GENIUS: web server to predict local gene networks and key genes for biological functions.

PubMed

Puelma, Tomas; Araus, Viviana; Canales, Javier; Vidal, Elena A; Cabello, Juan M; Soto, Alvaro; Gutiérrez, Rodrigo A

2017-03-01

GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions may have different levels of complexity, from specific biological processes to complex traits that involve several interacting processes. GENIUS also enriches the network with new genes related to the biological function of interest, with accuracies comparable to highly discriminative Support Vector Machine methods. GENIUS currently supports eight model organisms and is freely available for public use at http://networks.bio.puc.cl/genius . genius.psbl@gmail.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Dynamic pathway modeling of signal transduction networks: a domain-oriented approach.

PubMed

Conzelmann, Holger; Gilles, Ernst-Dieter

2008-01-01

Mathematical models of biological processes become more and more important in biology. The aim is a holistic understanding of how processes such as cellular communication, cell division, regulation, homeostasis, or adaptation work, how they are regulated, and how they react to perturbations. The great complexity of most of these processes necessitates the generation of mathematical models in order to address these questions. In this chapter we provide an introduction to basic principles of dynamic modeling and highlight both problems and chances of dynamic modeling in biology. The main focus will be on modeling of s transduction pathways, which requires the application of a special modeling approach. A common pattern, especially in eukaryotic signaling systems, is the formation of multi protein signaling complexes. Even for a small number of interacting proteins the number of distinguishable molecular species can be extremely high. This combinatorial complexity is due to the great number of distinct binding domains of many receptors and scaffold proteins involved in signal transduction. However, these problems can be overcome using a new domain-oriented modeling approach, which makes it possible to handle complex and branched signaling pathways.

PerSubs: A Graph-Based Algorithm for the Identification of Perturbed Subpathways Caused by Complex Diseases.

PubMed

Vrahatis, Aristidis G; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios

2017-01-01

In the era of Systems Biology and growing flow of omics experimental data from high throughput techniques, experimentalists are in need of more precise pathway-based tools to unravel the inherent complexity of diseases and biological processes. Subpathway-based approaches are the emerging generation of pathway-based analysis elucidating the biological mechanisms under the perspective of local topologies onto a complex pathway network. Towards this orientation, we developed PerSub, a graph-based algorithm which detects subpathways perturbed by a complex disease. The perturbations are imprinted through differentially expressed and co-expressed subpathways as recorded by RNA-seq experiments. Our novel algorithm is applied on data obtained from a real experimental study and the identified subpathways provide biological evidence for the brain aging.

A complexity basis for phenomenology: How information states at criticality offer a new approach to understanding experience of self, being and time.

PubMed

Hankey, Alex

2015-12-01

In the late 19th century Husserl studied our internal sense of time passing, maintaining that its deep connections into experience represent prima facie evidence for it as the basis for all investigations in the sciences: Phenomenology was born. Merleau-Ponty focused on perception pointing out that any theory of experience must accord with established aspects of biology i.e. be embodied. Recent analyses suggest that theories of experience require non-reductive, integrative information, together with a specific property connecting them to experience. Here we elucidate a new class of information states with just such properties found at the loci of control of complex biological systems, including nervous systems. Complexity biology concerns states satisfying self-organized criticality. Such states are located at critical instabilities, commonly observed in biological systems, and thought to maximize information diversity and processing, and hence to optimize regulation. Major results for biology follow: why organisms have unusually low entropies; and why they are not merely mechanical. Criticality states form singular self-observing systems, which reduce wave packets by processes of perfect self-observation associated with feedback gain g = 1. Analysis of their information properties leads to identification of a new kind of information state with high levels of internal coherence, and feedback loops integrated into their structure. The major idea presented here is that the integrated feedback loops are responsible for our 'sense of self', and also the feeling of continuity in our sense of time passing. Long-range internal correlations guarantee a unique kind of non-reductive, integrative information structure enabling such states to naturally support phenomenal experience. Being founded in complexity biology, they are 'embodied'; they also fulfill the statement that 'The self is a process', a singular process. High internal correlations and René Thom-style catastrophes support non-digital forms of information, gestalt cognition, and information transfer via quantum teleportation. Criticality in complexity biology can 'embody' cognitive states supporting gestalts, and phenomenology's senses of 'self,' time passing, existence and being. Copyright © 2015. Published by Elsevier Ltd.

Role of Conserved Oligomeric Golgi Complex in the Abnormalities of Glycoprotein Processing in Breast Cancer Cells

DTIC Science & Technology

2006-05-01

terminal oligosaccharide units serve as highly specific biological recognition molecules implicated in major regulatory processes of the cell...treatment or mock-treated for 9 days. To study the glycosylation process in COG complex depleted cells series of Pulse -Chase experiments have been...DAMD17-03-1-0243 TITLE: Role of the Conserved Oligomeric Golgi Complex in the Abnormalities of Glycoprotein Processing in Breast Cancer

Does constructive neutral evolution play an important role in the origin of cellular complexity? Making sense of the origins and uses of biological complexity.

PubMed

Speijer, Dave

2011-05-01

Recently, constructive neutral evolution has been touted as an important concept for the understanding of the emergence of cellular complexity. It has been invoked to help explain the development and retention of, amongst others, RNA splicing, RNA editing and ribosomal and mitochondrial respiratory chain complexity. The theory originated as a welcome explanation of isolated small scale cellular idiosyncrasies and as a reaction to 'overselectionism'. Here I contend, that in its extended form, it has major conceptual problems, can not explain observed patterns of complex processes, is too easily dismissive of alternative selectionist models, underestimates the creative force of complexity as such, and--if seen as a major evolutionary mechanism for all organisms--could stifle further thought regarding the evolution of highly complex biological processes. Copyright © 2011 WILEY Periodicals, Inc.

Advances and Computational Tools towards Predictable Design in Biological Engineering

PubMed Central

2014-01-01

The design process of complex systems in all the fields of engineering requires a set of quantitatively characterized components and a method to predict the output of systems composed by such elements. This strategy relies on the modularity of the used components or the prediction of their context-dependent behaviour, when parts functioning depends on the specific context. Mathematical models usually support the whole process by guiding the selection of parts and by predicting the output of interconnected systems. Such bottom-up design process cannot be trivially adopted for biological systems engineering, since parts function is hard to predict when components are reused in different contexts. This issue and the intrinsic complexity of living systems limit the capability of synthetic biologists to predict the quantitative behaviour of biological systems. The high potential of synthetic biology strongly depends on the capability of mastering this issue. This review discusses the predictability issues of basic biological parts (promoters, ribosome binding sites, coding sequences, transcriptional terminators, and plasmids) when used to engineer simple and complex gene expression systems in Escherichia coli. A comparison between bottom-up and trial-and-error approaches is performed for all the discussed elements and mathematical models supporting the prediction of parts behaviour are illustrated. PMID:25161694

A meta-analysis to evaluate the cellular processes regulated by the interactome of endogenous and over-expressed estrogen receptor alpha.

PubMed

Simões, Joana; Amado, Francisco M; Vitorino, Rui; Helguero, Luisa A

2015-01-01

The nature of the proteins complexes that regulate ERα subcellular localization and activity is still an open question in breast cancer biology. Identification of such complexes will help understand development of endocrine resistance in ER+ breast cancer. Mass spectrometry (MS) has allowed comprehensive analysis of the ERα interactome. We have compared six published works analyzing the ERα interactome of MCF-7 and HeLa cells in order to identify a shared or different pathway-related fingerprint. Overall, 806 ERα interacting proteins were identified. The cellular processes were differentially represented according to the ERα purification methodology, indicating that the methodologies used are complementary. While in MCF-7 cells, the interactome of endogenous and over-expressed ERα essentially represents the same biological processes and cellular components, the proteins identified were not over-lapping; thus, suggesting that the biological response may differ as the regulatory/participating proteins in these complexes are different. Interestingly, biological processes uniquely associated to ERα over-expressed in HeLa cell line included L-serine biosynthetic process, cellular amino acid biosynthetic process and cell redox homeostasis. In summary, all the approaches analyzed in this meta-analysis are valid and complementary; in particular, for those cases where the processes occur at low frequency with normal ERα levels, and can be identified when the receptor is over-expressed. However special effort should be put into validating these findings in cells expressing physiological ERα levels.

Biological removal of NOx from flue gas.

PubMed

Kumaraswamy, R; Muyzer, G; Kuenen, J G; Loosdrecht, M C M

2004-01-01

BioDeNOx is a novel integrated physico-chemical and biological process for the removal of nitrogen oxides (NOx) from flue gas. Due to the high temperature of flue gas the process is performed at a temperature between 50-55 degrees C. Flue gas containing CO2, O2, SO2 and NOx, is purged through Fe(II)EDTA2- containing liquid. The Fe(II)EDTA2- complex effectively binds the NOx; the bound NOx is converted into N2 in a complex reaction sequence. In this paper an overview of the potential microbial reactions in the BioDeNOx process is discussed. It is evident that though the process looks simple, due to the large number of parallel potential reactions and serial microbial conversions, it is much more complex. There is a need for a detailed investigation in order to properly understand and optimise the process.

Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics.

PubMed

Popescu, George V; Noutsos, Christos; Popescu, Sorina C

2016-01-01

In modern plant biology, progress is increasingly defined by the scientists' ability to gather and analyze data sets of high volume and complexity, otherwise known as "big data". Arguably, the largest increase in the volume of plant data sets over the last decade is a consequence of the application of the next-generation sequencing and mass-spectrometry technologies to the study of experimental model and crop plants. The increase in quantity and complexity of biological data brings challenges, mostly associated with data acquisition, processing, and sharing within the scientific community. Nonetheless, big data in plant science create unique opportunities in advancing our understanding of complex biological processes at a level of accuracy without precedence, and establish a base for the plant systems biology. In this chapter, we summarize the major drivers of big data in plant science and big data initiatives in life sciences with a focus on the scope and impact of iPlant, a representative cyberinfrastructure platform for plant science.

Materials Manufactured from 3D Printed Synthetic Biology Arrays

NASA Technical Reports Server (NTRS)

Gentry, Diana; Micks, Ashley

2013-01-01

Many complex, biologically-derived materials have extremely useful properties (think wood or silk), but are unsuitable for space-related applications due to production, manufacturing, or processing limitations. Large-scale ecosystem-based production, such as raising and harvesting trees for wood, is impractical in a self-contained habitat such as a space station or potential Mars colony. Manufacturing requirements, such as the specialized equipment needed to harvest and process cotton, add too much upmass for current launch technology. Cells in nature are already highly specialized for making complex biological materials on a micro scale. We envision combining these strengths with the recently emergent technologies of synthetic biology and 3D printing to create 3D-structured arrays of cells that are bioengineered to secrete different materials in a specified three-dimensional pattern.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery

PubMed Central

Macropol, Kathy; Can, Tolga; Singh, Ambuj K

2009-01-01

Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

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.

