the modeling process and the coupling between the Modelica-tool Dymola and Matlab/Simulink. The application is demonstrated by modeling the metabolism of Chinese Hamster Ovary Cells.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

SHEDS-HT: An Integrated Probabilistic Exposure Model for Prioritizing Exposures to Chemicals with Near-Field and Dietary Sources

EPA Science Inventory

United States Environmental Protection Agency (USEPA) researchers are developing a strategy for highthroughput (HT) exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for evaluating chemicals based on their potential for biologi...

A novel medical image data-based multi-physics simulation platform for computational life sciences.

PubMed

Neufeld, Esra; Szczerba, Dominik; Chavannes, Nicolas; Kuster, Niels

2013-04-06

Simulating and modelling complex biological systems in computational life sciences requires specialized software tools that can perform medical image data-based modelling, jointly visualize the data and computational results, and handle large, complex, realistic and often noisy anatomical models. The required novel solvers must provide the power to model the physics, biology and physiology of living tissue within the full complexity of the human anatomy (e.g. neuronal activity, perfusion and ultrasound propagation). A multi-physics simulation platform satisfying these requirements has been developed for applications including device development and optimization, safety assessment, basic research, and treatment planning. This simulation platform consists of detailed, parametrized anatomical models, a segmentation and meshing tool, a wide range of solvers and optimizers, a framework for the rapid development of specialized and parallelized finite element method solvers, a visualization toolkit-based visualization engine, a Python scripting interface for customized applications, a coupling framework, and more. Core components are cross-platform compatible and use open formats. Several examples of applications are presented: hyperthermia cancer treatment planning, tumour growth modelling, evaluating the magneto-haemodynamic effect as a biomarker and physics-based morphing of anatomical models.

Towards the virtual artery: a multiscale model for vascular physiology at the physics-chemistry-biology interface.

PubMed

Hoekstra, Alfons G; Alowayyed, Saad; Lorenz, Eric; Melnikova, Natalia; Mountrakis, Lampros; van Rooij, Britt; Svitenkov, Andrew; Závodszky, Gábor; Zun, Pavel

2016-11-13

This discussion paper introduces the concept of the Virtual Artery as a multiscale model for arterial physiology and pathologies at the physics-chemistry-biology (PCB) interface. The cellular level is identified as the mesoscopic level, and we argue that by coupling cell-based models with other relevant models on the macro- and microscale, a versatile model of arterial health and disease can be composed. We review the necessary ingredients, both models of arteries at many different scales, as well as generic methods to compose multiscale models. Next, we discuss how this can be combined into the virtual artery. Finally, we argue that the concept of models at the PCB interface could or perhaps should become a powerful paradigm, not only as in our case for studying physiology, but also for many other systems that have such PCB interfaces.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'. © 2016 The Authors.

Microdosimetric Modeling of Biological Effectiveness for Boron Neutron Capture Therapy Considering Intra- and Intercellular Heterogeneity in 10B Distribution.

PubMed

Sato, Tatsuhiko; Masunaga, Shin-Ichiro; Kumada, Hiroaki; Hamada, Nobuyuki

2018-01-17

We here propose a new model for estimating the biological effectiveness for boron neutron capture therapy (BNCT) considering intra- and intercellular heterogeneity in 10 B distribution. The new model was developed from our previously established stochastic microdosimetric kinetic model that determines the surviving fraction of cells irradiated with any radiations. In the model, the probability density of the absorbed doses in microscopic scales is the fundamental physical index for characterizing the radiation fields. A new computational method was established to determine the probability density for application to BNCT using the Particle and Heavy Ion Transport code System PHITS. The parameters used in the model were determined from the measured surviving fraction of tumor cells administrated with two kinds of 10 B compounds. The model quantitatively highlighted the indispensable need to consider the synergetic effect and the dose dependence of the biological effectiveness in the estimate of the therapeutic effect of BNCT. The model can predict the biological effectiveness of newly developed 10 B compounds based on their intra- and intercellular distributions, and thus, it can play important roles not only in treatment planning but also in drug discovery research for future BNCT.

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.

PubMed

Engelhardt, Benjamin; Kschischo, Maik; Fröhlich, Holger

2017-06-01

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α -Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data. © 2017 The Author(s).

Agent Based Modeling Applications for Geosciences

NASA Astrophysics Data System (ADS)

Stein, J. S.

2004-12-01

Agent-based modeling techniques have successfully been applied to systems in which complex behaviors or outcomes arise from varied interactions between individuals in the system. Each individual interacts with its environment, as well as with other individuals, by following a set of relatively simple rules. Traditionally this "bottom-up" modeling approach has been applied to problems in the fields of economics and sociology, but more recently has been introduced to various disciplines in the geosciences. This technique can help explain the origin of complex processes from a relatively simple set of rules, incorporate large and detailed datasets when they exist, and simulate the effects of extreme events on system-wide behavior. Some of the challenges associated with this modeling method include: significant computational requirements in order to keep track of thousands to millions of agents, methods and strategies of model validation are lacking, as is a formal methodology for evaluating model uncertainty. Challenges specific to the geosciences, include how to define agents that control water, contaminant fluxes, climate forcing and other physical processes and how to link these "geo-agents" into larger agent-based simulations that include social systems such as demographics economics and regulations. Effective management of limited natural resources (such as water, hydrocarbons, or land) requires an understanding of what factors influence the demand for these resources on a regional and temporal scale. Agent-based models can be used to simulate this demand across a variety of sectors under a range of conditions and determine effective and robust management policies and monitoring strategies. The recent focus on the role of biological processes in the geosciences is another example of an area that could benefit from agent-based applications. A typical approach to modeling the effect of biological processes in geologic media has been to represent these processes in a thermodynamic framework as a set of reactions that roll-up the integrated effect that diverse biological communities exert on a geological system. This approach may work well to predict the effect of certain biological communities in specific environments in which experimental data is available. However, it does not further our knowledge of how the geobiological system actually functions on a micro scale. Agent-based techniques may provide a framework to explore the fundamental interactions required to explain the system-wide behavior. This presentation will present a survey of several promising applications of agent-based modeling approaches to problems in the geosciences and describe specific contributions to some of the inherent challenges facing this approach.

A Computational Model of the Rainbow Trout Hypothalamus-Pituitary-Ovary-Liver Axis

PubMed Central

Gillies, Kendall; Krone, Stephen M.; Nagler, James J.; Schultz, Irvin R.

2016-01-01

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. In recent years there has been rapid growth in understanding fish reproductive biology, which has been motivated in part by recognition of the potential effects that climate change, habitat destruction and contaminant exposure can have on natural and cultured fish populations. New approaches to understanding the impacts of these stressors are being developed that require a systems biology approach with more biologically accurate and detailed mathematical models. We have developed a multi-scale mathematical model of the female rainbow trout hypothalamus-pituitary-ovary-liver axis to use as a tool to help understand the functioning of the system and for extrapolation of laboratory findings of stressor impacts on specific components of the axis. The model describes the essential endocrine components of the female rainbow trout reproductive axis. The model also describes the stage specific growth of maturing oocytes within the ovary and permits the presence of sub-populations of oocytes at different stages of development. Model formulation and parametrization was largely based on previously published in vivo and in vitro data in rainbow trout and new data on the synthesis of gonadotropins in the pituitary. Model predictions were validated against several previously published data sets for annual changes in gonadotropins and estradiol in rainbow trout. Estimates of select model parameters can be obtained from in vitro assays using either quantitative (direct estimation of rate constants) or qualitative (relative change from control values) approaches. This is an important aspect of mathematical models as in vitro, cell-based assays are expected to provide the bulk of experimental data for future risk assessments and will require quantitative physiological models to extrapolate across biological scales. PMID:27096735

A Computational Model of the Rainbow Trout Hypothalamus-Pituitary-Ovary-Liver Axis.

PubMed

Gillies, Kendall; Krone, Stephen M; Nagler, James J; Schultz, Irvin R

2016-04-01

Reproduction in fishes and other vertebrates represents the timely coordination of many endocrine factors that culminate in the production of mature, viable gametes. In recent years there has been rapid growth in understanding fish reproductive biology, which has been motivated in part by recognition of the potential effects that climate change, habitat destruction and contaminant exposure can have on natural and cultured fish populations. New approaches to understanding the impacts of these stressors are being developed that require a systems biology approach with more biologically accurate and detailed mathematical models. We have developed a multi-scale mathematical model of the female rainbow trout hypothalamus-pituitary-ovary-liver axis to use as a tool to help understand the functioning of the system and for extrapolation of laboratory findings of stressor impacts on specific components of the axis. The model describes the essential endocrine components of the female rainbow trout reproductive axis. The model also describes the stage specific growth of maturing oocytes within the ovary and permits the presence of sub-populations of oocytes at different stages of development. Model formulation and parametrization was largely based on previously published in vivo and in vitro data in rainbow trout and new data on the synthesis of gonadotropins in the pituitary. Model predictions were validated against several previously published data sets for annual changes in gonadotropins and estradiol in rainbow trout. Estimates of select model parameters can be obtained from in vitro assays using either quantitative (direct estimation of rate constants) or qualitative (relative change from control values) approaches. This is an important aspect of mathematical models as in vitro, cell-based assays are expected to provide the bulk of experimental data for future risk assessments and will require quantitative physiological models to extrapolate across biological scales.

Therapeutic target discovery using Boolean network attractors: improvements of kali

PubMed Central

Guziolowski, Carito

2018-01-01

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modelling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are (i) the possibility to work on asynchronous Boolean networks, (ii) a finer assessment of therapeutic targets and (iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modelled biological system. Given a logic-based model of pathological mechanisms, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness. kali is illustrated on an example network and used on a biological case study. The case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results. However, like any computational tool, kali can predict but cannot replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery. PMID:29515890

Algorithm for cellular reprogramming.

PubMed

Ronquist, Scott; Patterson, Geoff; Muir, Lindsey A; Lindsly, Stephen; Chen, Haiming; Brown, Markus; Wicha, Max S; Bloch, Anthony; Brockett, Roger; Rajapakse, Indika

2017-11-07

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology. With data-guided frameworks we can develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. Here we describe an approach for optimizing the use of transcription factors (TFs) in cellular reprogramming, based on a device commonly used in optimal control. We construct an approximate model for the natural evolution of a cell-cycle-synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points during the cell cycle. To arrive at a model of moderate complexity, we cluster gene expression based on division of the genome into topologically associating domains (TADs) and then model the dynamics of TAD expression levels. Based on this dynamical model and additional data, such as known TF binding sites and activity, we develop a methodology for identifying the top TF candidates for a specific cellular reprogramming task. Our data-guided methodology identifies a number of TFs previously validated for reprogramming and/or natural differentiation and predicts some potentially useful combinations of TFs. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes. Copyright © 2017 the Author(s). Published by PNAS.

Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome

PubMed Central

Jacquez, Geoffrey M.; Sabel, Clive E.; Shi, Chen

2015-01-01

The exposome, defined as the totality of an individual’s exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal and behavioral determinants (the behavome). Genetic GISc poses three key needs: First, a mathematical foundation for emergent theory; Second, process-based models that bridge biological and geographic scales; Third, biologically plausible estimates of space-time disease lags. Compartmental models are a possible solution; this article develops two models using pancreatic cancer as an exemplar. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages by diagnostic criteria. These provide empirical estimates of the distribution of latencies in cellular states and disease stages, and maps of the burden of yet to be diagnosed disease. This approach links our emerging knowledge of genomics to cancer progression at the cellular level, to individuals and their cancer stage at diagnosis, to geographic distributions of cancer in extant populations. These methodological developments and exemplar provide the basis for a new synthesis in health geography: genetic geographic information science. PMID:26339073

The origin of neutron biological effectiveness as a function of energy.

PubMed

Baiocco, G; Barbieri, S; Babini, G; Morini, J; Alloni, D; Friedland, W; Kundrát, P; Schmitt, E; Puchalska, M; Sihver, L; Ottolenghi, A

2016-09-22

The understanding of the impact of radiation quality in early and late responses of biological targets to ionizing radiation exposure necessarily grounds on the results of mechanistic studies starting from physical interactions. This is particularly true when, already at the physical stage, the radiation field is mixed, as it is the case for neutron exposure. Neutron Relative Biological Effectiveness (RBE) is energy dependent, maximal for energies ~1 MeV, varying significantly among different experiments. The aim of this work is to shed light on neutron biological effectiveness as a function of field characteristics, with a comprehensive modeling approach: this brings together transport calculations of neutrons through matter (with the code PHITS) and the predictive power of the biophysical track structure code PARTRAC in terms of DNA damage evaluation. Two different energy dependent neutron RBE models are proposed: the first is phenomenological and based only on the characterization of linear energy transfer on a microscopic scale; the second is purely ab-initio and based on the induction of complex DNA damage. Results for the two models are compared and found in good qualitative agreement with current standards for radiation protection factors, which are agreed upon on the basis of RBE data.

The origin of neutron biological effectiveness as a function of energy

NASA Astrophysics Data System (ADS)

Baiocco, G.; Barbieri, S.; Babini, G.; Morini, J.; Alloni, D.; Friedland, W.; Kundrát, P.; Schmitt, E.; Puchalska, M.; Sihver, L.; Ottolenghi, A.

2016-09-01

The understanding of the impact of radiation quality in early and late responses of biological targets to ionizing radiation exposure necessarily grounds on the results of mechanistic studies starting from physical interactions. This is particularly true when, already at the physical stage, the radiation field is mixed, as it is the case for neutron exposure. Neutron Relative Biological Effectiveness (RBE) is energy dependent, maximal for energies ~1 MeV, varying significantly among different experiments. The aim of this work is to shed light on neutron biological effectiveness as a function of field characteristics, with a comprehensive modeling approach: this brings together transport calculations of neutrons through matter (with the code PHITS) and the predictive power of the biophysical track structure code PARTRAC in terms of DNA damage evaluation. Two different energy dependent neutron RBE models are proposed: the first is phenomenological and based only on the characterization of linear energy transfer on a microscopic scale; the second is purely ab-initio and based on the induction of complex DNA damage. Results for the two models are compared and found in good qualitative agreement with current standards for radiation protection factors, which are agreed upon on the basis of RBE data.

Gene-Environment Interactions in Cardiovascular Disease

PubMed Central

Flowers, Elena; Froelicher, Erika Sivarajan; Aouizerat, Bradley E.

2011-01-01

Background Historically, models to describe disease were exclusively nature-based or nurture-based. Current theoretical models for complex conditions such as cardiovascular disease acknowledge the importance of both biologic and non-biologic contributors to disease. A critical feature is the occurrence of interactions between numerous risk factors for disease. The interaction between genetic (i.e. biologic, nature) and environmental (i.e. non-biologic, nurture) causes of disease is an important mechanism for understanding both the etiology and public health impact of cardiovascular disease. Objectives The purpose of this paper is to describe theoretical underpinnings of gene-environment interactions, models of interaction, methods for studying gene-environment interactions, and the related concept of interactions between epigenetic mechanisms and the environment. Discussion Advances in methods for measurement of genetic predictors of disease have enabled an increasingly comprehensive understanding of the causes of disease. In order to fully describe the effects of genetic predictors of disease, it is necessary to place genetic predictors within the context of known environmental risk factors. The additive or multiplicative effect of the interaction between genetic and environmental risk factors is often greater than the contribution of either risk factor alone. PMID:21684212

The origin of neutron biological effectiveness as a function of energy

PubMed Central

Baiocco, G.; Barbieri, S.; Babini, G.; Morini, J.; Alloni, D.; Friedland, W.; Kundrát, P.; Schmitt, E.; Puchalska, M.; Sihver, L.; Ottolenghi, A.

2016-01-01

The understanding of the impact of radiation quality in early and late responses of biological targets to ionizing radiation exposure necessarily grounds on the results of mechanistic studies starting from physical interactions. This is particularly true when, already at the physical stage, the radiation field is mixed, as it is the case for neutron exposure. Neutron Relative Biological Effectiveness (RBE) is energy dependent, maximal for energies ~1 MeV, varying significantly among different experiments. The aim of this work is to shed light on neutron biological effectiveness as a function of field characteristics, with a comprehensive modeling approach: this brings together transport calculations of neutrons through matter (with the code PHITS) and the predictive power of the biophysical track structure code PARTRAC in terms of DNA damage evaluation. Two different energy dependent neutron RBE models are proposed: the first is phenomenological and based only on the characterization of linear energy transfer on a microscopic scale; the second is purely ab-initio and based on the induction of complex DNA damage. Results for the two models are compared and found in good qualitative agreement with current standards for radiation protection factors, which are agreed upon on the basis of RBE data. PMID:27654349

Structure-based Markov random field model for representing evolutionary constraints on functional sites.