Empirical modeling for intelligent, real-time manufacture control

NASA Technical Reports Server (NTRS)

Xu, Xiaoshu

1994-01-01

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Designer cell signal processing circuits for biotechnology

PubMed Central

Bradley, Robert W.; Wang, Baojun

2015-01-01

Microorganisms are able to respond effectively to diverse signals from their environment and internal metabolism owing to their inherent sophisticated information processing capacity. A central aim of synthetic biology is to control and reprogramme the signal processing pathways within living cells so as to realise repurposed, beneficial applications ranging from disease diagnosis and environmental sensing to chemical bioproduction. To date most examples of synthetic biological signal processing have been built based on digital information flow, though analogue computing is being developed to cope with more complex operations and larger sets of variables. Great progress has been made in expanding the categories of characterised biological components that can be used for cellular signal manipulation, thereby allowing synthetic biologists to more rationally programme increasingly complex behaviours into living cells. Here we present a current overview of the components and strategies that exist for designer cell signal processing and decision making, discuss how these have been implemented in prototype systems for therapeutic, environmental, and industrial biotechnological applications, and examine emerging challenges in this promising field. PMID:25579192

A probabilistic framework for identifying biosignatures using Pathway Complexity

NASA Astrophysics Data System (ADS)

Marshall, Stuart M.; Murray, Alastair R. G.; Cronin, Leroy

2017-11-01

One thing that discriminates living things from inanimate matter is their ability to generate similarly complex or non-random structures in a large abundance. From DNA sequences to folded protein structures, living cells, microbial communities and multicellular structures, the material configurations in biology can easily be distinguished from non-living material assemblies. Many complex artefacts, from ordinary bioproducts to human tools, though they are not living things, are ultimately produced by biological processes-whether those processes occur at the scale of cells or societies, they are the consequences of living systems. While these objects are not living, they cannot randomly form, as they are the product of a biological organism and hence are either technological or cultural biosignatures. A generalized approach that aims to evaluate complex objects as possible biosignatures could be useful to explore the cosmos for new life forms. However, it is not obvious how it might be possible to create such a self-contained approach. This would require us to prove rigorously that a given artefact is too complex to have formed by chance. In this paper, we present a new type of complexity measure, which we call `Pathway Complexity', that allows us not only to threshold the abiotic-biotic divide, but also to demonstrate a probabilistic approach based on object abundance and complexity which can be used to unambiguously assign complex objects as biosignatures. We hope that this approach will not only open up the search for biosignatures beyond the Earth, but also allow us to explore the Earth for new types of biology, and to determine when a complex chemical system discovered in the laboratory could be considered alive. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Learning Cell Biology as a Team: A Project-Based Approach to Upper-Division Cell Biology

ERIC Educational Resources Information Center

Wright, Robin; Boggs, James

2002-01-01

To help students develop successful strategies for learning how to learn and communicate complex information in cell biology, we developed a quarter-long cell biology class based on team projects. Each team researches a particular human disease and presents information about the cellular structure or process affected by the disease, the cellular…

Can a Multimedia Tool Help Students' Learning Performance in Complex Biology Subjects?

ERIC Educational Resources Information Center

Koseoglu, Pinar; Efendioglu, Akin

2015-01-01

The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio) and teacher-centered biology (TCbio) instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, TCbio (n = 22) and Mbio (n =…

Animal models and conserved processes

PubMed Central

2012-01-01

Background The concept of conserved processes presents unique opportunities for using nonhuman animal models in biomedical research. However, the concept must be examined in the context that humans and nonhuman animals are evolved, complex, adaptive systems. Given that nonhuman animals are examples of living systems that are differently complex from humans, what does the existence of a conserved gene or process imply for inter-species extrapolation? Methods We surveyed the literature including philosophy of science, biological complexity, conserved processes, evolutionary biology, comparative medicine, anti-neoplastic agents, inhalational anesthetics, and drug development journals in order to determine the value of nonhuman animal models when studying conserved processes. Results Evolution through natural selection has employed components and processes both to produce the same outcomes among species but also to generate different functions and traits. Many genes and processes are conserved, but new combinations of these processes or different regulation of the genes involved in these processes have resulted in unique organisms. Further, there is a hierarchy of organization in complex living systems. At some levels, the components are simple systems that can be analyzed by mathematics or the physical sciences, while at other levels the system cannot be fully analyzed by reducing it to a physical system. The study of complex living systems must alternate between focusing on the parts and examining the intact whole organism while taking into account the connections between the two. Systems biology aims for this holism. We examined the actions of inhalational anesthetic agents and anti-neoplastic agents in order to address what the characteristics of complex living systems imply for inter-species extrapolation of traits and responses related to conserved processes. Conclusion We conclude that even the presence of conserved processes is insufficient for inter-species extrapolation when the trait or response being studied is located at higher levels of organization, is in a different module, or is influenced by other modules. However, when the examination of the conserved process occurs at the same level of organization or in the same module, and hence is subject to study solely by reductionism, then extrapolation is possible. PMID:22963674

Principal process analysis of biological models.

PubMed

Casagranda, Stefano; Touzeau, Suzanne; Ropers, Delphine; Gouzé, Jean-Luc

2018-06-14

Understanding the dynamical behaviour of biological systems is challenged by their large number of components and interactions. While efforts have been made in this direction to reduce model complexity, they often prove insufficient to grasp which and when model processes play a crucial role. Answering these questions is fundamental to unravel the functioning of living organisms. We design a method for dealing with model complexity, based on the analysis of dynamical models by means of Principal Process Analysis. We apply the method to a well-known model of circadian rhythms in mammals. The knowledge of the system trajectories allows us to decompose the system dynamics into processes that are active or inactive with respect to a certain threshold value. Process activities are graphically represented by Boolean and Dynamical Process Maps. We detect model processes that are always inactive, or inactive on some time interval. Eliminating these processes reduces the complex dynamics of the original model to the much simpler dynamics of the core processes, in a succession of sub-models that are easier to analyse. We quantify by means of global relative errors the extent to which the simplified models reproduce the main features of the original system dynamics and apply global sensitivity analysis to test the influence of model parameters on the errors. The results obtained prove the robustness of the method. The analysis of the sub-model dynamics allows us to identify the source of circadian oscillations. We find that the negative feedback loop involving proteins PER, CRY, CLOCK-BMAL1 is the main oscillator, in agreement with previous modelling and experimental studies. In conclusion, Principal Process Analysis is a simple-to-use method, which constitutes an additional and useful tool for analysing the complex dynamical behaviour of biological systems.

Exponential evolution: implications for intelligent extraterrestrial life.

PubMed

Russell, D A

1983-01-01

Some measures of biologic complexity, including maximal levels of brain development, are exponential functions of time through intervals of 10(6) to 10(9) yrs. Biological interactions apparently stimulate evolution but physical conditions determine the time required to achieve a given level of complexity. Trends in brain evolution suggest that other organisms could attain human levels within approximately 10(7) yrs. The number (N) and longevity (L) terms in appropriate modifications of the Drake Equation, together with trends in the evolution of biological complexity on Earth, could provide rough estimates of the prevalence of life forms at specified levels of complexity within the Galaxy. If life occurs throughout the cosmos, exponential evolutionary processes imply that higher intelligence will soon (10(9) yrs) become more prevalent than it now is. Changes in the physical universe become less rapid as time increases from the Big Bang. Changes in biological complexity may be most rapid at such later times. This lends a unique and symmetrical importance to early and late universal times.

Deciphering the Interdependence between Ecological and Evolutionary Networks.

PubMed

Melián, Carlos J; Matthews, Blake; de Andreazzi, Cecilia S; Rodríguez, Jorge P; Harmon, Luke J; Fortuna, Miguel A

2018-05-24

Biological systems consist of elements that interact within and across hierarchical levels. For example, interactions among genes determine traits of individuals, competitive and cooperative interactions among individuals influence population dynamics, and interactions among species affect the dynamics of communities and ecosystem processes. Such systems can be represented as hierarchical networks, but can have complex dynamics when interdependencies among levels of the hierarchy occur. We propose integrating ecological and evolutionary processes in hierarchical networks to explore interdependencies in biological systems. We connect gene networks underlying predator-prey trait distributions to food webs. Our approach addresses longstanding questions about how complex traits and intraspecific trait variation affect the interdependencies among biological levels and the stability of meta-ecosystems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Active Interaction Mapping as a tool to elucidate hierarchical functions of biological processes.

PubMed

Farré, Jean-Claude; Kramer, Michael; Ideker, Trey; Subramani, Suresh

2017-07-03

Increasingly, various 'omics data are contributing significantly to our understanding of novel biological processes, but it has not been possible to iteratively elucidate hierarchical functions in complex phenomena. We describe a general systems biology approach called Active Interaction Mapping (AI-MAP), which elucidates the hierarchy of functions for any biological process. Existing and new 'omics data sets can be iteratively added to create and improve hierarchical models which enhance our understanding of particular biological processes. The best datatypes to further improve an AI-MAP model are predicted computationally. We applied this approach to our understanding of general and selective autophagy, which are conserved in most eukaryotes, setting the stage for the broader application to other cellular processes of interest. In the particular application to autophagy-related processes, we uncovered and validated new autophagy and autophagy-related processes, expanded known autophagy processes with new components, integrated known non-autophagic processes with autophagy and predict other unexplored connections.

Towards Engineering Biological Systems in a Broader Context.

PubMed

Venturelli, Ophelia S; Egbert, Robert G; Arkin, Adam P

2016-02-27

Significant advances have been made in synthetic biology to program information processing capabilities in cells. While these designs can function predictably in controlled laboratory environments, the reliability of these devices in complex, temporally changing environments has not yet been characterized. As human society faces global challenges in agriculture, human health and energy, synthetic biology should develop predictive design principles for biological systems operating in complex environments. Natural biological systems have evolved mechanisms to overcome innumerable and diverse environmental challenges. Evolutionary design rules should be extracted and adapted to engineer stable and predictable ecological function. We highlight examples of natural biological responses spanning the cellular, population and microbial community levels that show promise in synthetic biology contexts. We argue that synthetic circuits embedded in host organisms or designed ecologies informed by suitable measurement of biotic and abiotic environmental parameters could be used as engineering substrates to achieve target functions in complex environments. Successful implementation of these methods will broaden the context in which synthetic biological systems can be applied to solve important problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessing Students' Ability to Trace Matter in Dynamic Systems in Cell Biology

ERIC Educational Resources Information Center

Wilson, Christopher D.; Anderson, Charles W.; Heidemann, Merle; Merrill, John E.; Merritt, Brett W.; Richmond, Gail; Sibley, Duncan F.; Parker, Joyce M.

2006-01-01

College-level biology courses contain many complex processes that are often taught and learned as detailed narratives. These processes can be better understood by perceiving them as dynamic systems that are governed by common fundamental principles. Conservation of matter is such a principle, and thus tracing matter is an essential step in…

Cell Migration Analysis: A Low-Cost Laboratory Experiment for Cell and Developmental Biology Courses Using Keratocytes from Fish Scales

ERIC Educational Resources Information Center

Prieto, Daniel; Aparicio, Gonzalo; Sotelo-Silveira, Jose R.

2017-01-01

Cell and developmental processes are complex, and profoundly dependent on spatial relationships that change over time. Innovative educational or teaching strategies are always needed to foster deep comprehension of these processes and their dynamic features. However, laboratory exercises in cell and developmental biology at the undergraduate level…

Using machine learning tools to model complex toxic interactions with limited sampling regimes.

PubMed

Bertin, Matthew J; Moeller, Peter; Guillette, Louis J; Chapman, Robert W

2013-03-19

A major impediment to understanding the impact of environmental stress, including toxins and other pollutants, on organisms, is that organisms are rarely challenged by one or a few stressors in natural systems. Thus, linking laboratory experiments that are limited by practical considerations to a few stressors and a few levels of these stressors to real world conditions is constrained. In addition, while the existence of complex interactions among stressors can be identified by current statistical methods, these methods do not provide a means to construct mathematical models of these interactions. In this paper, we offer a two-step process by which complex interactions of stressors on biological systems can be modeled in an experimental design that is within the limits of practicality. We begin with the notion that environment conditions circumscribe an n-dimensional hyperspace within which biological processes or end points are embedded. We then randomly sample this hyperspace to establish experimental conditions that span the range of the relevant parameters and conduct the experiment(s) based upon these selected conditions. Models of the complex interactions of the parameters are then extracted using machine learning tools, specifically artificial neural networks. This approach can rapidly generate highly accurate models of biological responses to complex interactions among environmentally relevant toxins, identify critical subspaces where nonlinear responses exist, and provide an expedient means of designing traditional experiments to test the impact of complex mixtures on biological responses. Further, this can be accomplished with an astonishingly small sample size.

A framework for stochastic simulations and visualization of biological electron-transfer dynamics

NASA Astrophysics Data System (ADS)

Nakano, C. Masato; Byun, Hye Suk; Ma, Heng; Wei, Tao; El-Naggar, Mohamed Y.

2015-08-01

Electron transfer (ET) dictates a wide variety of energy-conversion processes in biological systems. Visualizing ET dynamics could provide key insight into understanding and possibly controlling these processes. We present a computational framework named VizBET to visualize biological ET dynamics, using an outer-membrane Mtr-Omc cytochrome complex in Shewanella oneidensis MR-1 as an example. Starting from X-ray crystal structures of the constituent cytochromes, molecular dynamics simulations are combined with homology modeling, protein docking, and binding free energy computations to sample the configuration of the complex as well as the change of the free energy associated with ET. This information, along with quantum-mechanical calculations of the electronic coupling, provides inputs to kinetic Monte Carlo (KMC) simulations of ET dynamics in a network of heme groups within the complex. Visualization of the KMC simulation results has been implemented as a plugin to the Visual Molecular Dynamics (VMD) software. VizBET has been used to reveal the nature of ET dynamics associated with novel nonequilibrium phase transitions in a candidate configuration of the Mtr-Omc complex due to electron-electron interactions.

Surface modification of biomedical and dental implants and the processes of inflammation, wound healing and bone formation.

PubMed

Stanford, Clark M

2010-01-25

Bone adaptation or integration of an implant is characterized by a series of biological reactions that start with bone turnover at the interface (a process of localized necrosis), followed by rapid repair. The wound healing response is guided by a complex activation of macrophages leading to tissue turnover and new osteoblast differentiation on the implant surface. The complex role of implant surface topography and impact on healing response plays a role in biological criteria that can guide the design and development of future tissue-implant surface interfaces.

Dissecting social cell biology and tumors using Drosophila genetics.

PubMed

Pastor-Pareja, José Carlos; Xu, Tian

2013-01-01

Cancer was seen for a long time as a strictly cell-autonomous process in which oncogenes and tumor-suppressor mutations drive clonal cell expansions. Research in the past decade, however, paints a more integrative picture of communication and interplay between neighboring cells in tissues. It is increasingly clear as well that tumors, far from being homogenous lumps of cells, consist of different cell types that function together as complex tissue-level communities. The repertoire of interactive cell behaviors and the quantity of cellular players involved call for a social cell biology that investigates these interactions. Research into this social cell biology is critical for understanding development of normal and tumoral tissues. Such complex social cell biology interactions can be parsed in Drosophila. Techniques in Drosophila for analysis of gene function and clonal behavior allow us to generate tumors and dissect their complex interactive biology with cellular resolution. Here, we review recent Drosophila research aimed at understanding tissue-level biology and social cell interactions in tumors, highlighting the principles these studies reveal.

Cell migration analysis: A low-cost laboratory experiment for cell and developmental biology courses using keratocytes from fish scales.

PubMed

Prieto, Daniel; Aparicio, Gonzalo; Sotelo-Silveira, Jose R

2017-11-01

Cell and developmental processes are complex, and profoundly dependent on spatial relationships that change over time. Innovative educational or teaching strategies are always needed to foster deep comprehension of these processes and their dynamic features. However, laboratory exercises in cell and developmental biology at the undergraduate level do not often take into account the time dimension. In this article, we provide a laboratory exercise focused in cell migration, aiming to stimulate thinking in time and space dimensions through a simplification of more complex processes occurring in cell or developmental biology. The use of open-source tools for the analysis, as well as the whole package of raw results (available at http://github.com/danielprieto/keratocyte) make it suitable for its implementation in courses with very diverse budgets. Aiming to facilitate the student's transition from science-students to science-practitioners we propose an exercise of scientific thinking, and an evaluation method. This in turn is communicated here to facilitate the finding of common caveats and weaknesses in the process of producing simple scientific communications describing the results achieved. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(6):475-482, 2017. © 2017 The International Union of Biochemistry and Molecular Biology.

Analyzing Change in Students' Gene-to-Evolution Models in College-Level Introductory Biology

ERIC Educational Resources Information Center

Dauer, Joseph T.; Momsen, Jennifer L.; Speth, Elena Bray; Makohon-Moore, Sasha C.; Long, Tammy M.

2013-01-01

Research in contemporary biology has become increasingly complex and organized around understanding biological processes in the context of systems. To better reflect the ways of thinking required for learning about systems, we developed and implemented a pedagogical approach using box-and-arrow models (similar to concept maps) as a foundational…

DNAproDB: an interactive tool for structural analysis of DNA–protein complexes

PubMed Central

Sagendorf, Jared M.

2017-01-01

Abstract Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA–protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA–protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA–protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA–protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA–protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu. PMID:28431131

c-Mantic: A Cytoscape plugin for Semantic Web

EPA Science Inventory

Semantic Web tools can streamline the process of storing, analyzing and sharing biological information. Visualization is important for communicating such complex biological relationships. Here we use the flexibility and speed of the Cytoscape platform to interactively visualize s...

Systems Biology Graphical Notation: Process Description language Level 1 Version 1.3.

PubMed

Moodie, Stuart; Le Novère, Nicolas; Demir, Emek; Mi, Huaiyu; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Process Description language represents biological entities and processes between these entities within a network. SBGN PD focuses on the mechanistic description and temporal dependencies of biological interactions and transformations. The nodes (elements) are split into entity nodes describing, e.g., metabolites, proteins, genes and complexes, and process nodes describing, e.g., reactions and associations. The edges (connections) provide descriptions of relationships (or influences) between the nodes, such as consumption, production, stimulation and inhibition. Among all three languages of SBGN, PD is the closest to metabolic and regulatory pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Effects of aerobic and anaerobic biological processes on leaching of heavy metals from soil amended with sewage sludge compost.

PubMed

Fang, Wen; Wei, Yonghong; Liu, Jianguo; Kosson, David S; van der Sloot, Hans A; Zhang, Peng

2016-12-01

The risk from leaching of heavy metals is a major factor hindering land application of sewage sludge compost (SSC). Understanding the change in heavy metal leaching resulting from soil biological processes provides important information for assessing long-term behavior of heavy metals in the compost amended soil. In this paper, 180days aerobic incubation and 240days anaerobic incubation were conducted to investigate the effects of the aerobic and anaerobic biological processes on heavy metal leaching from soil amended with SSC, combined with chemical speciation modeling. Results showed that leaching concentrations of heavy metals at natural pH were similar before and after biological process. However, the major processes controlling heavy metals were influenced by the decrease of DOC with organic matter mineralization during biological processes. Mineralization of organic matter lowered the contribution of DOC-complexation to Ni and Zn leaching. Besides, the reducing condition produced by biological processes, particularly by the anaerobic biological process, resulted in the loss of sorption sites for As on Fe hydroxide, which increased the potential risk of As release at alkaline pH. Copyright © 2016 Elsevier Ltd. All rights reserved.

Industrial systems biology and its impact on synthetic biology of yeast cell factories.

PubMed

Fletcher, Eugene; Krivoruchko, Anastasia; Nielsen, Jens

2016-06-01

Engineering industrial cell factories to effectively yield a desired product while dealing with industrially relevant stresses is usually the most challenging step in the development of industrial production of chemicals using microbial fermentation processes. Using synthetic biology tools, microbial cell factories such as Saccharomyces cerevisiae can be engineered to express synthetic pathways for the production of fuels, biopharmaceuticals, fragrances, and food flavors. However, directing fluxes through these synthetic pathways towards the desired product can be demanding due to complex regulation or poor gene expression. Systems biology, which applies computational tools and mathematical modeling to understand complex biological networks, can be used to guide synthetic biology design. Here, we present our perspective on how systems biology can impact synthetic biology towards the goal of developing improved yeast cell factories. Biotechnol. Bioeng. 2016;113: 1164-1170. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

The value of mechanistic biophysical information for systems-level understanding of complex biological processes such as cytokinesis.

PubMed

Pollard, Thomas D

2014-12-02

This review illustrates the value of quantitative information including concentrations, kinetic constants and equilibrium constants in modeling and simulating complex biological processes. Although much has been learned about some biological systems without these parameter values, they greatly strengthen mechanistic accounts of dynamical systems. The analysis of muscle contraction is a classic example of the value of combining an inventory of the molecules, atomic structures of the molecules, kinetic constants for the reactions, reconstitutions with purified proteins and theoretical modeling to account for the contraction of whole muscles. A similar strategy is now being used to understand the mechanism of cytokinesis using fission yeast as a favorable model system. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Challenges in Developing Models Describing Complex Soil Systems

NASA Astrophysics Data System (ADS)

Simunek, J.; Jacques, D.