PubMed

Jeong, Chan-Seok; Kim, Dongsup

2016-02-24

Elucidating the cooperative mechanism of interconnected residues is an important component toward understanding the biological function of a protein. Coevolution analysis has been developed to model the coevolutionary information reflecting structural and functional constraints. Recently, several methods have been developed based on a probabilistic graphical model called the Markov random field (MRF), which have led to significant improvements for coevolution analysis; however, thus far, the performance of these models has mainly been assessed by focusing on the aspect of protein structure. In this study, we built an MRF model whose graphical topology is determined by the residue proximity in the protein structure, and derived a novel positional coevolution estimate utilizing the node weight of the MRF model. This structure-based MRF method was evaluated for three data sets, each of which annotates catalytic site, allosteric site, and comprehensively determined functional site information. We demonstrate that the structure-based MRF architecture can encode the evolutionary information associated with biological function. Furthermore, we show that the node weight can more accurately represent positional coevolution information compared to the edge weight. Lastly, we demonstrate that the structure-based MRF model can be reliably built with only a few aligned sequences in linear time. The results show that adoption of a structure-based architecture could be an acceptable approximation for coevolution modeling with efficient computation complexity.

PomBase: The Scientific Resource for Fission Yeast.

PubMed

Lock, Antonia; Rutherford, Kim; Harris, Midori A; Wood, Valerie

2018-01-01

The fission yeast Schizosaccharomyces pombe has become well established as a model species for studying conserved cell-level biological processes, especially the mechanics and regulation of cell division. PomBase integrates the S. pombe genome sequence with traditional genetic, molecular, and cell biological experimental data as well as the growing body of large datasets generated by emerging high-throughput methods. This chapter provides insight into the curation philosophy and data organization at PomBase, and provides a guide to using PomBase for infrequent visitors and anyone considering exploring S. pombe in their research.

Challenges in structural approaches to cell modeling.

PubMed

Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A

2016-07-31

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. Copyright © 2016 Elsevier Ltd. All rights reserved.

Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment.

PubMed

Masoudi-Nejad, Ali; Asgari, Yazdan

2015-02-01

The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Developing Guided Inquiry-Based Student Lab Worksheet for Laboratory Knowledge Course

NASA Astrophysics Data System (ADS)

Rahmi, Y. L.; Novriyanti, E.; Ardi, A.; Rifandi, R.

2018-04-01

The course of laboratory knowledge is an introductory course for biology students to follow various lectures practicing in the biology laboratory. Learning activities of laboratory knowledge course at this time in the Biology Department, Universitas Negeri Padang has not been completed by supporting learning media such as student lab worksheet. Guided inquiry learning model is one of the learning models that can be integrated into laboratory activity. The study aimed to produce student lab worksheet based on guided inquiry for laboratory knowledge course and to determine the validity of lab worksheet. The research was conducted using research and developmet (R&D) model. The instruments used in data collection in this research were questionnaire for student needed analysis and questionnaire to measure the student lab worksheet validity. The data obtained was quantitative from several validators. The validators consist of three lecturers. The percentage of a student lab worksheet validity was 94.18 which can be categorized was very good.

Full Monte Carlo-Based Biologic Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy on Graphics Processing Unit.

PubMed

Qin, Nan; Shen, Chenyang; Tsai, Min-Yu; Pinto, Marco; Tian, Zhen; Dedes, Georgios; Pompos, Arnold; Jiang, Steve B; Parodi, Katia; Jia, Xun

2018-01-01

One of the major benefits of carbon ion therapy is enhanced biological effectiveness at the Bragg peak region. For intensity modulated carbon ion therapy (IMCT), it is desirable to use Monte Carlo (MC) methods to compute the properties of each pencil beam spot for treatment planning, because of their accuracy in modeling physics processes and estimating biological effects. We previously developed goCMC, a graphics processing unit (GPU)-oriented MC engine for carbon ion therapy. The purpose of the present study was to build a biological treatment plan optimization system using goCMC. The repair-misrepair-fixation model was implemented to compute the spatial distribution of linear-quadratic model parameters for each spot. A treatment plan optimization module was developed to minimize the difference between the prescribed and actual biological effect. We used a gradient-based algorithm to solve the optimization problem. The system was embedded in the Varian Eclipse treatment planning system under a client-server architecture to achieve a user-friendly planning environment. We tested the system with a 1-dimensional homogeneous water case and 3 3-dimensional patient cases. Our system generated treatment plans with biological spread-out Bragg peaks covering the targeted regions and sparing critical structures. Using 4 NVidia GTX 1080 GPUs, the total computation time, including spot simulation, optimization, and final dose calculation, was 0.6 hour for the prostate case (8282 spots), 0.2 hour for the pancreas case (3795 spots), and 0.3 hour for the brain case (6724 spots). The computation time was dominated by MC spot simulation. We built a biological treatment plan optimization system for IMCT that performs simulations using a fast MC engine, goCMC. To the best of our knowledge, this is the first time that full MC-based IMCT inverse planning has been achieved in a clinically viable time frame. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of undergraduate students' conceptual models of a complex biological system across a diverse body of learners

NASA Astrophysics Data System (ADS)

Dirnbeck, Matthew R.

Biological systems pose a challenge both for learners and teachers because they are complex systems mediated by feedback loops; networks of cause-effect relationships; and non-linear, hierarchical, and emergent properties. Teachers and scientists routinely use models to communicate ideas about complex systems. Model-based pedagogies engage students in model construction as a means of practicing higher-order reasoning skills. One such modeling paradigm describes systems in terms of their structures, behaviors, and functions (SBF). The SBF framework is a simple modeling language that has been used to teach about complex biological systems. Here, we used student-generated SBF models to assess students' causal reasoning in the context of a novel biological problem on an exam. We compared students' performance on the modeling problem, their performance on a set of knowledge/comprehension questions, and their performance on a set of scientific reasoning questions. We found that students who performed well on knowledge and understanding questions also constructed more networked, higher quality models. Previous studies have shown that learners' mental maps increase in complexity with increased expertise. We wanted to investigate if biology students with varying levels of training in biology showed a similar pattern when constructing system models. In a pilot study, we administered the same modeling problem to two additional groups of students: 1) an animal physiology course for students pursuing a major in biology (n=37) and 2) an exercise physiology course for non-majors (n=27). We found that there was no significant difference in model organization across the three student populations, but there was a significant difference in the ability to represent function between the three populations. Between the three groups the non-majors had the lowest function scores, the introductory majors had the middle function scores, and the upper division majors had the highest function scores.

A novel paradigm for cell and molecule interaction ontology: from the CMM model to IMGT-ONTOLOGY

PubMed Central

2010-01-01

Background Biology is moving fast toward the virtuous circle of other disciplines: from data to quantitative modeling and back to data. Models are usually developed by mathematicians, physicists, and computer scientists to translate qualitative or semi-quantitative biological knowledge into a quantitative approach. To eliminate semantic confusion between biology and other disciplines, it is necessary to have a list of the most important and frequently used concepts coherently defined. Results We propose a novel paradigm for generating new concepts for an ontology, starting from model rather than developing a database. We apply that approach to generate concepts for cell and molecule interaction starting from an agent based model. This effort provides a solid infrastructure that is useful to overcome the semantic ambiguities that arise between biologists and mathematicians, physicists, and computer scientists, when they interact in a multidisciplinary field. Conclusions This effort represents the first attempt at linking molecule ontology with cell ontology, in IMGT-ONTOLOGY, the well established ontology in immunogenetics and immunoinformatics, and a paradigm for life science biology. With the increasing use of models in biology and medicine, the need to link different levels, from molecules to cells to tissues and organs, is increasingly important. PMID:20167082

Agent-Based Modeling in Molecular Systems Biology.

PubMed

Soheilypour, Mohammad; Mofrad, Mohammad R K

2018-07-01

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease. © 2018 WILEY Periodicals, Inc.

A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

PubMed

Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua; Huang, Wei

2017-12-01

As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n 2 ) time complexity. Copyright © 2017. Published by Elsevier B.V.

Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection

NASA Astrophysics Data System (ADS)

Hashimoto, Ryoji; Matsumura, Tomoya; Nozato, Yoshihiro; Watanabe, Kenji; Onoye, Takao

A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640×512 pixel input images can be processed in real-time with three agents at a rate of 9fps in 48MHz operation.

The Spring of Systems Biology-Driven Breeding.

PubMed

Lavarenne, Jérémy; Guyomarc'h, Soazig; Sallaud, Christophe; Gantet, Pascal; Lucas, Mikaël

2018-05-12

Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies. Copyright © 2018 Elsevier Ltd. All rights reserved.

Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems

EPA Science Inventory

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, ...

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments. PMID:28190948

Cellular track model of biological damage to mammalian cell cultures from galactic cosmic rays

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Katz, Robert; Wilson, John W.; Townsend, Lawrence W.; Nealy, John E.; Shinn, Judy L.

1991-01-01

The assessment of biological damage from the galactic cosmic rays (GCR) is a current interest for exploratory class space missions where the highly ionizing, high-energy, high-charge ions (HZE) particles are the major concern. The relative biological effectiveness (RBE) values determined by ground-based experiments with HZE particles are well described by a parametric track theory of cell inactivation. Using the track model and a deterministic GCR transport code, the biological damage to mammalian cell cultures is considered for 1 year in free space at solar minimum for typical spacecraft shielding. Included are the effects of projectile and target fragmentation. The RBE values for the GCR spectrum which are fluence-dependent in the track model are found to be more severe than the quality factors identified by the International Commission on Radiological Protection publication 26 and seem to obey a simple scaling law with the duration period in free space.

Urothelial cancer of the upper urinary tract: emerging biomarkers and integrative models for risk stratification.

PubMed

Mathieu, Romain; Vartolomei, Mihai D; Mbeutcha, Aurélie; Karakiewicz, Pierre I; Briganti, Alberto; Roupret, Morgan; Shariat, Shahrokh F

2016-08-01

The aim of this review was to provide an overview of current biomarkers and risk stratification models in urothelial cancer of the upper urinary tract (UTUC). A non-systematic Medline/PubMed literature search was performed using the terms "biomarkers", "preoperative models", "postoperative models", "risk stratification", together with "upper tract urothelial carcinoma". Original articles published between January 2003 and August 2015 were included based on their clinical relevance. Additional references were collected by cross referencing the bibliography of the selected articles. Various promising predictive and prognostic biomarkers have been identified in UTUC thanks to the increasing knowledge of the different biological pathways involved in UTUC tumorigenesis. These biomarkers may help identify tumors with aggressive biology and worse outcomes. Current tools aim at predicting muscle invasive or non-organ confined disease, renal failure after radical nephroureterectomy and survival outcomes. These models are still mainly based on imaging and clinicopathological feature and none has integrated biomarkers. Risk stratification in UTUC is still suboptimal, especially in the preoperative setting due to current limitations in staging and grading. Identification of novel biomarkers and external validation of current prognostic models may help improve risk stratification to allow evidence-based counselling for kidney-sparing approaches, perioperative chemotherapy and/or risk-based surveillance. Despite growing understanding of the biology underlying UTUC, management of this disease remains difficult due to the lack of validated biomarkers and the limitations of current predictive and prognostic tools. Further efforts and collaborations are necessaryry to allow their integration in daily practice.

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data.

PubMed

Yang, Laurence; Tan, Justin; O'Brien, Edward J; Monk, Jonathan M; Kim, Donghyuk; Li, Howard J; Charusanti, Pep; Ebrahim, Ali; Lloyd, Colton J; Yurkovich, James T; Du, Bin; Dräger, Andreas; Thomas, Alex; Sun, Yuekai; Saunders, Michael A; Palsson, Bernhard O

2015-08-25

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

Evolving cell models for systems and synthetic biology.

PubMed

Cao, Hongqing; Romero-Campero, Francisco J; Heeb, Stephan; Cámara, Miguel; Krasnogor, Natalio

2010-03-01

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm's results as well as of the resulting evolved cell models.

Improving Conceptual Understanding and Representation Skills through Excel-Based Modeling

ERIC Educational Resources Information Center

Malone, Kathy L.; Schunn, Christian D.; Schuchardt, Anita M.

2018-01-01

The National Research Council framework for science education and the Next Generation Science Standards have developed a need for additional research and development of curricula that is both technologically model-based and includes engineering practices. This is especially the case for biology education. This paper describes a quasi-experimental…

The sensitivity of Turing self-organization to biological feedback delays: 2D models of fish pigmentation.

PubMed

Gaffney, E A; Lee, S Seirin

2015-03-01

Turing morphogen models have been extensively explored in the context of large-scale self-organization in multicellular biological systems. However, reconciling the detailed biology of morphogen dynamics, while accounting for time delays associated with gene expression, reveals aberrant behaviours that are not consistent with early developmental self-organization, especially the requirement for exquisite temporal control. Attempts to reconcile the interpretation of Turing's ideas with an increasing understanding of the mechanisms driving zebrafish pigmentation suggests that one should reconsider Turing's model in terms of pigment cells rather than morphogens (Nakamasu et al., 2009, PNAS, 106: , 8429-8434; Yamaguchi et al., 2007, PNAS, 104: , 4790-4793). Here the dynamics of pigment cells is subject to response delays implicit in the cell cycle and apoptosis. Hence we explore simulations of fish skin patterning, focussing on the dynamical influence of gene expression delays in morphogen-based Turing models and response delays for cell-based Turing models. We find that reconciling the mechanisms driving the behaviour of Turing systems with observations of fish skin patterning remains a fundamental challenge. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

AGENT-BASED MODELS IN EMPIRICAL SOCIAL RESEARCH*

PubMed Central

Bruch, Elizabeth; Atwell, Jon

2014-01-01

Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research. PMID:25983351

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.

The physical and biological basis of quantitative parameters derived from diffusion MRI

PubMed Central

2012-01-01

Diffusion magnetic resonance imaging is a quantitative imaging technique that measures the underlying molecular diffusion of protons. Diffusion-weighted imaging (DWI) quantifies the apparent diffusion coefficient (ADC) which was first used to detect early ischemic stroke. However this does not take account of the directional dependence of diffusion seen in biological systems (anisotropy). Diffusion tensor imaging (DTI) provides a mathematical model of diffusion anisotropy and is widely used. Parameters, including fractional anisotropy (FA), mean diffusivity (MD), parallel and perpendicular diffusivity can be derived to provide sensitive, but non-specific, measures of altered tissue structure. They are typically assessed in clinical studies by voxel-based or region-of-interest based analyses. The increasing recognition of the limitations of the diffusion tensor model has led to more complex multi-compartment models such as CHARMED, AxCaliber or NODDI being developed to estimate microstructural parameters including axonal diameter, axonal density and fiber orientations. However these are not yet in routine clinical use due to lengthy acquisition times. In this review, I discuss how molecular diffusion may be measured using diffusion MRI, the biological and physical bases for the parameters derived from DWI and DTI, how these are used in clinical studies and the prospect of more complex tissue models providing helpful micro-structural information. PMID:23289085

Untangling nociceptive, neuropathic and neuroplastic mechanisms underlying the biological domain of back pain.

PubMed

Hush, Julia M; Stanton, Tasha R; Siddall, Philip; Marcuzzi, Anna; Attal, Nadine

2013-05-01

SUMMARY Current clinical practice guidelines advocate a model of diagnostic triage for back pain, underpinned by the biopsychosocial paradigm. However, limitations of this clinical model have become apparent: it can be difficult to classify patients into the diagnostic triage categories; patients with 'nonspecific back pain' are clearly not a homogenous group; and mean effects of treatments based on this approach are small. In this article, it is proposed that the biological domain of the biopsychosocial model needs to be reconceptualized using a neurobiological mechanism-based approach. Recent evidence about nociceptive and neuropathic contributors to back pain is outlined in the context of maladaptive neuroplastic changes of the somatosensory system. Implications for clinical practice and research are discussed.

Qualitative modeling of normal blood coagulation and its pathological states using stochastic activity networks.

PubMed

Mounts, W M; Liebman, M N

1997-07-01

We have developed a method for representing biological pathways and simulating their behavior based on the use of stochastic activity networks (SANs). SANs, an extension of the original Petri net, have been used traditionally to model flow systems including data-communications networks and manufacturing processes. We apply the methodology to the blood coagulation cascade, a biological flow system, and present the representation method as well as results of simulation studies based on published experimental data. In addition to describing the dynamic model, we also present the results of its utilization to perform simulations of clinical states including hemophilia's A and B as well as sensitivity analysis of individual factors and their impact on thrombin production.