2014-12-01

Quantitative mechanistic models that consider basic physical, mechanical, chemical, and biological processes have the potential to be powerful tools to integrate our understanding of complex soil systems, and the soil science community has often called for models that would include a large number of these diverse processes. However, once attempts have been made to develop such models, the response from the community has not always been overwhelming, especially after it discovered that these models are consequently highly complex, requiring not only a large number of parameters, not all of which can be easily (or at all) measured and/or identified, and which are often associated with large uncertainties, but also requiring from their users deep knowledge of all/most of these implemented physical, mechanical, chemical and biological processes. Real, or perceived, complexity of these models then discourages users from using them even for relatively simple applications, for which they would be perfectly adequate. Due to the nonlinear nature and chemical/biological complexity of the soil systems, it is also virtually impossible to verify these types of models analytically, raising doubts about their applicability. Code inter-comparisons, which is then likely the most suitable method to assess code capabilities and model performance, requires existence of multiple models of similar/overlapping capabilities, which may not always exist. It is thus a challenge not only to developed models describing complex soil systems, but also to persuade the soil science community in using them. As a result, complex quantitative mechanistic models are still an underutilized tool in soil science research. We will demonstrate some of the challenges discussed above on our own efforts in developing quantitative mechanistic models (such as HP1/2) for complex soil systems.

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.

Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.

PubMed

Verma, Garima; Palombo, Alessandro; Grigioni, Mauro; La Monaca, Morena; D'Avenio, Giuseppe

2018-01-01

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.

Mammalian synthetic biology for studying the cell

PubMed Central

Mathur, Melina; Xiang, Joy S.

2017-01-01

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. PMID:27932576

Conceptual Modeling in Systems Biology Fosters Empirical Findings: The mRNA Lifecycle

PubMed Central

Dori, Dov; Choder, Mordechai

2007-01-01

One of the main obstacles to understanding complex biological systems is the extent and rapid evolution of information, way beyond the capacity individuals to manage and comprehend. Current modeling approaches and tools lack adequate capacity to model concurrently structure and behavior of biological systems. Here we propose Object-Process Methodology (OPM), a holistic conceptual modeling paradigm, as a means to model both diagrammatically and textually biological systems formally and intuitively at any desired number of levels of detail. OPM combines objects, e.g., proteins, and processes, e.g., transcription, in a way that is simple and easily comprehensible to researchers and scholars. As a case in point, we modeled the yeast mRNA lifecycle. The mRNA lifecycle involves mRNA synthesis in the nucleus, mRNA transport to the cytoplasm, and its subsequent translation and degradation therein. Recent studies have identified specific cytoplasmic foci, termed processing bodies that contain large complexes of mRNAs and decay factors. Our OPM model of this cellular subsystem, presented here, led to the discovery of a new constituent of these complexes, the translation termination factor eRF3. Association of eRF3 with processing bodies is observed after a long-term starvation period. We suggest that OPM can eventually serve as a comprehensive evolvable model of the entire living cell system. The model would serve as a research and communication platform, highlighting unknown and uncertain aspects that can be addressed empirically and updated consequently while maintaining consistency. PMID:17849002

Mode of action from dose-response microarray data: case study using 10 environmental chemicals

EPA Science Inventory

Ligand-activated nuclear receptors regulate many biological processes through complex interactions with biological macromolecules. Certain xenobiotics alter nuclear receptor signaling through direct or indirect interactions. Defining the mode of action of such xenobiotics is di...

“Gestaltomics”: Systems Biology Schemes for the Study of Neuropsychiatric Diseases

PubMed Central

Gutierrez Najera, Nora A.; Resendis-Antonio, Osbaldo; Nicolini, Humberto

2017-01-01

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed. PMID:28536537

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.

The multi-replication protein A (RPA) system--a new perspective.

PubMed

Sakaguchi, Kengo; Ishibashi, Toyotaka; Uchiyama, Yukinobu; Iwabata, Kazuki

2009-02-01

Replication protein A (RPA) complex has been shown, using both in vivo and in vitro approaches, to be required for most aspects of eukaryotic DNA metabolism: replication, repair, telomere maintenance and homologous recombination. Here, we review recent data concerning the function and biological importance of the multi-RPA complex. There are distinct complexes of RPA found in the biological kingdoms, although for a long time only one type of RPA complex was believed to be present in eukaryotes. Each complex probably serves a different role. In higher plants, three distinct large and medium subunits are present, but only one species of the smallest subunit. Each of these protein subunits forms stable complexes with their respective partners. They are paralogs as complex. Humans possess two paralogs and one analog of RPA. The multi-RPA system can be regarded as universal in eukaryotes. Among eukaryotic kingdoms, paralogs, orthologs, analogs and heterologs of many DNA synthesis-related factors, including RPA, are ubiquitous. Convergent evolution seems to be ubiquitous in these processes. Using recent findings, we review the composition and biological functions of RPA complexes.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

PubMed

Drawert, Brian; Engblom, Stefan; Hellander, Andreas

2012-06-22

Experiments in silico using stochastic reaction-diffusion models have emerged as an important tool in molecular systems biology. Designing computational software for such applications poses several challenges. Firstly, realistic lattice-based modeling for biological applications requires a consistent way of handling complex geometries, including curved inner- and outer boundaries. Secondly, spatiotemporal stochastic simulations are computationally expensive due to the fast time scales of individual reaction- and diffusion events when compared to the biological phenomena of actual interest. We therefore argue that simulation software needs to be both computationally efficient, employing sophisticated algorithms, yet in the same time flexible in order to meet present and future needs of increasingly complex biological modeling. We have developed URDME, a flexible software framework for general stochastic reaction-transport modeling and simulation. URDME uses Unstructured triangular and tetrahedral meshes to resolve general geometries, and relies on the Reaction-Diffusion Master Equation formalism to model the processes under study. An interface to a mature geometry and mesh handling external software (Comsol Multiphysics) provides for a stable and interactive environment for model construction. The core simulation routines are logically separated from the model building interface and written in a low-level language for computational efficiency. The connection to the geometry handling software is realized via a Matlab interface which facilitates script computing, data management, and post-processing. For practitioners, the software therefore behaves much as an interactive Matlab toolbox. At the same time, it is possible to modify and extend URDME with newly developed simulation routines. Since the overall design effectively hides the complexity of managing the geometry and meshes, this means that newly developed methods may be tested in a realistic setting already at an early stage of development. In this paper we demonstrate, in a series of examples with high relevance to the molecular systems biology community, that the proposed software framework is a useful tool for both practitioners and developers of spatial stochastic simulation algorithms. Through the combined efforts of algorithm development and improved modeling accuracy, increasingly complex biological models become feasible to study through computational methods. URDME is freely available at http://www.urdme.org.

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

Protonation free energy levels in complex molecular systems.

PubMed

Antosiewicz, Jan M

2008-04-01

All proteins, nucleic acids, and other biomolecules contain residues capable of exchanging protons with their environment. These proton transfer phenomena lead to pH sensitivity of many molecular processes underlying biological phenomena. In the course of biological evolution, Nature has invented some mechanisms to use pH gradients to regulate biomolecular processes inside cells or in interstitial fluids. Therefore, an ability to model protonation equilibria in molecular systems accurately would be of enormous value for our understanding of biological processes and for possible rational influence on them, like in developing pH dependent drugs to treat particular diseases. This work presents a derivation, by thermodynamic and statistical mechanical methods, of an expression for the free energy of a complex molecular system at arbitrary ionization state of its titratable residues. This constitutes one of the elements of modeling protonation equilibria. Starting from a consideration of a simple acid-base equilibrium of a model compound with a single tritratable group, we arrive at an expression which is of general validity for complex systems. The only approximation used in this derivation is the postulating that the interaction energy between any pair of titratable sites does not depend on the protonation states of all the remaining ionizable groups.

Using synthetic biology to make cells tomorrow's test tubes.

PubMed

Garcia, Hernan G; Brewster, Robert C; Phillips, Rob

2016-04-18

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting.

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology.

PubMed

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-03-22

A protein complex consists of two or more proteins that are linked together through protein-protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples.

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology

PubMed Central

Sudhir, Putty-Reddy; Chen, Chung-Hsuan

2016-01-01

A protein complex consists of two or more proteins that are linked together through protein–protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples. PMID:27011181

Quantitative computational models of molecular self-assembly in systems biology

PubMed Central

Thomas, Marcus; Schwartz, Russell

2017-01-01

Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally. PMID:28535149

Quantitative computational models of molecular self-assembly in systems biology.

PubMed

Thomas, Marcus; Schwartz, Russell

2017-05-23

Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.

Synthetic biology: insights into biological computation.

PubMed

Manzoni, Romilde; Urrios, Arturo; Velazquez-Garcia, Silvia; de Nadal, Eulàlia; Posas, Francesc

2016-04-18

Organisms have evolved a broad array of complex signaling mechanisms that allow them to survive in a wide range of environmental conditions. They are able to sense external inputs and produce an output response by computing the information. Synthetic biology attempts to rationally engineer biological systems in order to perform desired functions. Our increasing understanding of biological systems guides this rational design, while the huge background in electronics for building circuits defines the methodology. In this context, biocomputation is the branch of synthetic biology aimed at implementing artificial computational devices using engineered biological motifs as building blocks. Biocomputational devices are defined as biological systems that are able to integrate inputs and return outputs following pre-determined rules. Over the last decade the number of available synthetic engineered devices has increased exponentially; simple and complex circuits have been built in bacteria, yeast and mammalian cells. These devices can manage and store information, take decisions based on past and present inputs, and even convert a transient signal into a sustained response. The field is experiencing a fast growth and every day it is easier to implement more complex biological functions. This is mainly due to advances in in vitro DNA synthesis, new genome editing tools, novel molecular cloning techniques, continuously growing part libraries as well as other technological advances. This allows that digital computation can now be engineered and implemented in biological systems. Simple logic gates can be implemented and connected to perform novel desired functions or to better understand and redesign biological processes. Synthetic biological digital circuits could lead to new therapeutic approaches, as well as new and efficient ways to produce complex molecules such as antibiotics, bioplastics or biofuels. Biological computation not only provides possible biomedical and biotechnological applications, but also affords a greater understanding of biological systems.

Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants

PubMed Central

Regan, Kelly; Wang, Kanix; Doughty, Emily; Li, Haiquan; Li, Jianrong; Lee, Younghee; Kann, Maricel G

2012-01-01

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics. PMID:22319180

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

Integrative systems biology is an approach that brings together diverse high throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost effective manner. Using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of yet unknown components of a system of interest and how its malfunction leads to disease. PMID:21044756

Complexity: the organizing principle at the interface of biological (dis)order.

PubMed

Bhat, Ramray; Pally, Dharma

2017-07-01

The term complexity means several things to biologists.When qualifying morphological phenotype, on the one hand, it is used to signify the sheer complicatedness of living systems, especially as a result of the multicomponent aspect of biological form. On the other hand, it has been used to represent the intricate nature of the connections between constituents that make up form: a more process-based explanation. In the context of evolutionary arguments, complexity has been defined, in a quantifiable fashion, as the amount of information, an informatic template such as a sequence of nucleotides or amino acids stores about its environment. In this perspective, we begin with a brief review of the history of complexity theory. We then introduce a developmental and an evolutionary understanding of what it means for biological systems to be complex.We propose that the complexity of living systems can be understood through two interdependent structural properties: multiscalarity of interconstituent mechanisms and excitability of the biological materials. The answer to whether a system becomes more or less complex over time depends on the potential for its constituents to interact in novel ways and combinations to give rise to new structures and functions, as well as on the evolution of excitable properties that would facilitate the exploration of interconstituent organization in the context of their microenvironments and macroenvironments.

Quantitative biological surface science: challenges and recent advances.

PubMed

Höök, Fredrik; Kasemo, Bengt; Grunze, Michael; Zauscher, Stefan

2008-12-23

Biological surface science is a broad, interdisciplinary subfield of surface science, where properties and processes at biological and synthetic surfaces and interfaces are investigated, and where biofunctional surfaces are fabricated. The need to study and to understand biological surfaces and interfaces in liquid environments provides sizable challenges as well as fascinating opportunities. Here, we report on recent progress in biological surface science that was described within the program assembled by the Biomaterial Interface Division of the Science and Technology of Materials, Interfaces and Processes (www.avs.org) during their 55th International Symposium and Exhibition held in Boston, October 19-24, 2008. The selected examples show that the rapid progress in nanoscience and nanotechnology, hand-in-hand with theory and simulation, provides increasingly sophisticated methods and tools to unravel the mechanisms and details of complex processes at biological surfaces and in-depth understanding of biomolecular surface interactions.

Mammalian synthetic biology for studying the cell.

PubMed

Mathur, Melina; Xiang, Joy S; Smolke, Christina D

2017-01-02

Synthetic biology is advancing the design of genetic devices that enable the study of cellular and molecular biology in mammalian cells. These genetic devices use diverse regulatory mechanisms to both examine cellular processes and achieve precise and dynamic control of cellular phenotype. Synthetic biology tools provide novel functionality to complement the examination of natural cell systems, including engineered molecules with specific activities and model systems that mimic complex regulatory processes. Continued development of quantitative standards and computational tools will expand capacities to probe cellular mechanisms with genetic devices to achieve a more comprehensive understanding of the cell. In this study, we review synthetic biology tools that are being applied to effectively investigate diverse cellular processes, regulatory networks, and multicellular interactions. We also discuss current challenges and future developments in the field that may transform the types of investigation possible in cell biology. © 2017 Mathur et al.

Simulating Life

ERIC Educational Resources Information Center

Sinclair, Michael; Dauerty, Helene; Alber, Mark

2016-01-01

Biomodeling is the study of the structures and behaviors of interacting biological entities such as molecules, cells, or organisms. While physical and chemical processes give rise to various spatial and temporal structures, even the simplest biological phenomenon is infinitely more complex (Kling 2004). Over the past decade, much of biomodeling…

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…

Evolutionary cell biology: functional insight from "endless forms most beautiful".

PubMed

Richardson, Elisabeth; Zerr, Kelly; Tsaousis, Anastasios; Dorrell, Richard G; Dacks, Joel B

2015-12-15

In animal and fungal model organisms, the complexities of cell biology have been analyzed in exquisite detail and much is known about how these organisms function at the cellular level. However, the model organisms cell biologists generally use include only a tiny fraction of the true diversity of eukaryotic cellular forms. The divergent cellular processes observed in these more distant lineages are still largely unknown in the general scientific community. Despite the relative obscurity of these organisms, comparative studies of them across eukaryotic diversity have had profound implications for our understanding of fundamental cell biology in all species and have revealed the evolution and origins of previously observed cellular processes. In this Perspective, we will discuss the complexity of cell biology found across the eukaryotic tree, and three specific examples of where studies of divergent cell biology have altered our understanding of key functional aspects of mitochondria, plastids, and membrane trafficking. © 2015 Richardson et al. 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).

Active control of complex, multicomponent self-assembly processes

NASA Astrophysics Data System (ADS)

Schulman, Rebecca

The kinetics of many complex biological self-assembly processes such as cytoskeletal assembly are precisely controlled by cells. Spatiotemporal control over rates of filament nucleation, growth and disassembly determine how self-assembly occurs and how the assembled form changes over time. These reaction rates can be manipulated by changing the concentrations of the components needed for assembly by activating or deactivating them. I will describe how we can use these principles to design driven self-assembly processes in which we assemble and disassemble multiple types of components to create micron-scale networks of semiflexible filaments assembled from DNA. The same set of primitive components can be assembled into many different, structures depending on the concentrations of different components and how designed, DNA-based chemical reaction networks manipulate these concentrations over time. These chemical reaction networks can in turn interpret environmental stimuli to direct complex, multistage response. Such a system is a laboratory for understanding complex active material behaviors, such as metamorphosis, self-healing or adaptation to the environment that are ubiquitous in biological systems but difficult to quantitatively characterize or engineer.

Short-term bioassay of complex organic mixtures. Part II. Mutagenicity testing

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

Epler, J.L.; Clark, B.R.; Ho, C.

1978-01-01

The feasibility of using short-term mutagenicity assays to predict the potential biohazard of various crude and complex test materials has been examined in a coupled chemical and biological approach. The principal focus of the research has involved the preliminary chemical characterizatiion and preparation for bioassay, followed by testing in the Salmonella histidine reversion assay system. The mutagenicity tests are intended to act as predictors of profound long-range health effects such as mutagenesis and/or carcinogenesis; act as a mechanism to rapidly isolate and identify a hazardous agent in a complex mixture; and function as a measure of biological activity correlating baselinemore » data with changes in process conditions. Since complex mixtures can be fractionated and approached in these short-term assays, information reflecting on the actual compounds responsible for the biological effect may be accumulated.« less

Soft-Bodied Fossils Are Not Simply Rotten Carcasses - Toward a Holistic Understanding of Exceptional Fossil Preservation: Exceptional Fossil Preservation Is Complex and Involves the Interplay of Numerous Biological and Geological Processes.

PubMed

Parry, Luke A; Smithwick, Fiann; Nordén, Klara K; Saitta, Evan T; Lozano-Fernandez, Jesus; Tanner, Alastair R; Caron, Jean-Bernard; Edgecombe, Gregory D; Briggs, Derek E G; Vinther, Jakob

2018-01-01

Exceptionally preserved fossils are the product of complex interplays of biological and geological processes including burial, autolysis and microbial decay, authigenic mineralization, diagenesis, metamorphism, and finally weathering and exhumation. Determining which tissues are preserved and how biases affect their preservation pathways is important for interpreting fossils in phylogenetic, ecological, and evolutionary frameworks. Although laboratory decay experiments reveal important aspects of fossilization, applying the results directly to the interpretation of exceptionally preserved fossils may overlook the impact of other key processes that remove or preserve morphological information. Investigations of fossils preserving non-biomineralized tissues suggest that certain structures that are decay resistant (e.g., the notochord) are rarely preserved (even where carbonaceous components survive), and decay-prone structures (e.g., nervous systems) can fossilize, albeit rarely. As we review here, decay resistance is an imperfect indicator of fossilization potential, and a suite of biological and geological processes account for the features preserved in exceptional fossils. © 2017 The Authors. BioEssays Published by WILEY Periodicals, Inc.

How to build a course in mathematical-biological modeling: content and processes for knowledge and skill.

PubMed

Hoskinson, Anne-Marie

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical-biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments.

How to Build a Course in Mathematical–Biological Modeling: Content and Processes for Knowledge and Skill

PubMed Central

2010-01-01

Biological problems in the twenty-first century are complex and require mathematical insight, often resulting in mathematical models of biological systems. Building mathematical–biological models requires cooperation among biologists and mathematicians, and mastery of building models. A new course in mathematical modeling presented the opportunity to build both content and process learning of mathematical models, the modeling process, and the cooperative process. There was little guidance from the literature on how to build such a course. Here, I describe the iterative process of developing such a course, beginning with objectives and choosing content and process competencies to fulfill the objectives. I include some inductive heuristics for instructors seeking guidance in planning and developing their own courses, and I illustrate with a description of one instructional model cycle. Students completing this class reported gains in learning of modeling content, the modeling process, and cooperative skills. Student content and process mastery increased, as assessed on several objective-driven metrics in many types of assessments. PMID:20810966

Structure-Based Characterization of Multiprotein Complexes

PubMed Central

Wiederstein, Markus; Gruber, Markus; Frank, Karl; Melo, Francisco; Sippl, Manfred J.

2014-01-01

Summary Multiprotein complexes govern virtually all cellular processes. Their 3D structures provide important clues to their biological roles, especially through structural correlations among protein molecules and complexes. The detection of such correlations generally requires comprehensive searches in databases of known protein structures by means of appropriate structure-matching techniques. Here, we present a high-speed structure search engine capable of instantly matching large protein oligomers against the complete and up-to-date database of biologically functional assemblies of protein molecules. We use this tool to reveal unseen structural correlations on the level of protein quaternary structure and demonstrate its general usefulness for efficiently exploring complex structural relationships among known protein assemblies. PMID:24954616

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces.

PubMed

Bordner, Andrew J; Gorin, Andrey A

2008-05-12

Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB). We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section). Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

Directed Evolution as a Powerful Synthetic Biology Tool

PubMed Central

Cobb, Ryan E.; Sun, Ning; Zhao, Huimin

2012-01-01

At the heart of synthetic biology lies the goal of rationally engineering a complete biological system to achieve a specific objective, such as bioremediation and synthesis of a valuable drug, chemical, or biofuel molecule. However, the inherent complexity of natural biological systems has heretofore precluded generalized application of this approach. Directed evolution, a process which mimics Darwinian selection on a laboratory scale, has allowed significant strides to be made in the field of synthetic biology by allowing rapid identification of desired properties from large libraries of variants. Improvement in biocatalyst activity and stability, engineering of biosynthetic pathways, tuning of functional regulatory systems and logic circuits, and development of desired complex phenotypes in industrial host organisms have all been achieved by way of directed evolution. Here, we review recent contributions of directed evolution to synthetic biology at the protein, pathway, network, and whole cell levels. PMID:22465795

Wood smoke particle sequesters cell iron to impact a biological effect.