On the mechanics of growing thin biological membranes

NASA Astrophysics Data System (ADS)

Rausch, Manuel K.; Kuhl, Ellen

2014-02-01

Despite their seemingly delicate appearance, thin biological membranes fulfill various crucial roles in the human body and can sustain substantial mechanical loads. Unlike engineering structures, biological membranes are able to grow and adapt to changes in their mechanical environment. Finite element modeling of biological growth holds the potential to better understand the interplay of membrane form and function and to reliably predict the effects of disease or medical intervention. However, standard continuum elements typically fail to represent thin biological membranes efficiently, accurately, and robustly. Moreover, continuum models are typically cumbersome to generate from surface-based medical imaging data. Here we propose a computational model for finite membrane growth using a classical midsurface representation compatible with standard shell elements. By assuming elastic incompressibility and membrane-only growth, the model a priori satisfies the zero-normal stress condition. To demonstrate its modular nature, we implement the membrane growth model into the general-purpose non-linear finite element package Abaqus/Standard using the concept of user subroutines. To probe efficiently and robustness, we simulate selected benchmark examples of growing biological membranes under different loading conditions. To demonstrate the clinical potential, we simulate the functional adaptation of a heart valve leaflet in ischemic cardiomyopathy. We believe that our novel approach will be widely applicable to simulate the adaptive chronic growth of thin biological structures including skin membranes, mucous membranes, fetal membranes, tympanic membranes, corneoscleral membranes, and heart valve membranes. Ultimately, our model can be used to identify diseased states, predict disease evolution, and guide the design of interventional or pharmaceutic therapies to arrest or revert disease progression.

On the mechanics of growing thin biological membranes

PubMed Central

Rausch, Manuel K.; Kuhl, Ellen

2013-01-01

Despite their seemingly delicate appearance, thin biological membranes fulfill various crucial roles in the human body and can sustain substantial mechanical loads. Unlike engineering structures, biological membranes are able to grow and adapt to changes in their mechanical environment. Finite element modeling of biological growth holds the potential to better understand the interplay of membrane form and function and to reliably predict the effects of disease or medical intervention. However, standard continuum elements typically fail to represent thin biological membranes efficiently, accurately, and robustly. Moreover, continuum models are typically cumbersome to generate from surface-based medical imaging data. Here we propose a computational model for finite membrane growth using a classical midsurface representation compatible with standard shell elements. By assuming elastic incompressibility and membrane-only growth, the model a priori satisfies the zero-normal stress condition. To demonstrate its modular nature, we implement the membrane growth model into the general-purpose non-linear finite element package Abaqus/Standard using the concept of user subroutines. To probe efficiently and robustness, we simulate selected benchmark examples of growing biological membranes under different loading conditions. To demonstrate the clinical potential, we simulate the functional adaptation of a heart valve leaflet in ischemic cardiomyopathy. We believe that our novel approach will be widely applicable to simulate the adaptive chronic growth of thin biological structures including skin membranes, mucous membranes, fetal membranes, tympanic membranes, corneoscleral membranes, and heart valve membranes. Ultimately, our model can be used to identify diseased states, predict disease evolution, and guide the design of interventional or pharmaceutic therapies to arrest or revert disease progression. PMID:24563551

Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control

PubMed Central

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms. PMID:25389391

Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

PubMed

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

Two Strain Dengue Model with Temporary Cross Immunity and Seasonality

NASA Astrophysics Data System (ADS)

Aguiar, Maíra; Ballesteros, Sebastien; Stollenwerk, Nico

2010-09-01

Models on dengue fever epidemiology have previously shown critical fluctuations with power law distributions and also deterministic chaos in some parameter regions due to the multi-strain structure of the disease pathogen. In our first model including well known biological features, we found a rich dynamical structure including limit cycles, symmetry breaking bifurcations, torus bifurcations, coexisting attractors including isola solutions and deterministic chaos (as indicated by positive Lyapunov exponents) in a much larger parameter region, which is also biologically more plausible than the previous results of other researches. Based on these findings we will investigate the model structures further including seasonality.

Two Strain Dengue Model with Temporary Cross Immunity and Seasonality

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

Aguiar, Maira; Ballesteros, Sebastien; Stollenwerk, Nico

Models on dengue fever epidemiology have previously shown critical fluctuations with power law distributions and also deterministic chaos in some parameter regions due to the multi-strain structure of the disease pathogen. In our first model including well known biological features, we found a rich dynamical structure including limit cycles, symmetry breaking bifurcations, torus bifurcations, coexisting attractors including isola solutions and deterministic chaos (as indicated by positive Lyapunov exponents) in a much larger parameter region, which is also biologically more plausible than the previous results of other researches. Based on these findings we will investigate the model structures further including seasonality.

CelOWS: an ontology based framework for the provision of semantic web services related to biological models.

PubMed

Matos, Ely Edison; Campos, Fernanda; Braga, Regina; Palazzi, Daniele

2010-02-01

The amount of information generated by biological research has lead to an intensive use of models. Mathematical and computational modeling needs accurate description to share, reuse and simulate models as formulated by original authors. In this paper, we introduce the Cell Component Ontology (CelO), expressed in OWL-DL. This ontology captures both the structure of a cell model and the properties of functional components. We use this ontology in a Web project (CelOWS) to describe, query and compose CellML models, using semantic web services. It aims to improve reuse and composition of existent components and allow semantic validation of new models.

Improving the forecast for biodiversity under climate change.

PubMed

Urban, M C; Bocedi, G; Hendry, A P; Mihoub, J-B; Pe'er, G; Singer, A; Bridle, J R; Crozier, L G; De Meester, L; Godsoe, W; Gonzalez, A; Hellmann, J J; Holt, R D; Huth, A; Johst, K; Krug, C B; Leadley, P W; Palmer, S C F; Pantel, J H; Schmitz, A; Zollner, P A; Travis, J M J

2016-09-09

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity. Copyright © 2016, American Association for the Advancement of Science.

Biologically inspired adaptive walking of a quadruped robot.

PubMed

Kimura, Hiroshi; Fukuoka, Yasuhiro; Cohen, Avis H

2007-01-15

We describe here the efforts to induce a quadruped robot to walk with medium-walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical and the neural systems by comparing biological concepts with those necessary conditions described in physical terms. PD-controller at joints constructs the virtual spring-damper system as the viscoelasticity model of a muscle. The neural system model consists of a central pattern generator (CPG), reflexes and responses. We validate the effectiveness of the proposed neural system model control using the quadruped robots called 'Tekken1&2'. MPEG footage of experiments can be seen at http://www.kimura.is.uec.ac.jp.

SBRML: a markup language for associating systems biology data with models.

PubMed

Dada, Joseph O; Spasić, Irena; Paton, Norman W; Mendes, Pedro

2010-04-01

Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results. The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

Multi-Step Usage of in Vivo Models During Rational Drug Design and Discovery

PubMed Central

Williams, Charles H.; Hong, Charles C.

2011-01-01

In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET) properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial models. This systems biology based approach paired with the power of computational biology; genetics and developmental biology provide a methodological framework to avoid the pitfalls of traditional target based drug design. PMID:21731440

Computing biological functions using BioΨ, a formal description of biological processes based on elementary bricks of actions

PubMed Central

Pérès, Sabine; Felicori, Liza; Rialle, Stéphanie; Jobard, Elodie; Molina, Franck

2010-01-01

Motivation: In the available databases, biological processes are described from molecular and cellular points of view, but these descriptions are represented with text annotations that make it difficult to handle them for computation. Consequently, there is an obvious need for formal descriptions of biological processes. Results: We present a formalism that uses the BioΨ concepts to model biological processes from molecular details to networks. This computational approach, based on elementary bricks of actions, allows us to calculate on biological functions (e.g. process comparison, mapping structure–function relationships, etc.). We illustrate its application with two examples: the functional comparison of proteases and the functional description of the glycolysis network. This computational approach is compatible with detailed biological knowledge and can be applied to different kinds of systems of simulation. Availability: www.sysdiag.cnrs.fr/publications/supplementary-materials/BioPsi_Manager/ Contact: sabine.peres@sysdiag.cnrs.fr; franck.molina@sysdiag.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20448138

Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa

2018-08-01

The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC 50 ) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors (EA) and/or charge transfer (ΔN) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑Q Sul ) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC 50 ) values. Copyright © 2018 Elsevier Ltd. All rights reserved.

A prototype computer-aided modelling tool for life-support system models

NASA Technical Reports Server (NTRS)

Preisig, H. A.; Lee, Tae-Yeong; Little, Frank

1990-01-01

Based on the canonical decomposition of physical-chemical-biological systems, a prototype kernel has been developed to efficiently model alternative life-support systems. It supports (1) the work in an interdisciplinary group through an easy-to-use mostly graphical interface, (2) modularized object-oriented model representation, (3) reuse of models, (4) inheritance of structures from model object to model object, and (5) model data base. The kernel is implemented in Modula-II and presently operates on an IBM PC.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

PubMed

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

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

Inquiry-based Science Instruction in High School Biology Courses: A Multiple Case Study

NASA Astrophysics Data System (ADS)

Aso, Eze

A lack of research exists about how secondary school science teachers use inquiry-based instruction to improve student learning. The purpose of this qualitative study was to explore how science teachers used inquiry-based instruction to improve student learning in high school biology courses. The conceptual framework was based on Banchi and Bell's model of increasing levels of complexity for inquiry-based instruction. A multiple case study research design was conducted of biology programs at 3 high schools in an urban school district in the northeastern region of the United States. Participants included 2 biology teachers from each of the 3 high schools. Data were collected from individual interviews with biology teachers, observations of lessons in biology, and documents related to state standards, assessments, and professional development. The first level of data analysis involved coding and categorizing the interview and observation data. A content analysis was used for the documents. The second level of data analysis involved examining data across all sources and all cases for themes and discrepancies. According to study findings, biology teachers used confirmation, structure, and guided inquiry to improve student learning. However, they found open inquiry challenging and frustrating to implement because professional development about scaffolding of instruction over time was needed, and students' reading and writing skills needed to improve. This study contributes to positive social change by providing educators and researchers with a deeper understanding about how to scaffold levels of inquiry-based science instruction in order to help students become scientifically literate citizens.

Understanding essential tremor: progress on the biological front.

PubMed

Louis, Elan D

2014-06-01

For many years, little was written about the underlying biology of ET, despite its high prevalence. Discussions of disease mechanisms were dominated by a focus on tremor physiology. The traditional model of ET, the olivary model, was proposed in the 1970s. The model suffers from several critical problems, and its relevance to ET has been questioned. Recent mechanistic research has focused on the cerebellum. Clinical and neuroimaging studies strongly implicate the importance of this brain region in ET. Recent mechanistic research has been grounded more in tissue-based changes (i.e., postmortem studies of the brain). These studies have collectively and systematically identified a sizable number of changes in the ET cerebellum, and have led to a new model of ET, referred to as the cerebellar degenerative model. Hence, there is a renewed interest in the science behind the biology of ET. How the new understanding of ET will translate into treatment changes is an open question.

BrisSynBio: a BBSRC/EPSRC-funded Synthetic Biology Research Centre.

PubMed

Sedgley, Kathleen R; Race, Paul R; Woolfson, Derek N

2016-06-15

BrisSynBio is the Bristol-based Biotechnology and Biological Sciences Research Council (BBSRC)/Engineering and Physical Sciences Research Council (EPSRC)-funded Synthetic Biology Research Centre. It is one of six such Centres in the U.K. BrisSynBio's emphasis is on rational and predictive bimolecular modelling, design and engineering in the context of synthetic biology. It trains the next generation of synthetic biologists in these approaches, to facilitate translation of fundamental synthetic biology research to industry and the clinic, and to do this within an innovative and responsible research framework. © 2016 The Author(s).

NASA Space Biology Research Associate Program for the 21st Century

NASA Technical Reports Server (NTRS)

Sonnenfeld, Gerald

2000-01-01

The Space Biology Research Associate Program for the 21st Century provided a unique opportunity to train individuals to conduct biological research in hypo- and hyper-gravity, and to conduct ground-based research. This grant was developed to maximize the potential for Space Biology as an emerging discipline and to train a cadre of space biologists. The field of gravitational and space biology is rapidly growing at the future of the field is reflected in the quality and education of its personnel. Our chief objective was to train and develop these scientists rapidly and in a cost effective model.

COBRApy: COnstraints-Based Reconstruction and Analysis for Python.

PubMed

Ebrahim, Ali; Lerman, Joshua A; Palsson, Bernhard O; Hyduke, Daniel R

2013-08-08

COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. http://opencobra.sourceforge.net/

Studying Biology to Understand Risk: Dosimetry Models and Quantitative Adverse Outcome Pathways

EPA Science Inventory

Confidence in the quantitative prediction of risk is increased when the prediction is based to as great an extent as possible on the relevant biological factors that constitute the pathway from exposure to adverse outcome. With the first examples now over 40 years old, physiologi...

Modeling Mendel's Laws on Inheritance in Computational Biology and Medical Sciences

ERIC Educational Resources Information Center

Singh, Gurmukh; Siddiqui, Khalid; Singh, Mankiran; Singh, Satpal

2011-01-01

The current research article is based on a simple and practical way of employing the computational power of widely available, versatile software MS Excel 2007 to perform interactive computer simulations for undergraduate/graduate students in biology, biochemistry, biophysics, microbiology, medicine in college and university classroom setting. To…

Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

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

Esposito, Emilio Xavier, E-mail: emilio@exeResearch.com; The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, IL 60045; Hopfinger, Anton J., E-mail: hopfingr@gmail.com

2015-10-01

Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted frommore » the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. - Highlights: • Proposed toxicity mechanism of action for decorated nanotubes complexes • Discussion of the key molecular features for each endpoint's mechanism of action • Unique mechanisms of action for each of the six biological systems • Hypothesized mechanisms of action based on QSAR/QNAR predictive models.« less

Evaluation of hierarchical models for integrative genomic analyses.

PubMed

Denis, Marie; Tadesse, Mahlet G

2016-03-01

Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data. Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to genomic datasets from two cancer types in the Cancer Genome Atlas project: glioblastoma multiforme and ovarian serous cystadenocarcinoma. The integrative models lead to improved model fit and predictive performance. They also provide a better understanding of the biological mechanisms underlying patients' survival. Source code implementing the integrative models is freely available at https://github.com/mgt000/IntegrativeAnalysis along with example datasets and sample R script applying the models to these data. The TCGA datasets used for analysis are publicly available at https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp marie.denis@cirad.fr or mgt26@georgetown.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

CellML metadata standards, associated tools and repositories

PubMed Central

Beard, Daniel A.; Britten, Randall; Cooling, Mike T.; Garny, Alan; Halstead, Matt D.B.; Hunter, Peter J.; Lawson, James; Lloyd, Catherine M.; Marsh, Justin; Miller, Andrew; Nickerson, David P.; Nielsen, Poul M.F.; Nomura, Taishin; Subramanium, Shankar; Wimalaratne, Sarala M.; Yu, Tommy

2009-01-01

The development of standards for encoding mathematical models is an important component of model building and model sharing among scientists interested in understanding multi-scale physiological processes. CellML provides such a standard, particularly for models based on biophysical mechanisms, and a substantial number of models are now available in the CellML Model Repository. However, there is an urgent need to extend the current CellML metadata standard to provide biological and biophysical annotation of the models in order to facilitate model sharing, automated model reduction and connection to biological databases. This paper gives a broad overview of a number of new developments on CellML metadata and provides links to further methodological details available from the CellML website. PMID:19380315

Epidemiology and Molecular Biology of Head and Neck Cancer.

PubMed

Jou, Adriana; Hess, Jochen

2017-01-01

Head and neck cancer is a common and aggressive malignancy with a high morbidity and mortality profile. Although the large majority of cases resemble head and neck squamous cell carcinoma (HNSCC), the current classification based on anatomic site and tumor stage fails to capture the high level of biologic heterogeneity, and appropriate clinical management remains a major challenge. Hence, a better understanding of the molecular biology of HNSCC is urgently needed to support biomarker development and personalized care for patients. This review focuses on recent findings based on integrative genomics analysis and multi-scale modeling approaches and how they are beginning to provide more sophisticated clues as to the biological and clinical diversity of HNSCC. © 2017 S. Karger GmbH, Freiburg.

Assessing anthropogenic impact on boreal lakes with historical fish species distribution data and hydrogeochemical modeling

PubMed Central

Valinia, Salar; Englund, Göran; Moldan, Filip; Futter, Martyn N; Köhler, Stephan J; Bishop, Kevin; Fölster, Jens

2014-01-01

Quantifying the effects of human activity on the natural environment is dependent on credible estimates of reference conditions to define the state of the environment before the onset of adverse human impacts. In Europe, emission controls that aimed at restoring ecological status were based on hindcasts from process-based models or paleolimnological reconstructions. For instance, 1860 is used in Europe as the target for restoration from acidification concerning biological and chemical parameters. A more practical problem is that the historical states of ecosystems and their function cannot be observed directly. Therefore, we (i) compare estimates of acidification based on long-term observations of roach (Rutilus rutilus) populations with hindcast pH from the hydrogeochemical model MAGIC; (ii) discuss policy implications and possible scope for use of long-term archival data for assessing human impacts on the natural environment and (iii) present a novel conceptual model for interpreting the importance of physico-chemical and ecological deviations from reference conditions. Of the 85 lakes studied, 78 were coherently classified by both methods. In 1980, 28 lakes were classified as acidified with the MAGIC model, however, roach was present in 14 of these. In 2010, MAGIC predicted chemical recovery in 50% of the lakes, however roach only recolonized in five lakes after 1990, showing a lag between chemical and biological recovery. Our study is the first study of its kind to use long-term archival biological data in concert with hydrogeochemical modeling for regional assessments of anthropogenic acidification. Based on our results, we show how the conceptual model can be used to understand and prioritize management of physico-chemical and ecological effects of anthropogenic stressors on surface water quality. PMID:24535943

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

Espinoza, I; Peschke, P; Karger, C

Purpose: In radiotherapy, it is important to predict the response of tumour to irradiation prior to the treatment. Mathematical modelling of tumour control probability (TCP) based on the dose distribution, medical imaging and other biological information may help to improve this prediction and to optimize the treatment plan. The aim of this work is to develop an image based 3D multiscale radiobiological model, which describes the growth and the response to radiotherapy of hypoxic tumors. Methods: The computer model is based on voxels, containing tumour, normal (including capillary) and dead cells. Killing of tumour cells due to irradiation is calculatedmore » by the Linear Quadratic Model (extended for hypoxia), and the proliferation and resorption of cells are modelled by exponential laws. The initial shape of the tumours is taken from CT images and the initial vascular and cell density information from PET and/or MR images. Including the fractionation regime and the physical dose distribution of the radiation treatment, the model simulates the spatial-temporal evolution of the tumor. Additionally, the dose distribution may be biologically optimized. Results: The model describes the appearance of hypoxia during tumour growth and the reoxygenation processes during radiotherapy. Among other parameters, the TCP is calculated for different dose distributions. The results are in accordance with published results. Conclusion: The simulation model may contribute to the understanding of the influence of biological parameters on tumor response during treatment, and specifically on TCP. It may be used to implement dose-painting approaches. Experimental and clinical validation is needed. This study is supported by a grant from the Ministry of Education of Chile, Programa Mece Educacion Superior (2)« less

Development of a lumbar EMG-based coactivation index for the assessment of complex dynamic tasks.

PubMed

Le, Peter; Aurand, Alexander; Walter, Benjamin A; Best, Thomas M; Khan, Safdar N; Mendel, Ehud; Marras, William S

2018-03-01

The objective of this study was to develop and test an EMG-based coactivation index and compare it to a coactivation index defined by a biologically assisted lumbar spine model to differentiate between tasks. The purpose was to provide a universal approach to assess coactivation of a multi-muscle system when a computational model is not accessible. The EMG-based index developed utilised anthropometric-defined muscle characteristics driven by torso kinematics and EMG. Muscles were classified as agonists/antagonists based upon 'simulated' moments of the muscles relative to the total 'simulated' moment. Different tasks were used to test the range of the index including lifting, pushing and Valsalva. Results showed that the EMG-based index was comparable to the index defined by a biologically assisted model (r 2  = 0.78). Overall, the EMG-based index provides a universal, usable method to assess the neuromuscular effort associated with coactivation for complex dynamic tasks when the benefit of a biomechanical model is not available. Practitioner Summary: A universal coactivation index for the lumbar spine was developed to assess complex dynamic tasks. This method was validated relative to a model-based index for use when a high-end computational model is not available. Its simplicity allows for fewer inputs and usability for assessment of task ergonomics and rehabilitation.

PREDICTING SUBSURFACE CONTAMINANT TRANSPORT AND TRANSFORMATION: CONSIDERATIONS FOR MODEL SELECTION AND FIELD VALIDATION

EPA Science Inventory

Predicting subsurface contaminant transport and transformation requires mathematical models based on a variety of physical, chemical, and biological processes. The mathematical model is an attempt to quantitatively describe observed processes in order to permit systematic forecas...

Synchronization of metronomes

NASA Astrophysics Data System (ADS)

Pantaleone, James

2002-10-01

Synchronization is a common phenomenon in physical and biological systems. We examine the synchronization of two (and more) metronomes placed on a freely moving base. The small motion of the base couples the pendulums causing synchronization. The synchronization is generally in-phase, with antiphase synchronization occurring only under special conditions. The metronome system provides a mechanical realization of the popular Kuramoto model for synchronization of biological oscillators, and is excellent for classroom demonstrations and an undergraduate physics lab.

Automatic parameter estimation of multicompartmental neuron models via minimization of trace error with control adjustment.

PubMed

Brookings, Ted; Goeritz, Marie L; Marder, Eve

2014-11-01

We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. Copyright © 2014 the American Physiological Society.

Rethinking biology instruction: The application of DNR-based instruction to the learning and teaching of biology

NASA Astrophysics Data System (ADS)

Maskiewicz, April Lee

Educational studies report that secondary and college level students have developed only limited understandings of the most basic biological processes and their interrelationships from typical classroom experiences. Furthermore, students have developed undesirable reasoning schemes and beliefs that directly affect how they make sense of and account for biological phenomena. For these reasons, there exists a need to rethink instructional practices in biology. This dissertation discusses how the principles of Harel's (1998, 2001) DNR-based instruction in mathematics could be applied to the teaching and learning of biology. DNR is an acronym for the three foundational principles of the system: Duality, Necessity, and Repeated-reasoning. This study examines the application of these three principles to ecology instruction. Through clinical and teaching interviews, I developed models of students' existing ways of understanding in ecology and inferred their ways of thinking. From these models a hypothetical learning trajectory was developed for 16 college level freshmen enrolled in a 10-week ecology teaching experiment. Through cyclical, interpretive analysis I documented and analyzed the evolution of the participants' progress. The results provide empirical evidence to support the claim that the DNR principles are applicable to ecology instruction. With respect to the Duality Principle, helping students develop specific ways of understanding led to the development of model-based reasoning---a way of thinking and the cognitive objective guiding instruction. Through carefully structured problem solving tasks, the students developed a biological understanding of the relationship between matter cycling, energy flow, and cellular processes such as photosynthesis and respiration, and used this understanding to account for observable phenomena in nature. In the case of intellectual necessity, the results illuminate how problem situations can be developed for biology learners that create cognitive disequilibrium-equilibrium phases and thus lead to modification or refinement of existing schemes. Elements that contributed to creating intellectual need include (a) problem tasks that built on students' existing knowledge; (b) problem tasks that challenged students; (c) a routine in which students presented their group's solution to the class; and (d) the didactical contract (Brousseau, 1997) established in the classroom.

The neural optimal control hierarchy for motor control

NASA Astrophysics Data System (ADS)

DeWolf, T.; Eliasmith, C.

2011-10-01

Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.

Methods for open innovation on a genome-design platform associating scientific, commercial, and educational communities in synthetic biology.

PubMed

Toyoda, Tetsuro

2011-01-01

Synthetic biology requires both engineering efficiency and compliance with safety guidelines and ethics. Focusing on the rational construction of biological systems based on engineering principles, synthetic biology depends on a genome-design platform to explore the combinations of multiple biological components or BIO bricks for quickly producing innovative devices. This chapter explains the differences among various platform models and details a methodology for promoting open innovation within the scope of the statutory exemption of patent laws. The detailed platform adopts a centralized evaluation model (CEM), computer-aided design (CAD) bricks, and a freemium model. It is also important for the platform to support the legal aspects of copyrights as well as patent and safety guidelines because intellectual work including DNA sequences designed rationally by human intelligence is basically copyrightable. An informational platform with high traceability, transparency, auditability, and security is required for copyright proof, safety compliance, and incentive management for open innovation in synthetic biology. GenoCon, which we have organized and explained here, is a competition-styled, open-innovation method involving worldwide participants from scientific, commercial, and educational communities that aims to improve the designs of genomic sequences that confer a desired function on an organism. Using only a Web browser, a participating contributor proposes a design expressed with CAD bricks that generate a relevant DNA sequence, which is then experimentally and intensively evaluated by the GenoCon organizers. The CAD bricks that comprise programs and databases as a Semantic Web are developed, executed, shared, reused, and well stocked on the secure Semantic Web platform called the Scientists' Networking System or SciNetS/SciNeS, based on which a CEM research center for synthetic biology and open innovation should be established. Copyright © 2011 Elsevier Inc. All rights reserved.

Habitat classification modeling with incomplete data: Pushing the habitat envelope

USGS Publications Warehouse

Zarnetske, P.L.; Edwards, T.C.; Moisen, Gretchen G.

2007-01-01

Habitat classification models (HCMs) are invaluable tools for species conservation, land-use planning, reserve design, and metapopulation assessments, particularly at broad spatial scales. However, species occurrence data are often lacking and typically limited to presence points at broad scales. This lack of absence data precludes the use of many statistical techniques for HCMs. One option is to generate pseudo-absence points so that the many available statistical modeling tools can be used. Traditional techniques generate pseudoabsence points at random across broadly defined species ranges, often failing to include biological knowledge concerning the species-habitat relationship. We incorporated biological knowledge of the species-habitat relationship into pseudo-absence points by creating habitat envelopes that constrain the region from which points were randomly selected. We define a habitat envelope as an ecological representation of a species, or species feature's (e.g., nest) observed distribution (i.e., realized niche) based on a single attribute, or the spatial intersection of multiple attributes. We created HCMs for Northern Goshawk (Accipiter gentilis atricapillus) nest habitat during the breeding season across Utah forests with extant nest presence points and ecologically based pseudo-absence points using logistic regression. Predictor variables were derived from 30-m USDA Landfire and 250-m Forest Inventory and Analysis (FIA) map products. These habitat-envelope-based models were then compared to null envelope models which use traditional practices for generating pseudo-absences. Models were assessed for fit and predictive capability using metrics such as kappa, thresholdindependent receiver operating characteristic (ROC) plots, adjusted deviance (Dadj2), and cross-validation, and were also assessed for ecological relevance. For all cases, habitat envelope-based models outperformed null envelope models and were more ecologically relevant, suggesting that incorporating biological knowledge into pseudo-absence point generation is a powerful tool for species habitat assessments. Furthermore, given some a priori knowledge of the species-habitat relationship, ecologically based pseudo-absence points can be applied to any species, ecosystem, data resolution, and spatial extent. ?? 2007 by the Ecological Society of America.

Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells.

PubMed

Jang, Minjeong; Koh, Ilkyoo; Lee, Seok Jae; Cheong, Jae-Ho; Kim, Pilnam

2017-01-27

Gastric cancer (GC) is a common aggressive malignant tumor with high incidence and mortality worldwide. GC is classified into intestinal and diffuse types according to the histo-morphological features. Because of distinctly different clinico-pathological features, new cancer therapy strategies and in vitro preclinical models for the two pathological variants of GC is necessary. Since extracellular matrix (ECM) influence the biological behavior of tumor cells, we hypothesized that GC might be more similarly modeled in 3D with matrix rather than in 2D. Herein, we developed a microfluidic-based a three-dimensional (3D) in vitro gastric cancer model, with subsequent drug resistance assay. AGS (intestinal type) and Hs746T (diffuse type) gastric cancer cell lines were encapsulated in collagen beads with high cellular viability. AGS exhibited an aggregation pattern with expansive growth, whereas Hs746T showed single-cell-level infiltration. Importantly, in microtumor models, epithelial-mesenchymal transition (EMT) and metastatic genes were upregulated, whereas E-cadherin was downregulated. Expression of ß-catenin was decreased in drug-resistant cells, and chemosensitivity toward the anticancer drug (5-FU) was observed in microtumors. These results suggest that in vitro microtumor models may represent a biologically relevant platform for studying gastric cancer cell biology and tumorigenesis, and for accelerating the development of novel therapeutic targets.

A modeling approach to account for toxicokinetic interactions in the calculation of biological hazard index for chemical mixtures.

PubMed

Haddad, S; Tardif, R; Viau, C; Krishnan, K

1999-09-05

Biological hazard index (BHI) is defined as biological level tolerable for exposure to mixture, and is calculated by an equation similar to the conventional hazard index. The BHI calculation, at the present time, is advocated for use in situations where toxicokinetic interactions do not occur among mixture constituents. The objective of this study was to develop an approach for calculating interactions-based BHI for chemical mixtures. The approach consisted of simulating the concentration of exposure indicator in the biological matrix of choice (e.g. venous blood) for each component of the mixture to which workers are exposed and then comparing these to the established BEI values, for calculating the BHI. The simulation of biomarker concentrations was performed using a physiologically-based toxicokinetic (PBTK) model which accounted for the mechanism of interactions among all mixture components (e.g. competitive inhibition). The usefulness of the present approach is illustrated by calculating BHI for varying ambient concentrations of a mixture of three chemicals (toluene (5-40 ppm), m-xylene (10-50 ppm), and ethylbenzene (10-50 ppm)). The results show that the interactions-based BHI can be greater or smaller than that calculated on the basis of additivity principle, particularly at high exposure concentrations. At lower exposure concentrations (e.g. 20 ppm each of toluene, m-xylene and ethylbenzene), the BHI values obtained using the conventional methodology are similar to the interactions-based methodology, confirming that the consequences of competitive inhibition are negligible at lower concentrations. The advantage of the PBTK model-based methodology developed in this study relates to the fact that, the concentrations of individual chemicals in mixtures that will not result in a significant increase in the BHI (i.e. > 1) can be determined by iterative simulation.

A proposal for a computer-based framework of support for public health in the management of biological incidents: the Czech Republic experience.

PubMed

Bures, Vladimír; Otcenásková, Tereza; Cech, Pavel; Antos, Karel

2012-11-01

Biological incidents jeopardising public health require decision-making that consists of one dominant feature: complexity. Therefore, public health decision-makers necessitate appropriate support. Based on the analogy with business intelligence (BI) principles, the contextual analysis of the environment and available data resources, and conceptual modelling within systems and knowledge engineering, this paper proposes a general framework for computer-based decision support in the case of a biological incident. At the outset, the analysis of potential inputs to the framework is conducted and several resources such as demographic information, strategic documents, environmental characteristics, agent descriptors and surveillance systems are considered. Consequently, three prototypes were developed, tested and evaluated by a group of experts. Their selection was based on the overall framework scheme. Subsequently, an ontology prototype linked with an inference engine, multi-agent-based model focusing on the simulation of an environment, and expert-system prototypes were created. All prototypes proved to be utilisable support tools for decision-making in the field of public health. Nevertheless, the research revealed further issues and challenges that might be investigated by both public health focused researchers and practitioners.

Biomimetic cellular metals-using hierarchical structuring for energy absorption.

PubMed

Bührig-Polaczek, A; Fleck, C; Speck, T; Schüler, P; Fischer, S F; Caliaro, M; Thielen, M

2016-07-19

Fruit walls as well as nut and seed shells typically perform a multitude of functions. One of the biologically most important functions consists in the direct or indirect protection of the seeds from mechanical damage or other negative environmental influences. This qualifies such biological structures as role models for the development of new materials and components that protect commodities and/or persons from damage caused for example by impacts due to rough handling or crashes. We were able to show how the mechanical properties of metal foam based components can be improved by altering their structure on various hierarchical levels inspired by features and principles important for the impact and/or puncture resistance of the biological role models, rather than by tuning the properties of the bulk material. For this various investigation methods have been established which combine mechanical testing with different imaging methods, as well as with in situ and ex situ mechanical testing methods. Different structural hierarchies especially important for the mechanical deformation and failure behaviour of the biological role models, pomelo fruit (Citrus maxima) and Macadamia integrifolia, were identified. They were abstracted and transferred into corresponding structural principles and thus hierarchically structured bio-inspired metal foams have been designed. A production route for metal based bio-inspired structures by investment casting was successfully established. This allows the production of complex and reliable structures, by implementing and combining different hierarchical structural elements found in the biological concept generators, such as strut design and integration of fibres, as well as by minimising casting defects. To evaluate the structural effects, similar investigation methods and mechanical tests were applied to both the biological role models and the metallic foams. As a result an even deeper quantitative understanding of the form-structure-function relationship of the biological concept generators as well as the bio-inspired metal foams was achieved, on deeper hierarchical levels and overarching different levels.