EPA Science Inventory

The biological effect of an inorganic particle (i.e., silica) can be associated with a disruption in cell iron homeostasis. Organic compounds included in particles originating from combustion processes can also complex sources of host cell iron to disrupt metal homeostasis. We te...

Selection platforms for directed evolution in synthetic biology

PubMed Central

Tizei, Pedro A.G.; Csibra, Eszter; Torres, Leticia; Pinheiro, Vitor B.

2016-01-01

Life on Earth is incredibly diverse. Yet, underneath that diversity, there are a number of constants and highly conserved processes: all life is based on DNA and RNA; the genetic code is universal; biology is limited to a small subset of potential chemistries. A vast amount of knowledge has been accrued through describing and characterizing enzymes, biological processes and organisms. Nevertheless, much remains to be understood about the natural world. One of the goals in Synthetic Biology is to recapitulate biological complexity from simple systems made from biological molecules–gaining a deeper understanding of life in the process. Directed evolution is a powerful tool in Synthetic Biology, able to bypass gaps in knowledge and capable of engineering even the most highly conserved biological processes. It encompasses a range of methodologies to create variation in a population and to select individual variants with the desired function–be it a ligand, enzyme, pathway or even whole organisms. Here, we present some of the basic frameworks that underpin all evolution platforms and review some of the recent contributions from directed evolution to synthetic biology, in particular methods that have been used to engineer the Central Dogma and the genetic code. PMID:27528765

Selection platforms for directed evolution in synthetic biology.

PubMed

Tizei, Pedro A G; Csibra, Eszter; Torres, Leticia; Pinheiro, Vitor B

2016-08-15

Life on Earth is incredibly diverse. Yet, underneath that diversity, there are a number of constants and highly conserved processes: all life is based on DNA and RNA; the genetic code is universal; biology is limited to a small subset of potential chemistries. A vast amount of knowledge has been accrued through describing and characterizing enzymes, biological processes and organisms. Nevertheless, much remains to be understood about the natural world. One of the goals in Synthetic Biology is to recapitulate biological complexity from simple systems made from biological molecules-gaining a deeper understanding of life in the process. Directed evolution is a powerful tool in Synthetic Biology, able to bypass gaps in knowledge and capable of engineering even the most highly conserved biological processes. It encompasses a range of methodologies to create variation in a population and to select individual variants with the desired function-be it a ligand, enzyme, pathway or even whole organisms. Here, we present some of the basic frameworks that underpin all evolution platforms and review some of the recent contributions from directed evolution to synthetic biology, in particular methods that have been used to engineer the Central Dogma and the genetic code. © 2016 The Author(s).

Biological robustness.

PubMed

Kitano, Hiroaki

2004-11-01

Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.

Complexity and Entropy Analysis of DNMT1 Gene

USDA-ARS?s Scientific Manuscript database

Background: The application of complexity information on DNA sequence and protein in biological processes are well established in this study. Available sequences for DNMT1 gene, which is a maintenance methyltransferase is responsible for copying DNA methylation patterns to the daughter strands durin...

Novel insights into an old disease: recent developments in scabies mite biology.

PubMed

Holt, Deborah C; Fischer, Katja

2013-04-01

Scabies is a serious disease of both humans and other animals caused by infestation of the skin with the ectoparasitic mite Sarcoptes scabiei. Our current understanding of scabies mite biology and disease processes is far outweighed by the significant, worldwide impact of the disease. This review summarizes the recent data which furthers our knowledge of mite biology, host specificity and parasite host evasion mechanisms. Recent data concords with the previous work demonstrating limited gene flow between different host-associated populations of scabies mites. This evidence of the host specificity of scabies mites has important implications for disease control programmes. Other studies have begun to decipher the molecular basis of the complex host-parasite interactions underlying scabies infestations. Scabies mites have developed complex mechanisms to interfere with the host defence processes that may also enhance the survival of the associated skin microbiome, consistent with the epidemiological evidence. Recently developed natural host models of scabies are valuable tools to further study the disease processes and to trial novel therapeutic agents. Although significant progress has been made, further research is needed to understand the biology, host-parasite interactions and pathogenesis of this ubiquitous parasite.

Human cumulative culture: a comparative perspective.

PubMed

Dean, Lewis G; Vale, Gill L; Laland, Kevin N; Flynn, Emma; Kendal, Rachel L

2014-05-01

Many animals exhibit social learning and behavioural traditions, but human culture exhibits unparalleled complexity and diversity, and is unambiguously cumulative in character. These similarities and differences have spawned a debate over whether animal traditions and human culture are reliant on homologous or analogous psychological processes. Human cumulative culture combines high-fidelity transmission of cultural knowledge with beneficial modifications to generate a 'ratcheting' in technological complexity, leading to the development of traits far more complex than one individual could invent alone. Claims have been made for cumulative culture in several species of animals, including chimpanzees, orangutans and New Caledonian crows, but these remain contentious. Whilst initial work on the topic of cumulative culture was largely theoretical, employing mathematical methods developed by population biologists, in recent years researchers from a wide range of disciplines, including psychology, biology, economics, biological anthropology, linguistics and archaeology, have turned their attention to the experimental investigation of cumulative culture. We review this literature, highlighting advances made in understanding the underlying processes of cumulative culture and emphasising areas of agreement and disagreement amongst investigators in separate fields. © 2013 The Authors. Biological Reviews © 2013 Cambridge Philosophical Society.

A Macro-Level Analysis of SRL Processes and Their Relations to the Acquisition of a Sophisticated Mental Model of a Complex System

ERIC Educational Resources Information Center

Greene, Jeffrey Alan; Azevedo, Roger

2009-01-01

In this study, we used think-aloud verbal protocols to examine how various macro-level processes of self-regulated learning (SRL; e.g., planning, monitoring, strategy use, handling of task difficulty and demands) were associated with the acquisition of a sophisticated mental model of a complex biological system. Numerous studies examine how…

Modeling of the U1 snRNP assembly pathway in alternative splicing in human cells using Petri nets.

PubMed

Kielbassa, J; Bortfeldt, R; Schuster, S; Koch, I

2009-02-01

The investigation of spliceosomal processes is currently a topic of intense research in molecular biology. In the molecular mechanism of alternative splicing, a multi-protein-RNA complex - the spliceosome - plays a crucial role. To understand the biological processes of alternative splicing, it is essential to comprehend the biogenesis of the spliceosome. In this paper, we propose the first abstract model of the regulatory assembly pathway of the human spliceosomal subunit U1. Using Petri nets, we describe its highly ordered assembly that takes place in a stepwise manner. Petri net theory represents a mathematical formalism to model and analyze systems with concurrent processes at different abstraction levels with the possibility to combine them into a uniform description language. There exist many approaches to determine static and dynamic properties of Petri nets, which can be applied to analyze biochemical systems. In addition, Petri net tools usually provide intuitively understandable graphical network representations, which facilitate the dialog between experimentalists and theoreticians. Our Petri net model covers binding, transport, signaling, and covalent modification processes. Through the computation of structural and behavioral Petri net properties and their interpretation in biological terms, we validate our model and use it to get a better understanding of the complex processes of the assembly pathway. We can explain the basic network behavior, using minimal T-invariants which represent special pathways through the network. We find linear as well as cyclic pathways. We determine the P-invariants that represent conserved moieties in a network. The simulation of the net demonstrates the importance of the stability of complexes during the maturation pathway. We can show that complexes that dissociate too fast, hinder the formation of the complete U1 snRNP.

Harnessing glycomics technologies: integrating structure with function for glycan characterization

PubMed Central

Robinson, Luke N.; Artpradit, Charlermchai; Raman, Rahul; Shriver, Zachary H.; Ruchirawat, Mathuros; Sasisekharan, Ram

2013-01-01

Glycans, or complex carbohydrates, are a ubiquitous class of biological molecules which impinge on a variety of physiological processes ranging from signal transduction to tissue development and microbial pathogenesis. In comparison to DNA and proteins, glycans present unique challenges to the study of their structure and function owing to their complex and heterogeneous structures and the dominant role played by multivalency in their sequence-specific biological interactions. Arising from these challenges, there is a need to integrate information from multiple complementary methods to decode structure-function relationships. Focusing on acidic glycans, we describe here key glycomics technologies for characterizing their structural attributes, including linkage, modifications, and topology, as well as for elucidating their role in biological processes. Two cases studies, one involving sialylated branched glycans and the other sulfated glycosaminoglycans, are used to highlight how integration of orthogonal information from diverse datasets enables rapid convergence of glycan characterization for development of robust structure-function relationships. PMID:22522536

Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2.

PubMed

Sorokin, Anatoly; Le Novère, Nicolas; Luna, Augustin; Czauderna, Tobias; Demir, Emek; Haw, Robin; Mi, Huaiyu; Moodie, Stuart; Schreiber, Falk; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

The social neuroscience and the theory of integrative levels.

PubMed

Bello-Morales, Raquel; Delgado-García, José María

2015-01-01

The theory of integrative levels provides a general description of the evolution of matter through successive orders of complexity and integration. Along its development, material forms pass through different levels of organization, such as physical, chemical, biological or sociological. The appearance of novel structures and dynamics during this process of development of matter in complex systems has been called emergence. Social neuroscience (SN), an interdisciplinary field that aims to investigate the biological mechanisms that underlie social structures, processes, and behavior and the influences between social and biological levels of organization, has affirmed the necessity for including social context as an essential element to understand the human behavior. To do this, SN proposes a multilevel integrative approach by means of three principles: multiple determinism, nonadditive determinism and reciprocal determinism. These theoretical principles seem to share the basic tenets of the theory of integrative levels but, in this paper, we aim to reveal the differences among both doctrines. First, SN asserts that combination of neural and social variables can produce emergent phenomena that would not be predictable from a neuroscientific or social psychological analysis alone; SN also suggests that to achieve a complete understanding of social structures we should use an integrative analysis that encompasses levels of organization ranging from the genetic level to the social one; finally, SN establishes that there can be mutual influences between biological and social factors in determining behavior, accepting, therefore, a double influence, upward from biology to social level, and downward, from social level to biology. In contrast, following the theory of integrative levels, emergent phenomena are not produced by the combination of variables from two levels, but by the increment of complexity at one level. In addition, the social behavior and structures might be contemplated not as the result of mixing or summing social and biological influences, but as emergent phenomena that should be described with its own laws. Finally, following the integrative levels view, influences upward, from biology to social level, and downward, from social level to biology, might not be equivalent, since the bottom-up processes are emergent and the downward causation (DC) is not.