Transient and sustained elementary flux mode networks on a catalytic string-based chemical evolution model.

PubMed

Pereira, José A

2014-08-01

Theoretical models designed to test the metabolism-first hypothesis for prebiotic evolution have yield strong indications about the hypothesis validity but could sometimes use a more extensive identification between model objects and real objects towards a more meaningful interpretation of results. In an attempt to go in that direction, the string-based model SSE ("steady state evolution") was developed, where abstract molecules (strings) and catalytic interaction rules are based on some of the most important features of carbon compounds in biological chemistry. The system is open with a random inflow and outflow of strings but also with a permanent string food source. Although specific catalysis is a key aspect of the model, used to define reaction rules, the focus is on energetics rather than kinetics. Standard energy change tables were constructed and used with standard formation reactions to track energy flows through the interpretation of equilibrium constant values. Detection of metabolic networks on the reaction system was done with elementary flux mode (EFM) analysis. The combination of these model design and analysis options enabled obtaining metabolic and catalytic networks showing several central features of biological metabolism, some more clearly than in previous models: metabolic networks with stepwise synthesis, energy coupling, catalysts regulation, SN2 coupling, redox coupling, intermediate cycling, coupled inverse pathways (metabolic cycling), autocatalytic cycles and catalytic cascades. The results strongly suggest that the main biological metabolism features, including the genotype-phenotype interpretation, are caused by the principles of catalytic systems and are prior to modern genetic systems principles. It also gives further theoretical support to the thesis that the basic features of biologic metabolism are a consequence of the time evolution of a random catalyst search working on an open system with a permanent food source. The importance of the food source characteristics and evolutionary possibilities are discussed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Developmental and Life-Stage Physiologically-Based Pharmacokinetic (PBPK) Models in Humans and Animal Models.

EPA Science Inventory

PBPK models provide a computational framework for incorporating pertinent physiological and biochemical information to estimate in vivo levels of xenobiotics in biological tissues. In general, PBPK models are used to correlate exposures to target tissue levels of chemicals and th...

Towards a 21st century roadmap for biomedical research and ...

EPA Pesticide Factsheets

Decades of costly failures in translating drug candidates from preclinical disease models to human therapeutic use warrant reconsideration of the priority placed on animal models in biomedical research. Following an international workshop attended by experts from academia, government institutions, research funding bodies and the corporate and NGO sectors, this consensus report analyses, as case studies, five disease areas with major unmet needs for new treatments. In view of the scientifically driven transition towards a human pathways-based paradigm in toxicology, a similar paradigm shift appears to be justified in biomedical research. There is a pressing need for an approach that strategically implements advanced, human biology-based models and tools to understand disease pathways at multiple biological scales. We present recommendations to help achieve this. To discover and develop new therapies, we need 21-century roadmaps for biomedical research based on multiscale human disease pathways, and supported by policy and funding strategies that prioritise human relevance.

A Chado case study: an ontology-based modular schema for representing genome-associated biological information.

PubMed

Mungall, Christopher J; Emmert, David B

2007-07-01

A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused. Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences. GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).

Costs of Providing Infusion Therapy for Rheumatoid Arthritis in a Hospital-based Infusion Center Setting.

PubMed

Schmier, Jordana; Ogden, Kristine; Nickman, Nancy; Halpern, Michael T; Cifaldi, Mary; Ganguli, Arijit; Bao, Yanjun; Garg, Vishvas

2017-08-01

Many hospital-based infusion centers treat patients with rheumatoid arthritis (RA) with intravenous biologic agents, yet may have a limited understanding of the overall costs of infusion in this setting. The purposes of this study were to conduct a microcosting analysis from a hospital perspective and to develop a model using an activity-based costing approach for estimating costs associated with the provision of hospital-based infusion services (preparation, administration, and follow-up) in the United States for maintenance treatment of moderate to severe RA. A spreadsheet-based model was developed. Inputs included hourly wages, time spent providing care, supply/overhead costs, laboratory testing, infusion center size, and practice pattern information. Base-case values were derived from data from surveys, published studies, standard cost sources, and expert opinion. Costs are presented in year-2017 US dollars. The base case modeled a hospital infusion center serving patients with RA treated with abatacept, tocilizumab, infliximab, or rituximab. Estimated overall costs of infusions per patient per year were $36,663 (rituximab), $36,821 (tocilizumab), $44,973 (infliximab), and $46,532 (abatacept). Of all therapies, the biologic agents represented the greatest share of overall costs, ranging from 87% to $91% of overall costs per year. Excluding infusion drug costs, labor accounted for 53% to 57% of infusion costs. Biologic agents represented the highest single cost associated with RA infusion care; however, personnel, supplies, and overhead costs also contributed substantially to overall costs (8%-16%). This model may provide a helpful and adaptable framework for use by hospitals in informing decision making about services offered and their associated financial implications. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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.

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

Swanson, K; Corwin, D; Rockne, R

Purpose: To demonstrate a method of generating patient-specific, biologically-guided radiation therapy (RT) plans and to quantify and predict response to RT in glioblastoma. We investigate the biological correlates and imaging physics driving T2-MRI based response to radiation therapy using an MRI simulator. Methods: We have integrated a patient-specific biomathematical model of glioblastoma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated RT optimization to construct individualized, biologically-guided plans. Patient-individualized simulations of the standard-of-care and optimized plans are compared in terms of several biological metrics quantified on MRI. An extension of the PI model is used to investigate themore » role of angiogenesis and its correlates in glioma response to therapy with the Proliferation-Invasion-Hypoxia- Necrosis-Angiogenesis model (PIHNA). The PIHNA model is used with a brain tissue phantom to predict tumor-induced vasogenic edema, tumor and tissue density that is used in a multi-compartmental MRI signal equation for generation of simulated T2- weighted MRIs. Results: Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized RT plans would have a significant impact on delaying tumor progression, with Days Gained increases from 21% to 105%. For the T2- MRI simulations, initial validation tests compared average simulated T2 values for white matter, tumor, and peripheral edema to values cited in the literature. Simulated results closely match the characteristic T2 value for each tissue. Conclusion: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for RT generated biologically-guided doses that decreased normal tissue dose and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Simulated T2-MRI is shown to be consistent with known physics of MRI and can be used to further investigate biological drivers of imaging-based response to RT.« less

Oyster larval transport in coastal Alabama: Dominance of physical transport over biological behavior in a shallow estuary

NASA Astrophysics Data System (ADS)

Kim, Choong-Ki; Park, Kyeong; Powers, Sean P.; Graham, William M.; Bayha, Keith M.

2010-10-01

Among the various factors affecting recruitment of marine invertebrates and fish, larval transport may produce spatial and temporal patterns of abundance that are important determinants of management strategies. Here we conducted a field and modeling study to investigate the larval transport of eastern oyster, Crassostrea virginica, in Mobile Bay and eastern Mississippi Sound, Alabama. A three-dimensional larval transport model accounting for physical transport, biological movement of larvae, and site- and larval-specific conditions was developed. A hydrodynamic model was used to simulate physical transport, and biological movement was parameterized as a function of swimming and sinking velocity of oyster larvae. Site- and larval-specific conditions, including spawning location, spawning stock size, spawning time, and larval period, were determined based on the previous studies. The model reasonably reproduced the observed gradient in oyster spat settlement and bivalve larval concentration, although the model results were less dynamic than the data, probably owing to the simplified biological conditions employed in the model. A persistent gradient decreasing from west to east in the model results at time scales of overall average, season, and each survey in 2006 suggests that the larval supply may be responsible for the corresponding gradient in oyster spat settlement observed over the past 40 years. Biological movement increased larval retention near the spawning area, thus providing a favorable condition for local recruitment of oysters. Inclusion of biological movement, however, caused little change in the overall patterns of larval transport and still resulted in a west-east gradient, presumably because of frequent destratification in the shallow Mobile Bay system.

RaptorX server: a resource for template-based protein structure modeling.

PubMed

Källberg, Morten; Margaryan, Gohar; Wang, Sheng; Ma, Jianzhu; Xu, Jinbo

2014-01-01

Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server ( http://raptorx.uchicago.edu ), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling.Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min.

Modular design of synthetic gene circuits with biological parts and pools.

PubMed

Marchisio, Mario Andrea

2015-01-01

Synthetic gene circuits can be designed in an electronic fashion by displaying their basic components-Standard Biological Parts and Pools of molecules-on the computer screen and connecting them with hypothetical wires. This procedure, achieved by our add-on for the software ProMoT, was successfully applied to bacterial circuits. Recently, we have extended this design-methodology to eukaryotic cells. Here, highly complex components such as promoters and Pools of mRNA contain hundreds of species and reactions whose calculation demands a rule-based modeling approach. We showed how to build such complex modules via the joint employment of the software BioNetGen (rule-based modeling) and ProMoT (modularization). In this chapter, we illustrate how to utilize our computational tool for synthetic biology with the in silico implementation of a simple eukaryotic gene circuit that performs the logic AND operation.

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

PubMed Central

Klein, Michael T; Hou, Gang; Quann, Richard J; Wei, Wei; Liao, Kai H; Yang, Raymond S H; Campain, Julie A; Mazurek, Monica A; Broadbelt, Linda J

2002-01-01

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun. We propose to expand the horizons on classic pharmacokinetics and physiologically based pharmacokinetics (PBPK), where human or animal bodies were often described by a few compartments, by integrating PBPK with reaction network modeling described in this article. If one draws a parallel between an oil refinery, where the application of this modeling approach has been very successful, and a human body, the individual processing units in the oil refinery may be considered equivalent to the vital organs of the human body. Even though the cell or organ may be much more complicated, the complex biochemical reaction networks in each organ may be similarly modeled and linked in much the same way as the modeling of the entire oil refinery through linkage of the individual processing units. The integrated chemical engineering software package described in this article, BioMOL, denotes the biological application of molecular-oriented lumping. BioMOL can build a detailed model in 1-1,000 CPU sec using standard desktop hardware. The models solve and optimize using standard and widely available hardware and software and can be presented in the context of a user-friendly interface. We believe this is an engineering tool with great promise in its application to complex biological reaction networks. PMID:12634134

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.

Adaptive optical microscope for brain imaging in vivo

NASA Astrophysics Data System (ADS)

Wang, Kai

2017-04-01

The optical heterogeneity of biological tissue imposes a major limitation to acquire detailed structural and functional information deep in the biological specimens using conventional microscopes. To restore optimal imaging performance, we developed an adaptive optical microscope based on direct wavefront sensing technique. This microscope can reliably measure and correct biological samples induced aberration. We demonstrated its performance and application in structural and functional brain imaging in various animal models, including fruit fly, zebrafish and mouse.

Reconstructing Anaximander's biological model unveils a theory of evolution akin to Darwin's, though centuries before the birth of science.

PubMed

Trevisanato, Siro Igino

2016-08-01

Anaximander's fragments on biology report a theory of evolution, which, unlike the development of other biological systems in the ancient Aegean, is naturalistic and is not based on metaphysics. According to Anaximander, evolution affected all living beings, including humans. The first biological systems formed in an aquatic environment, and were encased in a rugged and robust envelope. Evolution progressed with modifications that enabled the formation of more dynamic biological systems. For instance, after reaching land, the robust armors around aquatic beings dried up, and became brittle, This led to the loss of the armor and the development of more mobile life forms. Anaximander's theory combines observations of animals with speculations, and as such mirrors the more famous theory of evolution by Charles Darwin expressed 24 centuries later. The poor reception received by Anaximander's model in his time, illustrates a zeitgeist that would explain the contemporary lag phase in the development of biology and, as a result, medicine, in the ancient western world.

Does a prosocial-selfish distinction help explain the biological affects? Comment on Buck (1999).

PubMed

Gray, Jeremy R

2002-10-01

R. Buck (1999) argued that a conceptual distinction between prosocial and selfish motivation is necessary to understand the biological affects (consciously experienced feelings and desires having an innate neurochemical basis). However, at a biological level of analysis, a prosocial-selfish distinction is doubtful empirically and conceptually. For this reason, Buck's proposed typology of biological affects is unclear. Moreover, a prosocial-selfish distinction is not necessary to explain hemispheric differences in brain activity associated with affect. In contrast, an approach-withdrawal distinction explains some data uniquely well, although numerous exceptions imply that simple models are inadequate. To extend hemispheric models of experienced emotion, a prosocial-selfish distinction is unlikely to be explanatory, whereas an alternative account based on a distinction between verbal and nonverbal working memory may be useful.

A model of proto-object based saliency

PubMed Central

Russell, Alexander F.; Mihalaş, Stefan; von der Heydt, Rudiger; Niebur, Ernst; Etienne-Cummings, Ralph

2013-01-01

Organisms use the process of selective attention to optimally allocate their computational resources to the instantaneously most relevant subsets of a visual scene, ensuring that they can parse the scene in real time. Many models of bottom-up attentional selection assume that elementary image features, like intensity, color and orientation, attract attention. Gestalt psychologists, how-ever, argue that humans perceive whole objects before they analyze individual features. This is supported by recent psychophysical studies that show that objects predict eye-fixations better than features. In this report we present a neurally inspired algorithm of object based, bottom-up attention. The model rivals the performance of state of the art non-biologically plausible feature based algorithms (and outperforms biologically plausible feature based algorithms) in its ability to predict perceptual saliency (eye fixations and subjective interest points) in natural scenes. The model achieves this by computing saliency as a function of proto-objects that establish the perceptual organization of the scene. All computational mechanisms of the algorithm have direct neural correlates, and our results provide evidence for the interface theory of attention. PMID:24184601

Convex reformulation of biologically-based multi-criteria intensity-modulated radiation therapy optimization including fractionation effects

NASA Astrophysics Data System (ADS)

Hoffmann, Aswin L.; den Hertog, Dick; Siem, Alex Y. D.; Kaanders, Johannes H. A. M.; Huizenga, Henk

2008-11-01

Finding fluence maps for intensity-modulated radiation therapy (IMRT) can be formulated as a multi-criteria optimization problem for which Pareto optimal treatment plans exist. To account for the dose-per-fraction effect of fractionated IMRT, it is desirable to exploit radiobiological treatment plan evaluation criteria based on the linear-quadratic (LQ) cell survival model as a means to balance the radiation benefits and risks in terms of biologic response. Unfortunately, the LQ-model-based radiobiological criteria are nonconvex functions, which make the optimization problem hard to solve. We apply the framework proposed by Romeijn et al (2004 Phys. Med. Biol. 49 1991-2013) to find transformations of LQ-model-based radiobiological functions and establish conditions under which transformed functions result in equivalent convex criteria that do not change the set of Pareto optimal treatment plans. The functions analysed are: the LQ-Poisson-based model for tumour control probability (TCP) with and without inter-patient heterogeneity in radiation sensitivity, the LQ-Poisson-based relative seriality s-model for normal tissue complication probability (NTCP), the equivalent uniform dose (EUD) under the LQ-Poisson model and the fractionation-corrected Probit-based model for NTCP according to Lyman, Kutcher and Burman. These functions differ from those analysed before in that they cannot be decomposed into elementary EUD or generalized-EUD functions. In addition, we show that applying increasing and concave transformations to the convexified functions is beneficial for the piecewise approximation of the Pareto efficient frontier.

Effect of a care plan based on Roy adaptation model biological dimension on stroke patients' physiologic adaptation level.

PubMed

Alimohammadi, Nasrollah; Maleki, Bibi; Shahriari, Mohsen; Chitsaz, Ahmad

2015-01-01

Stroke is a stressful event with several functional, physical, psychological, social, and economic problems that affect individuals' different living balances. With coping strategies, patients try to control these problems and return to their natural life. The aim of this study is to investigate the effect of a care plan based on Roy adaptation model biological dimension on stroke patients' physiologic adaptation level. This study is a clinical trial in which 50 patients, affected by brain stroke and being admitted in the neurology ward of Kashani and Alzahra hospitals, were randomly assigned to control and study groups in Isfahan in 2013. Roy adaptation model care plan was administered in biological dimension in the form of four sessions and phone call follow-ups for 1 month. The forms related to Roy adaptation model were completed before and after intervention in the two groups. Chi-square test and t-test were used to analyze the data through SPSS 18. There was a significant difference in mean score of adaptation in physiological dimension in the study group after intervention (P < 0.001) compared to before intervention. Comparison of the mean scores of changes of adaptation in the patients affected by brain stroke in the study and control groups showed a significant increase in physiological dimension in the study group by 47.30 after intervention (P < 0.001). The results of study showed that Roy adaptation model biological dimension care plan can result in an increase in adaptation in patients with stroke in physiological dimension. Nurses can use this model for increasing patients' adaptation.

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.

Interacting domain-specific languages with biological problem solving environments

NASA Astrophysics Data System (ADS)

Cickovski, Trevor M.

Iteratively developing a biological model and verifying results with lab observations has become standard practice in computational biology. This process is currently facilitated by biological Problem Solving Environments (PSEs), multi-tiered and modular software frameworks which traditionally consist of two layers: a computational layer written in a high level language using design patterns, and a user interface layer which hides its details. Although PSEs have proven effective, they still enforce some communication overhead between biologists refining their models through repeated comparison with experimental observations in vitro or in vivo, and programmers actually implementing model extensions and modifications within the computational layer. I illustrate the use of biological Domain-Specific Languages (DSLs) as a middle-level PSE tier to ameliorate this problem by providing experimentalists with the ability to iteratively test and develop their models using a higher degree of expressive power compared to a graphical interface, while saving the requirement of general purpose programming knowledge. I develop two radically different biological DSLs: XML-based BIOLOGO will model biological morphogenesis using a cell-centered stochastic cellular automaton and translate into C++ modules for an object-oriented PSE C OMPUCELL3D, and MDLab will provide a set of high-level Python libraries for running molecular dynamics simulations, using wrapped functionality from the C++ PSE PROTOMOL. I describe each language in detail, including its its roles within the larger PSE and its expressibility in terms of representable phenomena, and a discussion of observations from users of the languages. Moreover I will use these studies to draw general conclusions about biological DSL development, including dependencies upon the goals of the corresponding PSE, strategies, and tradeoffs.

Biological Networks for Cancer Candidate Biomarkers Discovery

PubMed Central

Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

2016-01-01

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

Population-expression models of immune response

NASA Astrophysics Data System (ADS)

Stromberg, Sean P.; Antia, Rustom; Nemenman, Ilya

2013-06-01

The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable.

Dose addition models based on biologically-relevant reductions in fetal testosterone accurately predict postnatal reproductive tract alterations by a phthalate mixture in rats

EPA Science Inventory

Challenges in cumulative risk assessment of anti-androgenic phthalate mixtures include a lack of data on all the individual phthalates and difficulty determining the biological relevance of reduction in fetal testosterone (T) on postnatal development. The objectives of the curren...

A Multi-Level Model of Moral Thinking Based on Neuroscience and Moral Psychology

ERIC Educational Resources Information Center

Jeong, Changwoo; Han, Hye Min

2011-01-01

Developments in neurobiology are providing new insights into the biological and physical features of human thinking, and brain-activation imaging methods such as functional magnetic resonance imaging have become the most dominant research techniques to approach the biological part of thinking. With the aid of neurobiology, there also have been…

The pediatric sepsis biomarker risk model: potential implications for sepsis therapy and biology.

PubMed

Alder, Matthew N; Lindsell, Christopher J; Wong, Hector R

2014-07-01

Sepsis remains a major cause of morbidity and mortality in adult and pediatric intensive care units. Heterogeneity of demographics, comorbidities, biological mechanisms, and severity of illness leads to difficulty in determining which patients are at highest risk of mortality. Determining mortality risk is important for weighing the potential benefits of more aggressive interventions and for deciding whom to enroll in clinical trials. Biomarkers can be used to parse patients into different risk categories and can outperform current methods of patient risk stratification based on physiologic parameters. Here we review the Pediatric Sepsis Biomarker Risk Model that has also been modified and applied to estimate mortality risk in adult patients. We compare the two models and speculate on the biological implications of the biomarkers in patients with sepsis.

APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.

PubMed

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

APG: an Active Protein-Gene Network Model to Quantify Regulatory Signals in Complex Biological Systems

PubMed Central

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information. PMID:23346354

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends.

PubMed

Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J

2017-07-01

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.

Coupling of Coastal Zone Color Scanner data to a physical-biological model of the southeastern U.S. continental shelf ecosystem. I - CZCS data description and Lagrangian particle tracing experiments. II - An Eulerian model. III - Nutrient and phytoplankton fluxes and CZCS data assimilation

NASA Technical Reports Server (NTRS)

Ishizaka, Joji

1990-01-01

Surface phytoplankton biomass of the southeastern U.S. continental shelf area is discussed based on coastal zone color scanner (CZCS) images obtained in April 1980. Data of chlorophyll distributions are analyzed in conjunction with concurrent flow and temperature fields. Lagrangian particle tracing experiments show that the particles move consistently with the evolution of the chlorophyll patterns. A four-component physical-biological model for a horizontal plane at a nominal depth of 17 m is presented. Model simulations using various physical-biological dynamics and boundary conditions show that the variability of chlorophyll distributions is controlled by horizontal advection. Phytoplankton and nutrient fluxes, calculated using the model, show considerable variability with time. The chlorophyll distributions obtained from the CZCS images are assimilated into the model to improve the phytoplankton flux estimates.

The semiotics of control and modeling relations in complex systems.

PubMed

Joslyn, C

2001-01-01

We provide a conceptual analysis of ideas and principles from the systems theory discourse which underlie Pattee's semantic or semiotic closure, which is itself foundational for a school of theoretical biology derived from systems theory and cybernetics, and is now being related to biological semiotics and explicated in the relational biological school of Rashevsky and Rosen. Atomic control systems and models are described as the canonical forms of semiotic organization, sharing measurement relations, but differing topologically in that control systems are circularly and models linearly related to their environments. Computation in control systems is introduced, motivating hierarchical decomposition, hybrid modeling and control systems, and anticipatory or model-based control. The semiotic relations in complex control systems are described in terms of relational constraints, and rules and laws are distinguished as contingent and necessary functional entailments, respectively. Finally, selection as a meta-level of constraint is introduced as the necessary condition for semantic relations in control systems and models.

Agent-Based Modeling in Systems Pharmacology.

PubMed

Cosgrove, J; Butler, J; Alden, K; Read, M; Kumar, V; Cucurull-Sanchez, L; Timmis, J; Coles, M

2015-11-01

Modeling and simulation (M&S) techniques provide a platform for knowledge integration and hypothesis testing to gain insights into biological systems that would not be possible a priori. Agent-based modeling (ABM) is an M&S technique that focuses on describing individual components rather than homogenous populations. This tutorial introduces ABM to systems pharmacologists, using relevant case studies to highlight how ABM-specific strengths have yielded success in the area of preclinical mechanistic modeling.

An in vitro approach for lipolysis measurement using high-resolution mass spectrometry and partial least squares based analysis.

PubMed

Chang, Wen-Qi; Zhou, Jian-Liang; Li, Yi; Shi, Zi-Qi; Wang, Li; Yang, Jie; Li, Ping; Liu, Li-Fang; Xin, Gui-Zhong

2017-01-15

The elevation of free fatty acids (FFAs) has been regarded as a universal metabolic signature of excessive adipocyte lipolysis. Nowadays, in vitro lipolysis assay is generally essential for drug screening prior to the animal study. Here, we present a novel in vitro approach for lipolysis measurement combining UHPLC-Orbitrap and partial least squares (PLS) based analysis. Firstly, the calibration matrix was constructed by serial proportions of mixed samples (blended with control and model samples). Then, lipidome profiling was performed by UHPLC-Orbitrap, and 403 variables were extracted and aligned as dataset. Owing to the high resolution of Orbitrap analyzer and open source lipid identification software, 28 FFAs were further screened and identified. Based on the relative intensity of the screened FFAs, PLS regression model was constructed for lipolysis measurement. After leave-one-out cross-validation, ten principal components have been designated to build the final PLS model with excellent performances (RMSECV, 0.0268; RMSEC, 0.0173; R 2 , 0.9977). In addition, the high predictive accuracy (R 2  = 0.9907 and RMSEP = 0.0345) of the trained PLS model was also demonstrated using test samples. Finally, taking curcumin as a model compound, its antilipolytic effect on palmitic acid-induced lipolysis was successfully predicted as 31.78% by the proposed approach. Besides, supplementary evidences of curcumin induced modification in FFAs compositions as well as lipidome were given by PLS extended methods. Different from general biological assays, high resolution MS-based method provide more sophisticated information included in biological events. Thus, the novel biological evaluation model proposed here showed promising perspectives for drug evaluation or disease diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

A biologically based model for the integration of sensory-motor contingencies in rules and plans: a prefrontal cortex based extension of the Distributed Adaptive Control architecture.

PubMed

Duff, Armin; Fibla, Marti Sanchez; Verschure, Paul F M J

2011-06-30

Intelligence depends on the ability of the brain to acquire and apply rules and representations. At the neuronal level these properties have been shown to critically depend on the prefrontal cortex. Here we present, in the context of the Distributed Adaptive Control architecture (DAC), a biologically based model for flexible control and planning based on key physiological properties of the prefrontal cortex, i.e. reward modulated sustained activity and plasticity of lateral connectivity. We test the model in a series of pertinent tasks, including multiple T-mazes and the Tower of London that are standard experimental tasks to assess flexible control and planning. We show that the model is both able to acquire and express rules that capture the properties of the task and to quickly adapt to changes. Further, we demonstrate that this biomimetic self-contained cognitive architecture generalizes to planning. In addition, we analyze the extended DAC architecture, called DAC 6, as a model that can be applied for the creation of intelligent and psychologically believable synthetic agents. Copyright © 2010 Elsevier Inc. All rights reserved.

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints

NASA Astrophysics Data System (ADS)

Polster, Lisa; Schuemann, Jan; Rinaldi, Ilaria; Burigo, Lucas; McNamara, Aimee L.; Stewart, Robert D.; Attili, Andrea; Carlson, David J.; Sato, Tatsuhiko; Ramos Méndez, José; Faddegon, Bruce; Perl, Joseph; Paganetti, Harald

2015-07-01

The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints

PubMed Central

Polster, Lisa; Schuemann, Jan; Rinaldi, Ilaria; Burigo, Lucas; McNamara, Aimee L.; Stewart, Robert D.; Attili, Andrea; Carlson, David J.; Sato, Tatsuhiko; Méndez, José Ramos; Faddegon, Bruce; Perl, Joseph; Paganetti, Harald

2015-01-01

The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer (LET), while the others are based on DNA Double Strand Break (DSB) induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models. PMID:26061666

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

Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors

PubMed Central

Ren, Jiaping; Wang, Xinjie; Manocha, Dinesh

2016-01-01

We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real-world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses. PMID:27187068

Synthetic biology, inspired by synthetic chemistry.

PubMed

Malinova, V; Nallani, M; Meier, W P; Sinner, E K

2012-07-16

The topic synthetic biology appears still as an 'empty basket to be filled'. However, there is already plenty of claims and visions, as well as convincing research strategies about the theme of synthetic biology. First of all, synthetic biology seems to be about the engineering of biology - about bottom-up and top-down approaches, compromising complexity versus stability of artificial architectures, relevant in biology. Synthetic biology accounts for heterogeneous approaches towards minimal and even artificial life, the engineering of biochemical pathways on the organismic level, the modelling of molecular processes and finally, the combination of synthetic with nature-derived materials and architectural concepts, such as a cellular membrane. Still, synthetic biology is a discipline, which embraces interdisciplinary attempts in order to have a profound, scientific base to enable the re-design of nature and to compose architectures and processes with man-made matter. We like to give an overview about the developments in the field of synthetic biology, regarding polymer-based analogs of cellular membranes and what questions can be answered by applying synthetic polymer science towards the smallest unit in life, namely a cell. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Penrose-Hameroff orchestrated objective-reduction proposal for human consciousness is not biologically feasible.

PubMed

McKemmish, Laura K; Reimers, Jeffrey R; McKenzie, Ross H; Mark, Alan E; Hush, Noel S

2009-08-01

Penrose and Hameroff have argued that the conventional models of a brain function based on neural networks alone cannot account for human consciousness, claiming that quantum-computation elements are also required. Specifically, in their Orchestrated Objective Reduction (Orch OR) model [R. Penrose and S. R. Hameroff, J. Conscious. Stud. 2, 99 (1995)], it is postulated that microtubules act as quantum processing units, with individual tubulin dimers forming the computational elements. This model requires that the tubulin is able to switch between alternative conformational states in a coherent manner, and that this process be rapid on the physiological time scale. Here, the biological feasibility of the Orch OR proposal is examined in light of recent experimental studies on microtubule assembly and dynamics. It is shown that the tubulins do not possess essential properties required for the Orch OR proposal, as originally proposed, to hold. Further, we consider also recent progress in the understanding of the long-lived coherent motions in biological systems, a feature critical to Orch OR, and show that no reformation of the proposal based on known physical paradigms could lead to quantum computing within microtubules. Hence, the Orch OR model is not a feasible explanation of the origin of consciousness.

Essential Oils from Thyme (Thymus vulgaris): Chemical Composition and Biological Effects in Mouse Model.

PubMed

Vetvicka, Vaclav; Vetvickova, Jana

2016-12-01

Thymus species are popular spices and contain volatile oils as main chemical constituents. Recently, plant-derived essential oils are gaining significant attention due to their significant biological activities. Seven different thymus-derived essential oils were compared in our study. First, we focused on their chemical composition, which was followed up by testing their effects on phagocytosis, cytokine production, chemotaxis, edema inhibition, and liver protection. We found limited biological activities among tested oils, with no correlation between composition and biological effects. Similarly, no oils were effective in every reaction. Based on our data, the tested biological use of these essential oils is questionable.

A Bayesian network approach for modeling local failure in lung cancer

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Craft, Jeffrey; Lozi, Rawan Al; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O.; Bradley, Jeffrey D.; El Naqa, Issam

2011-03-01

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

From Random Walks to Brownian Motion, from Diffusion to Entropy: Statistical Principles in Introductory Physics

NASA Astrophysics Data System (ADS)

Reeves, Mark

2014-03-01

Entropy changes underlie the physics that dominates biological interactions. Indeed, introductory biology courses often begin with an exploration of the qualities of water that are important to living systems. However, one idea that is not explicitly addressed in most introductory physics or biology textbooks is dominant contribution of the entropy in driving important biological processes towards equilibrium. From diffusion to cell-membrane formation, to electrostatic binding in protein folding, to the functioning of nerve cells, entropic effects often act to counterbalance deterministic forces such as electrostatic attraction and in so doing, allow for effective molecular signaling. A small group of biology, biophysics and computer science faculty have worked together for the past five years to develop curricular modules (based on SCALEUP pedagogy) that enable students to create models of stochastic and deterministic processes. Our students are first-year engineering and science students in the calculus-based physics course and they are not expected to know biology beyond the high-school level. In our class, they learn to reduce seemingly complex biological processes and structures to be described by tractable models that include deterministic processes and simple probabilistic inference. The students test these models in simulations and in laboratory experiments that are biologically relevant. The students are challenged to bridge the gap between statistical parameterization of their data (mean and standard deviation) and simple model-building by inference. This allows the students to quantitatively describe realistic cellular processes such as diffusion, ionic transport, and ligand-receptor binding. Moreover, the students confront ``random'' forces and traditional forces in problems, simulations, and in laboratory exploration throughout the year-long course as they move from traditional kinematics through thermodynamics to electrostatic interactions. This talk will present a number of these exercises, with particular focus on the hands-on experiments done by the students, and will give examples of the tangible material that our students work with throughout the two-semester sequence of their course on introductory physics with a bio focus. Supported by NSF DUE.