The social neuroscience and the theory of integrative levels

PubMed Central

Bello-Morales, Raquel; Delgado-García, José María

2015-01-01

The theory of integrative levels provides a general description of the evolution of matter through successive orders of complexity and integration. Along its development, material forms pass through different levels of organization, such as physical, chemical, biological or sociological. The appearance of novel structures and dynamics during this process of development of matter in complex systems has been called emergence. Social neuroscience (SN), an interdisciplinary field that aims to investigate the biological mechanisms that underlie social structures, processes, and behavior and the influences between social and biological levels of organization, has affirmed the necessity for including social context as an essential element to understand the human behavior. To do this, SN proposes a multilevel integrative approach by means of three principles: multiple determinism, nonadditive determinism and reciprocal determinism. These theoretical principles seem to share the basic tenets of the theory of integrative levels but, in this paper, we aim to reveal the differences among both doctrines. First, SN asserts that combination of neural and social variables can produce emergent phenomena that would not be predictable from a neuroscientific or social psychological analysis alone; SN also suggests that to achieve a complete understanding of social structures we should use an integrative analysis that encompasses levels of organization ranging from the genetic level to the social one; finally, SN establishes that there can be mutual influences between biological and social factors in determining behavior, accepting, therefore, a double influence, upward from biology to social level, and downward, from social level to biology. In contrast, following the theory of integrative levels, emergent phenomena are not produced by the combination of variables from two levels, but by the increment of complexity at one level. In addition, the social behavior and structures might be contemplated not as the result of mixing or summing social and biological influences, but as emergent phenomena that should be described with its own laws. Finally, following the integrative levels view, influences upward, from biology to social level, and downward, from social level to biology, might not be equivalent, since the bottom-up processes are emergent and the downward causation (DC) is not. PMID:26578909

Modeling biology with HDL languages: a first step toward a genetic design automation tool inspired from microelectronics.

PubMed

Gendrault, Yves; Madec, Morgan; Lallement, Christophe; Haiech, Jacques

2014-04-01

Nowadays, synthetic biology is a hot research topic. Each day, progresses are made to improve the complexity of artificial biological functions in order to tend to complex biodevices and biosystems. Up to now, these systems are handmade by bioengineers, which require strong technical skills and leads to nonreusable development. Besides, scientific fields that share the same design approach, such as microelectronics, have already overcome several issues and designers succeed in building extremely complex systems with many evolved functions. On the other hand, in systems engineering and more specifically in microelectronics, the development of the domain has been promoted by both the improvement of technological processes and electronic design automation tools. The work presented in this paper paves the way for the adaptation of microelectronics design tools to synthetic biology. Considering the similarities and differences between the synthetic biology and microelectronics, the milestones of this adaptation are described. The first one concerns the modeling of biological mechanisms. To do so, a new formalism is proposed, based on an extension of the generalized Kirchhoff laws to biology. This way, a description of all biological mechanisms can be made with languages widely used in microelectronics. Our approach is therefore successfully validated on specific examples drawn from the literature.

Pinpointing the level of isolation between two cryptic species sharing the same microhabitat: a case study with a scarabaeid species complex

USDA-ARS?s Scientific Manuscript database

We explored biological processes underlying speciation within dung beetles belonging to the vacca species complex (Scarabaeidae: Onthophagus). The two taxa of this complex, O. vacca and O. medius, not only are known to have a large overlapping Palearctic distribution range but also share the same co...

Economic Analysis of Biological Invasions in Forests

Treesearch

Tomas P. Holmes; Julian Aukema; Jeffrey Englin; Robert G. Haight; Kent Kovacs; Brian Leung

2014-01-01

Biological invasions of native forests by nonnative pests result from complex stochastic processes that are difficult to predict. Although economic optimization models describe efficient controls across the stages of an invasion, the ability to calibrate such models is constrained by lack of information on pest population dynamics and consequent economic damages. Here...

Plants: Novel Developmental Processes.

ERIC Educational Resources Information Center

Goldberg, Robert B.

1988-01-01

Describes the diversity of plants. Outlines novel developmental and complex genetic processes that are specific to plants. Identifies approaches that can be used to solve problems in plant biology. Cites the advantages of using higher plants for experimental systems. (RT)

A new theoretical approach to analyze complex processes in cytoskeleton proteins.

PubMed

Li, Xin; Kolomeisky, Anatoly B

2014-03-20

Cytoskeleton proteins are filament structures that support a large number of important biological processes. These dynamic biopolymers exist in nonequilibrium conditions stimulated by hydrolysis chemical reactions in their monomers. Current theoretical methods provide a comprehensive picture of biochemical and biophysical processes in cytoskeleton proteins. However, the description is only qualitative under biologically relevant conditions because utilized theoretical mean-field models neglect correlations. We develop a new theoretical method to describe dynamic processes in cytoskeleton proteins that takes into account spatial correlations in the chemical composition of these biopolymers. Our approach is based on analysis of probabilities of different clusters of subunits. It allows us to obtain exact analytical expressions for a variety of dynamic properties of cytoskeleton filaments. By comparing theoretical predictions with Monte Carlo computer simulations, it is shown that our method provides a fully quantitative description of complex dynamic phenomena in cytoskeleton proteins under all conditions.

A general mechanism for competitor-induced dissociation of molecular complexes

PubMed Central

Paramanathan, Thayaparan; Reeves, Daniel; Friedman, Larry J.; Kondev, Jane; Gelles, Jeff

2014-01-01

The kinetic stability of non-covalent macromolecular complexes controls many biological phenomena. Here we find that physical models of complex dissociation predict that competitor molecules will in general accelerate the breakdown of isolated bimolecular complexes by occluding rapid rebinding of the two binding partners. This prediction is largely independent of molecular details. We confirm the prediction with single-molecule fluorescence experiments on a well-characterized DNA strand dissociation reaction. Contrary to common assumptions, competitor–induced acceleration of dissociation can occur in biologically relevant competitor concentration ranges and does not necessarily implyternary association of competitor with the bimolecular complex. Thus, occlusion of complex rebinding may play a significant role in a variety of biomolecular processes. The results also show that single-molecule colocalization experiments can accurately measure dissociation rates despite their limited spatio temporal resolution. PMID:25342513

EPR Characterization of Dinitrosyl Iron Complexes with Thiol-Containing Ligands as an Approach to Their Identification in Biological Objects: An Overview.

PubMed

Vanin, Anatoly F

2018-06-01

The overview demonstrates how the use of only one physico-chemical approach, viz., the electron paramagnetic resonance method, allowed detection and identification of dinitrosyl iron complexes with thiol-containing ligands in various animal and bacterial cells. These complexes are formed in biological objects in the paramagnetic (electron paramagnetic resonance-active) mononuclear and diamagnetic (electron paramagnetic resonance-silent) binuclear forms and control the activity of nitrogen monoxide, one of the most universal regulators of metabolic processes in the organism. The analysis of electronic and spatial structures of dinitrosyl iron complex sheds additional light on the mechanism whereby dinitrosyl iron complex with thiol-containing ligands function in human and animal cells as donors of nitrogen monoxide and its ionized form, viz., nitrosonium ions (NO + ).

Accomplishment Summary 1968-1969. Biological Computer Laboratory.

ERIC Educational Resources Information Center

Von Foerster, Heinz; And Others

This report summarizes theoretical, applied, and experimental studies in the areas of computational principles in complex intelligent systems, cybernetics, multivalued logic, and the mechanization of cognitive processes. This work is summarized under the following topic headings: properties of complex dynamic systems; computers and the language…

Parameterization and Sensitivity Analysis of a Complex Simulation Model for Mosquito Population Dynamics, Dengue Transmission, and Their Control

PubMed Central

Ellis, Alicia M.; Garcia, Andres J.; Focks, Dana A.; Morrison, Amy C.; Scott, Thomas W.

2011-01-01

Models can be useful tools for understanding the dynamics and control of mosquito-borne disease. More detailed models may be more realistic and better suited for understanding local disease dynamics; however, evaluating model suitability, accuracy, and performance becomes increasingly difficult with greater model complexity. Sensitivity analysis is a technique that permits exploration of complex models by evaluating the sensitivity of the model to changes in parameters. Here, we present results of sensitivity analyses of two interrelated complex simulation models of mosquito population dynamics and dengue transmission. We found that dengue transmission may be influenced most by survival in each life stage of the mosquito, mosquito biting behavior, and duration of the infectious period in humans. The importance of these biological processes for vector-borne disease models and the overwhelming lack of knowledge about them make acquisition of relevant field data on these biological processes a top research priority. PMID:21813844

[Application of microelectronics CAD tools to synthetic biology].

PubMed

Madec, Morgan; Haiech, Jacques; Rosati, Élise; Rezgui, Abir; Gendrault, Yves; Lallement, Christophe

2017-02-01

Synthetic biology is an emerging science that aims to create new biological functions that do not exist in nature, based on the knowledge acquired in life science over the last century. Since the beginning of this century, several projects in synthetic biology have emerged. The complexity of the developed artificial bio-functions is relatively low so that empirical design methods could be used for the design process. Nevertheless, with the increasing complexity of biological circuits, this is no longer the case and a large number of computer aided design softwares have been developed in the past few years. These tools include languages for the behavioral description and the mathematical modelling of biological systems, simulators at different levels of abstraction, libraries of biological devices and circuit design automation algorithms. All of these tools already exist in other fields of engineering sciences, particularly in microelectronics. This is the approach that is put forward in this paper. © 2017 médecine/sciences – Inserm.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Structure-based characterization of multiprotein complexes.

PubMed

Wiederstein, Markus; Gruber, Markus; Frank, Karl; Melo, Francisco; Sippl, Manfred J

2014-07-08

Multiprotein complexes govern virtually all cellular processes. Their 3D structures provide important clues to their biological roles, especially through structural correlations among protein molecules and complexes. The detection of such correlations generally requires comprehensive searches in databases of known protein structures by means of appropriate structure-matching techniques. Here, we present a high-speed structure search engine capable of instantly matching large protein oligomers against the complete and up-to-date database of biologically functional assemblies of protein molecules. We use this tool to reveal unseen structural correlations on the level of protein quaternary structure and demonstrate its general usefulness for efficiently exploring complex structural relationships among known protein assemblies. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

The effects of morphological irregularity on the mechanical behavior of interdigitated biological sutures under tension.