Challenges and Rewards on the Road to Translational Systems Biology in Acute Illness: Four Case Reports from Interdisciplinary Teams

PubMed Central

An, Gary; Hunt, C. Anthony; Clermont, Gilles; Neugebauer, Edmund; Vodovotz, Yoram

2007-01-01

Introduction Translational systems biology approaches can be distinguished from mainstream systems biology in that their goal is to drive novel therapies and streamline clinical trials in critical illness. One systems biology approach, dynamic mathematical modeling (DMM), is increasingly used in dealing with the complexity of the inflammatory response and organ dysfunction. The use of DMM often requires a broadening of research methods and a multidisciplinary team approach that includes bioscientists, mathematicians, engineers, and computer scientists. However, the development of these groups must overcome domain-specific barriers to communication and understanding. Methods We present four case studies of successful translational, interdisciplinary systems biology efforts, which differ by organizational level from an individual to an entire research community. Results Case 1 is a single investigator involved in DMM of the acute inflammatory response at Cook County Hospital, in which extensive translational progress was made using agent-based models of inflammation and organ damage. Case 2 is a community-level effort from the University of Witten-Herdecke in Cologne, whose efforts have led to the formation of the Society for Complexity in Acute Illness. Case 3 is an institution-based group, the Biosystems Group at the University of California, San Francisco, whose work has included a focus on a common lexicon for DMM. Case 4 is an institution-based, trans-disciplinary research group (the Center for Inflammation and Regenerative Modeling at the University of Pittsburgh, whose modeling work has led to internal education efforts, grant support, and commercialization. Conclusion A transdisciplinary approach, which involves team interaction in an iterative fashion to address ambiguity and is supported by educational initiatives, is likely to be necessary for DMM in acute illness. Community-wide organizations such as the Society of Complexity in Acute Illness (SCAI) must strive to facilitate the implementation of DMM in sepsis/trauma research into the research community as a whole. PMID:17548029

High Throughput Exposure Prioritization of Chemicals Using a Screening-Level Probabilistic SHEDS-Lite Exposure Model

EPA Science Inventory

These novel modeling approaches for screening, evaluating and classifying chemicals based on the potential for biologically-relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. The new modeling approach is derived from the Stocha...

Kinetic Model of Growth of Arthropoda Populations

NASA Astrophysics Data System (ADS)

Ershov, Yu. A.; Kuznetsov, M. A.

2018-05-01

Kinetic equations were derived for calculating the growth of crustacean populations ( Crustacea) based on the biological growth model suggested earlier using shrimp ( Caridea) populations as an example. The development cycle of successive stages for populations can be represented in the form of quasi-chemical equations. The kinetic equations that describe the development cycle of crustaceans allow quantitative prediction of the development of populations depending on conditions. In contrast to extrapolation-simulation models, in the developed kinetic model of biological growth the kinetic parameters are the experimental characteristics of population growth. Verification and parametric identification of the developed model on the basis of the experimental data showed agreement with experiment within the error of the measurement technique.

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

DOT2: Macromolecular Docking With Improved Biophysical Models

PubMed Central

Roberts, Victoria A.; Thompson, Elaine E.; Pique, Michael E.; Perez, Martin S.; Eyck, Lynn Ten

2015-01-01

Computational docking is a useful tool for predicting macromolecular complexes, which are often difficult to determine experimentally. Here we present the DOT2 software suite, an updated version of the DOT intermolecular docking program. DOT2 provides straightforward, automated construction of improved biophysical models based on molecular coordinates, offering checkpoints that guide the user to include critical features. DOT has been updated to run more quickly, allow flexibility in grid size and spacing, and generate a complete list of favorable candidate configu-rations. Output can be filtered by experimental data and rescored by the sum of electrostatic and atomic desolvation energies. We show that this rescoring method improves the ranking of correct complexes for a wide range of macromolecular interactions, and demonstrate that biologically relevant models are essential for biologically relevant results. The flexibility and versatility of DOT2 accommodate realistic models of complex biological systems, improving the likelihood of a successful docking outcome. PMID:23695987

Adaptive invasive species distribution models: A framework for modeling incipient invasions

USGS Publications Warehouse

Uden, Daniel R.; Allen, Craig R.; Angeler, David G.; Corral, Lucia; Fricke, Kent A.

2015-01-01

The utilization of species distribution model(s) (SDM) for approximating, explaining, and predicting changes in species’ geographic locations is increasingly promoted for proactive ecological management. Although frameworks for modeling non-invasive species distributions are relatively well developed, their counterparts for invasive species—which may not be at equilibrium within recipient environments and often exhibit rapid transformations—are lacking. Additionally, adaptive ecological management strategies address the causes and effects of biological invasions and other complex issues in social-ecological systems. We conducted a review of biological invasions, species distribution models, and adaptive practices in ecological management, and developed a framework for adaptive, niche-based, invasive species distribution model (iSDM) development and utilization. This iterative, 10-step framework promotes consistency and transparency in iSDM development, allows for changes in invasive drivers and filters, integrates mechanistic and correlative modeling techniques, balances the avoidance of type 1 and type 2 errors in predictions, encourages the linking of monitoring and management actions, and facilitates incremental improvements in models and management across space, time, and institutional boundaries. These improvements are useful for advancing coordinated invasive species modeling, management and monitoring from local scales to the regional, continental and global scales at which biological invasions occur and harm native ecosystems and economies, as well as for anticipating and responding to biological invasions under continuing global change.

Predicting Lactational and Early Post-Weaning Exposures in Rats Using Biologically Based Pharmacokinetic Modeling

EPA Science Inventory

Risk and safety assessments for early life exposures to environmental chemicals or pharmaceuticals based on cross-species extrapolation would greatly benefit from information on chemical dosimetry in the young.

GUI to Facilitate Research on Biological Damage from Radiation

NASA Technical Reports Server (NTRS)

Cucinotta, Frances A.; Ponomarev, Artem Lvovich

2010-01-01

A graphical-user-interface (GUI) computer program has been developed to facilitate research on the damage caused by highly energetic particles and photons impinging on living organisms. The program brings together, into one computational workspace, computer codes that have been developed over the years, plus codes that will be developed during the foreseeable future, to address diverse aspects of radiation damage. These include codes that implement radiation-track models, codes for biophysical models of breakage of deoxyribonucleic acid (DNA) by radiation, pattern-recognition programs for extracting quantitative information from biological assays, and image-processing programs that aid visualization of DNA breaks. The radiation-track models are based on transport models of interactions of radiation with matter and solution of the Boltzmann transport equation by use of both theoretical and numerical models. The biophysical models of breakage of DNA by radiation include biopolymer coarse-grained and atomistic models of DNA, stochastic- process models of deposition of energy, and Markov-based probabilistic models of placement of double-strand breaks in DNA. The program is designed for use in the NT, 95, 98, 2000, ME, and XP variants of the Windows operating system.

MANAGING EXPOSURES TO NEUROTOXIC AIR POLLUTANTS.

EPA Science Inventory

Researchers at EPA's National Health and Environmental Effects Research Laboratory are developing a biologically-based dose-response model to describe the neurotoxic effects of exposure to volatile organic compounds (VOCs). The model is being developed to improve risk assessment...

QUANTITATIVE PROCEDURES FOR NEUROTOXICOLOGY RISK ASSESSMENT

EPA Science Inventory

In this project, previously published information on biologically based dose-response model for brain development was used to quantitatively evaluate critical neurodevelopmental processes, and to assess potential chemical impacts on early brain development. This model has been ex...

Discovering the Power of Individual-Based Modelling in Teaching and Learning: The Study of a Predator-Prey System

NASA Astrophysics Data System (ADS)

Ginovart, Marta

2014-08-01

The general aim is to promote the use of individual-based models (biological agent-based models) in teaching and learning contexts in life sciences and to make their progressive incorporation into academic curricula easier, complementing other existing modelling strategies more frequently used in the classroom. Modelling activities for the study of a predator-prey system for a mathematics classroom in the first year of an undergraduate program in biosystems engineering have been designed and implemented. These activities were designed to put two modelling approaches side by side, an individual-based model and a set of ordinary differential equations. In order to organize and display this, a system with wolves and sheep in a confined domain was considered and studied. With the teaching material elaborated and a computer to perform the numerical resolutions involved and the corresponding individual-based simulations, the students answered questions and completed exercises to achieve the learning goals set. Students' responses regarding the modelling of biological systems and these two distinct methodologies applied to the study of a predator-prey system were collected via questionnaires, open-ended queries and face-to-face dialogues. Taking into account the positive responses of the students when they were doing these activities, it was clear that using a discrete individual-based model to deal with a predator-prey system jointly with a set of ordinary differential equations enriches the understanding of the modelling process, adds new insights and opens novel perspectives of what can be done with computational models versus other models. The complementary views given by the two modelling approaches were very well assessed by students.

A rigorous approach to investigating common assumptions about disease transmission: Process algebra as an emerging modelling methodology for epidemiology.

PubMed

McCaig, Chris; Begon, Mike; Norman, Rachel; Shankland, Carron

2011-03-01

Changing scale, for example, the ability to move seamlessly from an individual-based model to a population-based model, is an important problem in many fields. In this paper, we introduce process algebra as a novel solution to this problem in the context of models of infectious disease spread. Process algebra allows us to describe a system in terms of the stochastic behaviour of individuals, and is a technique from computer science. We review the use of process algebra in biological systems, and the variety of quantitative and qualitative analysis techniques available. The analysis illustrated here solves the changing scale problem: from the individual behaviour we can rigorously derive equations to describe the mean behaviour of the system at the level of the population. The biological problem investigated is the transmission of infection, and how this relates to individual interactions.

A Toda lattice model of DNA

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

Christiansen, P.L.; Scott, A.C.; Muto, V.

In recent years the possibility that anharmonic excitations could play a role in the dynamics of SNA has been considered by several authors. It has been suggested that solitons may be generated thermally at biological temperatures. The denaturation of the DNA double helix has been investigated by statistical mechanics methods and by dynamical simulations. Here the potential for the hydrogen bond in each base pair is approximated by a Morse potential. In the present paper we describe the Toda lattice model of DNA. Temperature enters via the initial conditions and through a perturbation of the dynamical equations. The model ismore » refined by introduction of transversal motion of the Toda lattice and by transversal coupling of two lattices in the hydrogen bonds present in the base pairs. Using Lennard-Jones potentials to model these bonds we are able to obtain results concerning the open states of DNA at biological temperatures. 39 refs., 7 figs.« less

Quantification of 2D elemental distribution maps of intermediate-thick biological sections by low energy synchrotron μ-X-ray fluorescence spectrometry

NASA Astrophysics Data System (ADS)

Kump, P.; Vogel-Mikuš, K.

2018-05-01

Two fundamental-parameter (FP) based models for quantification of 2D elemental distribution maps of intermediate-thick biological samples by synchrotron low energy μ-X-ray fluorescence spectrometry (SR-μ-XRF) are presented and applied to the elemental analysis in experiments with monochromatic focused photon beam excitation at two low energy X-ray fluorescence beamlines—TwinMic, Elettra Sincrotrone Trieste, Italy, and ID21, ESRF, Grenoble, France. The models assume intermediate-thick biological samples composed of measured elements, the sources of the measurable spectral lines, and by the residual matrix, which affects the measured intensities through absorption. In the first model a fixed residual matrix of the sample is assumed, while in the second model the residual matrix is obtained by the iteration refinement of elemental concentrations and an adjusted residual matrix. The absorption of the incident focused beam in the biological sample at each scanned pixel position, determined from the output of a photodiode or a CCD camera, is applied as a control in the iteration procedure of quantification.

Model-based redesign of global transcription regulation

PubMed Central

Carrera, Javier; Rodrigo, Guillermo; Jaramillo, Alfonso

2009-01-01

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology. PMID:19188257

Fluidity models in ancient Greece and current practices of sex assignment

PubMed Central

Chen, Min-Jye; McCann-Crosby, Bonnie; Gunn, Sheila; Georgiadis, Paraskevi; Placencia, Frank; Mann, David; Axelrad, Marni; Karaviti, L.P; McCullough, Laurence B.

2018-01-01

Disorders of sexual differentiation such as androgen insensitivity and gonadal dysgenesis can involve an intrinsic fluidity at different levels, from the anatomical and biological to the social (gender) that must be considered in the context of social constraints. Sex assignment models based on George Engel’s biopsychosocial aspects model of biology accept fluidity of gender as a central concept and therefore help establish expectations within the uncertainty of sex assignment and anticipate potential changes. The biology underlying the fluidity inherent to these disorders should be presented to parents at diagnosis, an approach that the gender medicine field should embrace as good practice. Greek mythology provides many accepted archetypes of change, and the ancient Greek appreciation of metamorphosis can be used as context with these patients. Our goal is to inform expertise and optimal approaches, knowing that this fluidity may eventually necessitate sex reassignment. Physicians should provide sex assignment education based on different components of sexual differentiation, prepare parents for future hormone-triggered changes in their children, and establish a sex-assignment algorithm. PMID:28478088

Anticipatory dynamics of biological systems: from molecular quantum states to evolution

NASA Astrophysics Data System (ADS)

Igamberdiev, Abir U.

2015-08-01

Living systems possess anticipatory behaviour that is based on the flexibility of internal models generated by the system's embedded description. The idea was suggested by Aristotle and is explicitly introduced to theoretical biology by Rosen. The possibility of holding the embedded internal model is grounded in the principle of stable non-equilibrium (Bauer). From the quantum mechanical view, this principle aims to minimize energy dissipation in expense of long relaxation times. The ideas of stable non-equilibrium were developed by Liberman who viewed living systems as subdivided into the quantum regulator and the molecular computer supporting coherence of the regulator's internal quantum state. The computational power of the cell molecular computer is based on the possibility of molecular rearrangements according to molecular addresses. In evolution, the anticipatory strategies are realized both as a precession of phylogenesis by ontogenesis (Berg) and as the anticipatory search of genetic fixation of adaptive changes that incorporates them into the internal model of genetic system. We discuss how the fundamental ideas of anticipation can be introduced into the basic foundations of theoretical biology.

Fluidity models in ancient Greece and current practices of sex assignment.

PubMed

Chen, Min-Jye; McCann-Crosby, Bonnie; Gunn, Sheila; Georgiadis, Paraskevi; Placencia, Frank; Mann, David; Axelrad, Marni; Karaviti, L P; McCullough, Laurence B

2017-06-01

Disorders of sexual differentiation such as androgen insensitivity and gonadal dysgenesis can involve an intrinsic fluidity at different levels, from the anatomical and biological to the social (gender) that must be considered in the context of social constraints. Sex assignment models based on George Engel's biopsychosocial aspects model of biology accept fluidity of gender as a central concept and therefore help establish expectations within the uncertainty of sex assignment and anticipate potential changes. The biology underlying the fluidity inherent to these disorders should be presented to parents at diagnosis, an approach that the gender medicine field should embrace as good practice. Greek mythology provides many accepted archetypes of change, and the ancient Greek appreciation of metamorphosis can be used as context with these patients. Our goal is to inform expertise and optimal approaches, knowing that this fluidity may eventually necessitate sex reassignment. Physicians should provide sex assignment education based on different components of sexual differentiation, prepare parents for future hormone-triggered changes in their children, and establish a sex-assignment algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain).

PubMed

García Nieto, P J; Alonso Fernández, J R; de Cos Juez, F J; Sánchez Lasheras, F; Díaz Muñiz, C

2013-04-01

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational waters. As a result, anticipate its presence is a matter of importance to prevent risks. The aim of this study is to use a hybrid approach based on support vector regression (SVR) in combination with genetic algorithms (GAs), known as a genetic algorithm support vector regression (GA-SVR) model, in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). The GA-SVR approach is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out proved its high performance. Some physical-chemical parameters have been considered along with the biological ones. The results obtained are two-fold. In the first place, the significance of each biological and physical-chemical variable on the cyanotoxins presence in the reservoir is determined with success. Finally, a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. Copyright © 2013 Elsevier Inc. All rights reserved.

Biologically active ligands for yersinia outer protein H (YopH): feature based pharmacophore screening, docking and molecular dynamics studies.

PubMed

Tamilvanan, Thangaraju; Hopper, Waheeta

2014-01-01

Yersinia pestis, a Gram negative bacillus, spreads via lymphatic to lymph nodes and to all organs through the bloodstream, causing plague. Yersinia outer protein H (YopH) is one of the important effector proteins, which paralyzes lymphocytes and macrophages by dephosphorylating critical tyrosine kinases and signal transduction molecules. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse sets of YopH inhibitors, which would be useful for designing of potential antitoxin. In this study, we have selected 60 biologically active inhibitors of YopH to perform Ligand based pharmacophore study to elucidate the important structural features responsible for biological activity. Pharmacophore model demonstrated the importance of two acceptors, one hydrophobic and two aromatic features toward the biological activity. Based on these features, different databases were screened to identify novel compounds and these ligands were subjected for docking, ADME properties and Binding energy prediction. Post docking validation was performed using molecular dynamics simulation for selected ligands to calculate the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The ligands, ASN03270114, Mol_252138, Mol_31073 and ZINC04237078 may act as inhibitors against YopH of Y. pestis.

Ways of incorporating photographic images in learning and assessing high school biology: A study of visual perception and visual cognition

NASA Astrophysics Data System (ADS)

Nixon, Brenda Chaumont

This study evaluated the cognitive benefits and costs of incorporating biology-textbook and student-generated photographic images into the learning and assessment processes within a 10th grade biology classroom. The study implemented Wandersee's (2000) 20-Q Model of Image-Based Biology Test-Item Design (20-Q Model) to explore the use of photographic images to assess students' understanding of complex biological processes. A thorough review of the students' textbook using ScaleMaster R with PC Interface was also conducted. The photographs, diagrams, and other representations found in the textbook were measured to determine the percentage of each graphic depicted in the book and comparisons were made to the text. The theoretical framework that guided the research included Human Constructivist tenets espoused by Mintzes, Wandersee and Novak (2000). Physiological and cognitive factors of images and image-based learning as described by Robin (1992), Solso (1997) and Wandersee (2000) were examined. Qualitative case study design presented by Yin (1994), Denzin and Lincoln (1994) was applied and data were collected through interviews, observations, student activities, student and school artifacts and Scale Master IIRTM measurements. The results of the study indicate that although 24% of the high school biology textbook is devoted to photographic images which contribute significantly to textbook cost, the teacher and students paid little attention to photographic images other than as aesthetic elements for creating biological ambiance, wasting valuable opportunities for learning. The analysis of the photographs corroborated findings published by the Association American Association for the Advancement of Science that indicated "While most of the books are lavishly illustrated, these representations are rarely helpful, because they are too abstract, needlessly complicated, or inadequately explained" (Roseman, 2000, p. 2). The findings also indicate that applying the 20-Q Model to photographs in biology textbooks can (a) effectively assess students' understanding of complex biological concepts, (b) offer alternative assessment strategies that complement individual learning styles, (c) identify misconceptions, and (d) encourage students to practice metacognition. In addition, once students have learned how to interpret textbook images, application of that knowledge through self-generated biologically relevant digital or print images provides opportunities for increased conceptual understanding.

Maintaining homeostasis by decision-making.

PubMed

Korn, Christoph W; Bach, Dominik R

2015-05-01

Living organisms need to maintain energetic homeostasis. For many species, this implies taking actions with delayed consequences. For example, humans may have to decide between foraging for high-calorie but hard-to-get, and low-calorie but easy-to-get food, under threat of starvation. Homeostatic principles prescribe decisions that maximize the probability of sustaining appropriate energy levels across the entire foraging trajectory. Here, predictions from biological principles contrast with predictions from economic decision-making models based on maximizing the utility of the endpoint outcome of a choice. To empirically arbitrate between the predictions of biological and economic models for individual human decision-making, we devised a virtual foraging task in which players chose repeatedly between two foraging environments, lost energy by the passage of time, and gained energy probabilistically according to the statistics of the environment they chose. Reaching zero energy was framed as starvation. We used the mathematics of random walks to derive endpoint outcome distributions of the choices. This also furnished equivalent lotteries, presented in a purely economic, casino-like frame, in which starvation corresponded to winning nothing. Bayesian model comparison showed that--in both the foraging and the casino frames--participants' choices depended jointly on the probability of starvation and the expected endpoint value of the outcome, but could not be explained by economic models based on combinations of statistical moments or on rank-dependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decision-making than economic models based on endpoint utility maximization.

Maintaining Homeostasis by Decision-Making

PubMed Central

Korn, Christoph W.; Bach, Dominik R.

2015-01-01

Living organisms need to maintain energetic homeostasis. For many species, this implies taking actions with delayed consequences. For example, humans may have to decide between foraging for high-calorie but hard-to-get, and low-calorie but easy-to-get food, under threat of starvation. Homeostatic principles prescribe decisions that maximize the probability of sustaining appropriate energy levels across the entire foraging trajectory. Here, predictions from biological principles contrast with predictions from economic decision-making models based on maximizing the utility of the endpoint outcome of a choice. To empirically arbitrate between the predictions of biological and economic models for individual human decision-making, we devised a virtual foraging task in which players chose repeatedly between two foraging environments, lost energy by the passage of time, and gained energy probabilistically according to the statistics of the environment they chose. Reaching zero energy was framed as starvation. We used the mathematics of random walks to derive endpoint outcome distributions of the choices. This also furnished equivalent lotteries, presented in a purely economic, casino-like frame, in which starvation corresponded to winning nothing. Bayesian model comparison showed that—in both the foraging and the casino frames—participants’ choices depended jointly on the probability of starvation and the expected endpoint value of the outcome, but could not be explained by economic models based on combinations of statistical moments or on rank-dependent utility. This implies that under precisely defined constraints biological principles are better suited to explain human decision-making than economic models based on endpoint utility maximization. PMID:26024504

A framework for predicting impacts on ecosystem services ...

EPA Pesticide Factsheets

Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. The framework introduced here represents an ongoing initiative supported by the National Institute of Mathematical and Biological Synthesis (NIMBioS; http://www.nimbi

Adaptive walking of a quadrupedal robot based on layered biological reflexes

NASA Astrophysics Data System (ADS)

Zhang, Xiuli; Mingcheng, E.; Zeng, Xiangyu; Zheng, Haojun

2012-07-01

A multiple-legged robot is traditionally controlled by using its dynamic model. But the dynamic-model-based approach fails to acquire satisfactory performances when the robot faces rough terrains and unknown environments. Referring animals' neural control mechanisms, a control model is built for a quadruped robot walking adaptively. The basic rhythmic motion of the robot is controlled by a well-designed rhythmic motion controller(RMC) comprising a central pattern generator(CPG) for hip joints and a rhythmic coupler (RC) for knee joints. CPG and RC have relationships of motion-mapping and rhythmic couple. Multiple sensory-motor models, abstracted from the neural reflexes of a cat, are employed. These reflex models are organized and thus interact with the CPG in three layers, to meet different requirements of complexity and response time to the tasks. On the basis of the RMC and layered biological reflexes, a quadruped robot is constructed, which can clear obstacles and walk uphill and downhill autonomously, and make a turn voluntarily in uncertain environments, interacting with the environment in a way similar to that of an animal. The paper provides a biologically inspired architecture, with which a robot can walk adaptively in uncertain environments in a simple and effective way, and achieve better performances.

Modeling mammary organogenesis from biological first principles: Cells and their physical constraints.

PubMed

Montévil, Maël; Speroni, Lucia; Sonnenschein, Carlos; Soto, Ana M

2016-10-01

In multicellular organisms, relations among parts and between parts and the whole are contextual and interdependent. These organisms and their cells are ontogenetically linked: an organism starts as a cell that divides producing non-identical cells, which organize in tri-dimensional patterns. These association patterns and cells types change as tissues and organs are formed. This contextuality and circularity makes it difficult to establish detailed cause and effect relationships. Here we propose an approach to overcome these intrinsic difficulties by combining the use of two models; 1) an experimental one that employs 3D culture technology to obtain the structures of the mammary gland, namely, ducts and acini, and 2) a mathematical model based on biological principles. The typical approach for mathematical modeling in biology is to apply mathematical tools and concepts developed originally in physics or computer sciences. Instead, we propose to construct a mathematical model based on proper biological principles. Specifically, we use principles identified as fundamental for the elaboration of a theory of organisms, namely i) the default state of cell proliferation with variation and motility and ii) the principle of organization by closure of constraints. This model has a biological component, the cells, and a physical component, a matrix which contains collagen fibers. Cells display agency and move and proliferate unless constrained; they exert mechanical forces that i) act on collagen fibers and ii) on other cells. As fibers organize, they constrain the cells on their ability to move and to proliferate. The model exhibits a circularity that can be interpreted in terms of closure of constraints. Implementing the mathematical model shows that constraints to the default state are sufficient to explain ductal and acinar formation, and points to a target of future research, namely, to inhibitors of cell proliferation and motility generated by the epithelial cells. The success of this model suggests a step-wise approach whereby additional constraints imposed by the tissue and the organism could be examined in silico and rigorously tested by in vitro and in vivo experiments, in accordance with the organicist perspective we embrace. Copyright © 2016. Published by Elsevier Ltd.

Modeling mammary organogenesis from biological first principles: Cells and their physical constraints

PubMed Central

Montévil, Maël; Speroni, Lucia; Sonnenschein, Carlos; Soto, Ana M.

2017-01-01

In multicellular organisms, relations among parts and between parts and the whole are contextual and interdependent. These organisms and their cells are ontogenetically linked: an organism starts as a cell that divides producing non-identical cells, which organize in tri-dimensional patterns. These association patterns and cells types change as tissues and organs are formed. This contextuality and circularity makes it difficult to establish detailed cause and effect relationships. Here we propose an approach to overcome these intrinsic difficulties by combining the use of two models; 1) an experimental one that employs 3D culture technology to obtain the structures of the mammary gland, namely, ducts and acini, and 2) a mathematical model based on biological principles. The typical approach for mathematical modeling in biology is to apply mathematical tools and concepts developed originally in physics or computer sciences. Instead, we propose to construct a mathematical model based on proper biological principles. Specifically, we use principles identified as fundamental for the elaboration of a theory of organisms, namely i) the default state of cell proliferation with variation and motility and ii) the principle of organization by closure of constraints. This model has a biological component, the cells, and a physical component, a matrix which contains collagen fibers. Cells display agency and move and proliferate unless constrained; they exert mechanical forces that i) act on collagen fibers and ii) on other cells. As fibers organize, they constrain the cells on their ability to move and to proliferate. The model exhibits a circularity that can be interpreted in terms of closure of constraints. Implementing the mathematical model shows that constraints to the default state are sufficient to explain ductal and acinar formation, and points to a target of future research, namely, to inhibitors of cell proliferation and motility generated by the epithelial cells. The success of this model suggests a step-wise approach whereby additional constraints imposed by the tissue and the organism could be examined in silico and rigorously tested by in vitro and in vivo experiments, in accordance with the organicist perspective we embrace. PMID:27544910

Modeling science: Supporting a more authentic epistemology of science

NASA Astrophysics Data System (ADS)

Svoboda, Julia Marie

In this dissertation I argue that model-based inquiry has the potential to create experiences for students to consider how scientific knowledge is generated and evaluated - that is, for students to consider the epistemology of science. I also argue that such epistemically rich experiences can lead to shifts in students' conceptions of the nature of scientific knowledge. The context of this work is a yearlong biological modeling traineeship for undergraduate mathematics and biology majors called Collaborative Learning at the Interface of Mathematics and Biology (CLIMB). I used an ethnographically-based approach to collect detailed field notes, video, documents and interviews with faculty and students in CLIMB. The resulting dataset provides a rich description of the CLIMB program as well as students experiences in this program. Analysis of the CLIMB curriculum revealed that the degree to which students were treated as independent scholars and challenged with authentic problems influenced the productivity of their activity. A more detailed analysis of the nature of modeling tasks revealed that only when models were at the center of their activity did students have opportunities to consider epistemic themes relating to how knowledge is created and critiqued in science. Finally, a case study that followed a single student described how rich epistemically rich experiences with modeling have the potential to shift the ways in which students conceive of scientific knowledge and practice. It also provided evidence that supports the theory that students have complex multidimensional epistemic ecologies as opposed to static views about science. As a whole, this dissertation provides a rich description of how model-based inquiry can support learning about the epistemology of science and suggests that scientific modeling should have a more central role in science education.

Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

PubMed

Davidsen, Peter K; Turan, Nil; Egginton, Stuart; Falciani, Francesco

2016-02-01

The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. Copyright © 2016 the American Physiological Society.

Rich in life but poor in data: the known knowns and known unknowns of modelling how soil biology drives soil structure

NASA Astrophysics Data System (ADS)

Hallett, Paul; Ogden, Mike

2015-04-01

Soil biology has a fascinating capacity to manipulate pore structure by altering or overcoming hydrological and mechanical properties of soil. Many have postulated, quite rightly, that this capacity of soil biology to 'engineer' its habitat drives its diversity, improves competitiveness and increases resilience to external stresses. A large body of observational research has quantified pore structure evolution accompanied by the growth of organisms in soil. Specific compounds that are exuded by organisms or the biological structures they create have been isolated and found to correlate well with observed changes to pore structure or soil stability. This presentation will provide an overview of basic mechanical and hydrological properties of soil that are affected by biology, and consider missing data that are essential to model how they impact soil structure evolution. Major knowledge gaps that prevent progress will be identified and suggestions will be made of how research in this area should progress. We call for more research to gain a process based understanding of structure formation by biology, to complement observational studies of soil structure before and after imposed biological activity. Significant advancement has already been made in modelling soil stabilisation by plant roots, by combining data on root biomechanics, root-soil interactions and soil mechanical properties. Approaches for this work were developed from earlier materials science and geotechnical engineering research, and the same ethos should be adopted to model the impacts of other biological compounds. Fungal hyphae likely reinforce soils in a similar way to plant roots, with successful biomechanical measurements of these micron diameter structures achieved with micromechanical test frames. Extending root reinforcement models to fungi would not be a straightforward exercise, however, as interparticle bonding and changes to pore water caused by fungal exudates could have a major impact on structure formation and stability. Biological exudates from fungi, bacteria or roots have been found to decrease surface tension and increase viscosity of pore water, with observed impacts to soil strength and water retention. Modelling approaches developed in granular mechanics and geotechnical engineering could be built upon to incorporate biological transformations of hydrological and mechanical properties of soil. With new testing approaches, adapted from materials science, pore scale hydromechanical impacts from biological exudates can be quantified. The research can be complemented with model organisms with differences in biological structures (e.g. root hair mutants), exudation or other properties. Coupled with technological advances that provide 4D imaging of soil structure at relatively rapid capture rates, the potential opportunities to disentangle and model how biology drives soil structure evolution and stability are vast. By quantifying basic soil hydrological and mechanical processes that are driven by soil biology, unknown unknowns may also emerge, providing new insight into how soils function.

Correcting the record of structural publications requires joint effort of the community and journal editors.

PubMed

Rupp, Bernhard; Wlodawer, Alexander; Minor, Wladek; Helliwell, John R; Jaskolski, Mariusz

2016-12-01

Seriously flawed and even fictional models of biomolecular crystal structures, although rare, still persist in the record of structural repositories and databases. The ensuing problems of database contamination and persistence of publications based on incorrect structure models must be effectively addressed. The burden cannot be simply left to the critical voices who take the effort to contribute dissenting comments that are mostly ignored. The entire structural biology community, and particularly the journal editors who exercise significant power in this respect, must engage in a constructive dialog lest structural biology lose its credibility as an evidence-based empirical science. © 2016 Federation of European Biochemical Societies.

Analysis of Effects of Organizational Behavior on Evolving System of Systems Acquisition Programs Through Agent Based Modeling

DTIC Science & Technology

2013-03-01

function is based on how individualistic or collectivistic a system is. Low individualism values mean the system is more collective and is less likely...Hofstede’s cultural dimensions, integrated with a modified version of the Bak- Sneppen biological evolutionary model, this research highlights which set...14 Hofstede’s Cultural Dimensions

"A Cellular Encounter": Constructing the Cell as a Whole System Using Illustrative Models

ERIC Educational Resources Information Center

Cohen, Joel I.

2014-01-01

A standard part of biology curricula is a project-based assessment of cell structure and function. However, these are often individual assignments that promote little problem-solving or group learning and avoid the subject of organelle chemical interactions. I evaluate a model-based cell project designed to foster group and individual guided…

Societal impact of synthetic biology: responsible research and innovation (RRI).

PubMed

Gregorowius, Daniel; Deplazes-Zemp, Anna

2016-11-30

Synthetic biology is an emerging field at the interface between biology and engineering, which has generated many expectations for beneficial biomedical and biotechnological applications. At the same time, however, it has also raised concerns about risks or the aim of producing new forms of living organisms. Researchers from different disciplines as well as policymakers and the general public have expressed the need for a form of technology assessment that not only deals with technical aspects, but also includes societal and ethical issues. A recent and very influential model of technology assessment that tries to implement these aims is known as RRI (Responsible Research and Innovation). In this paper, we introduce this model and its historical precursor strategies. Based on the societal and ethical issues which are presented in the current literature, we discuss challenges and opportunities of applying the RRI model for the assessment of synthetic biology. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.