PubMed

Liu, Lei; Jiang, Yunyao; Boyce, Mary; Ortiz, Christine; Baur, Jeffery; Song, Juha; Li, Yaning

2017-06-14

Irregular interdigitated morphology is prevalent in biological sutures in nature. Suture complexity index has long been recognized as the most important morphological parameter to govern the mechanical properties of biological sutures. However, the suture complexity index alone does not reflect all aspects of suture morphology. The goal of this investigation was to determine that besides suture complexity index, whether the degree of morphological irregularity of biological sutures has influences on the mechanical properties, and if there is any, how to quantify these influences. To explore these issues, theoretical and finite element (FE) suture models with the same suture complexity index but different levels of morphological irregularity were developed. The quasi-static stiffness, strength for damage initiation and post-failure process of irregular sutures were studied. It was shown that for the same suture complexity index, when the level of morphological irregularity increases, the overall strain to failure will increase while tensile stiffness is retained; also, the total energy to fracture increases with a sacrifice in strength to damage initiation. These results reveal that morphological irregularity is another important independent parameter to govern and balance the mechanical properties of biological sutures. Therefore, from the mechanics point of view, the prevalence of irregular suture morphology in nature is a merit, not a defect. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visualization of molecular structures using HoloLens-based augmented reality

PubMed Central

Hoffman, MA; Provance, JB

2017-01-01

Biological molecules and biologically active small molecules are complex three dimensional structures. Current flat screen monitors are limited in their ability to convey the full three dimensional characteristics of these molecules. Augmented reality devices, including the Microsoft HoloLens, offer an immersive platform to change how we interact with molecular visualizations. We describe a process to incorporate the three dimensional structures of small molecules and complex proteins into the Microsoft HoloLens using aspirin and the human leukocyte antigen (HLA) as examples. Small molecular structures can be introduced into the HoloStudio application, which provides native support for rotating, resizing and performing other interactions with these molecules. Larger molecules can be imported through the Unity gaming development platform and then Microsoft Visual Developer. The processes described here can be modified to import a wide variety of molecular structures into augmented reality systems and improve our comprehension of complex structural features. PMID:28815109

Rapid self-assembly of complex biomolecular architectures during mussel byssus biofabrication

PubMed Central

Priemel, Tobias; Degtyar, Elena; Dean, Mason N.; Harrington, Matthew J.

2017-01-01

Protein-based biogenic materials provide important inspiration for the development of high-performance polymers. The fibrous mussel byssus, for instance, exhibits exceptional wet adhesion, abrasion resistance, toughness and self-healing capacity–properties that arise from an intricate hierarchical organization formed in minutes from a fluid secretion of over 10 different protein precursors. However, a poor understanding of this dynamic biofabrication process has hindered effective translation of byssus design principles into synthetic materials. Here, we explore mussel byssus assembly in Mytilus edulis using a synergistic combination of histological staining and confocal Raman microspectroscopy, enabling in situ tracking of specific proteins during induced thread formation from soluble precursors to solid fibres. Our findings reveal critical insights into this complex biological manufacturing process, showing that protein precursors spontaneously self-assemble into complex architectures, while maturation proceeds in subsequent regulated steps. Beyond their biological importance, these findings may guide development of advanced materials with biomedical and industrial relevance. PMID:28262668

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

Horowitz, Scott; Salmon, Loïc; Koldewey, Philipp

We present that challenges in determining the structures of heterogeneous and dynamic protein complexes have greatly hampered past efforts to obtain a mechanistic understanding of many important biological processes. One such process is chaperone-assisted protein folding. Obtaining structural ensembles of chaperone–substrate complexes would ultimately reveal how chaperones help proteins fold into their native state. To address this problem, we devised a new structural biology approach based on X-ray crystallography, termed residual electron and anomalous density (READ). READ enabled us to visualize even sparsely populated conformations of the substrate protein immunity protein 7 (Im7) in complex with the Escherichia coli chaperonemore » Spy, and to capture a series of snapshots depicting the various folding states of Im7 bound to Spy. The ensemble shows that Spy-associated Im7 samples conformations ranging from unfolded to partially folded to native-like states and reveals how a substrate can explore its folding landscape while being bound to a chaperone.« less

Multiple hypothesis tracking for cluttered biological image sequences.

PubMed

Chenouard, Nicolas; Bloch, Isabelle; Olivo-Marin, Jean-Christophe

2013-11-01

In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.

Structure, Agency, Complexity Theory and Interdisciplinary Research in Education Studies

ERIC Educational Resources Information Center

Smith, John A.

2013-01-01

This article argues that Education Studies needs to develop its existing interdisciplinarity understanding of structures and agencies by giving greater attention to the modern process theories of self-organisation in the physical, biological, psychological and social sciences, sometimes given the umbrella term "complexity theory". The…

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

NASA Astrophysics Data System (ADS)

Stoop, Ruedi; Gomez, Florian

2016-07-01

The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.

Applications of systems approaches in the study of rheumatic diseases.

PubMed

Kim, Ki-Jo; Lee, Saseong; Kim, Wan-Uk

2015-03-01

The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.

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.

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.

Noninvasive imaging of protein-protein interactions in living organisms.

PubMed

Haberkorn, Uwe; Altmann, Annette

2003-06-01

Genomic research is expected to generate new types of complex observational data, changing the types of experiments as well as our understanding of biological processes. The investigation and definition of relationships among proteins is essential for understanding the function of each gene and the mechanisms of biological processes that specific genes are involved in. Recently, a study by Paulmurugan et al. demonstrated a tool for in vivo noninvasive imaging of protein-protein interactions and intracellular networks.

Mass Spectrometry: A Technique of Many Faces

PubMed Central

Olshina, Maya A.; Sharon, Michal

2016-01-01

Protein complexes form the critical foundation for a wide range of biological process, however understanding the intricate details of their activities is often challenging. In this review we describe how mass spectrometry plays a key role in the analysis of protein assemblies and the cellular pathways which they are involved in. Specifically, we discuss how the versatility of mass spectrometric approaches provides unprecedented information on multiple levels. We demonstrate this on the ubiquitin-proteasome proteolytic pathway, a process that is responsible for protein turnover. We follow the various steps of this degradation route and illustrate the different mass spectrometry workflows that were applied for elucidating molecular information. Overall, this review aims to stimulate the integrated use of multiple mass spectrometry approaches for analyzing complex biological systems. PMID:28100928

Tissue regeneration with photobiomodulation

NASA Astrophysics Data System (ADS)

Tang, Elieza G.; Arany, Praveen R.

2013-03-01

Low level light therapy (LLLT) has been widely reported to reduce pain and inflammation and enhance wound healing and tissue regeneration in various settings. LLLT has been noted to have both stimulatory and inhibitory biological effects and these effects have been termed Photobiomodulation (PBM). Several elegant studies have shown the key role of Cytochrome C oxidase and ROS in initiating this process. The downstream biological responses remain to be clearly elucidated. Our work has demonstrated activation of an endogenous latent growth factor complex, TGF-β1, as one of the major biological events in PBM. TGF-β1 has critical roles in various biological processes especially in inflammation, immune responses, wound healing and stem cell biology. This paper overviews some of the studies demonstrating the efficacy of PBM in promoting tissue regeneration.

COMPLEX HOST-PARASITE SYSTEMS IN MARTES: IMPLICATIONS FOR CONSERVATION BIOLOGY OF ENDEMIC FAUNAS.

USDA-ARS?s Scientific Manuscript database

Complex assemblages of hosts and parasites reveal insights about biogeography and ecology and inform us about processes which serve to structure faunal diversity and the biosphere in space and time. Exploring aspects of parasite diversity among martens (species of Martes) and other mustelids reveal...

Modeling the assembly order of multimeric heteroprotein complexes

PubMed Central

Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Shin, Woong-Hee

2018-01-01

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes. PMID:29329283

Modeling the assembly order of multimeric heteroprotein complexes.

PubMed

Peterson, Lenna X; Togawa, Yoichiro; Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Roy, Amitava; Shin, Woong-Hee; Kihara, Daisuke

2018-01-01

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.

Connections Matter: Social Networks and Lifespan Health in Primate Translational Models

PubMed Central

McCowan, Brenda; Beisner, Brianne; Bliss-Moreau, Eliza; Vandeleest, Jessica; Jin, Jian; Hannibal, Darcy; Hsieh, Fushing

2016-01-01

Humans live in societies full of rich and complex relationships that influence health. The ability to improve human health requires a detailed understanding of the complex interplay of biological systems that contribute to disease processes, including the mechanisms underlying the influence of social contexts on these biological systems. A longitudinal computational systems science approach provides methods uniquely suited to elucidate the mechanisms by which social systems influence health and well-being by investigating how they modulate the interplay among biological systems across the lifespan. In the present report, we argue that nonhuman primate social systems are sufficiently complex to serve as model systems allowing for the development and refinement of both analytical and theoretical frameworks linking social life to health. Ultimately, developing systems science frameworks in nonhuman primate models will speed discovery of the mechanisms that subserve the relationship between social life and human health. PMID:27148103

Is Self-organization a Rational Expectation?

NASA Astrophysics Data System (ADS)

Luediger, Heinz

Over decades and under varying names the study of biology-inspired algorithms applied to non-living systems has been the subject of a small and somewhat exotic research community. Only the recent coincidence of a growing inability to master the design, development and operation of increasingly intertwined systems and processes, and an accelerated trend towards a naïve if not romanticizing view of nature in the sciences, has led to the adoption of biology-inspired algorithmic research by a wider range of sciences. Adaptive systems, as we apparently observe in nature, are meanwhile viewed as a promising way out of the complexity trap and, propelled by a long list of ‘self’ catchwords, complexity research has become an influential stream in the science community. This paper presents four provocative theses that cast doubt on the strategic potential of complexity research and the viability of large scale deployment of biology-inspired algorithms in an expectation driven world.

Synchronization in complex networks

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

Arenas, A.; Diaz-Guilera, A.; Moreno, Y.

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analyticalmore » approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.« less

New understanding of rhizosphere processes enabled by advances in molecular and spatially resolved techniques

DOE PAGES

Hess, Nancy J.; Pasa-Tolic, Ljiljana; Bailey, Vanessa L.; ...

2017-04-12

Understanding the role played by microorganisms within soil systems is challenged by the unique intersection of physics, chemistry, mineralogy and biology in fostering habitat for soil microbial communities. To address these challenges will require observations across multiple spatial and temporal scales to capture the dynamics and emergent behavior from complex and interdependent processes. The heterogeneity and complexity of the rhizosphere require advanced techniques that press the simultaneous frontiers of spatial resolution, analyte sensitivity and specificity, reproducibility, large dynamic range, and high throughput. Fortunately many exciting technical advancements are now available to inform and guide the development of new hypotheses. Themore » aim of this Special issue is to provide a holistic view of the rhizosphere in the perspective of modern molecular biology methodologies that enabled a highly-focused, detailed view on the processes in the rhizosphere, including numerous, strong and complex interactions between plant roots, soil constituents and microorganisms. We discuss the current rhizosphere research challenges and knowledge gaps, as well as perspectives and approaches using newly available state-of-the-art toolboxes. These new approaches and methodologies allow the study of rhizosphere processes and properties, and rhizosphere as a central component of ecosystems and biogeochemical cycles.« less

New understanding of rhizosphere processes enabled by advances in molecular and spatially resolved techniques

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

Hess, Nancy J.; Paša-Tolić, Ljiljana; Bailey, Vanessa L.

Understanding the role played by microorganisms within soil systems is challenged by the unique intersection of physics, chemistry, mineralogy and biology in fostering habitat for soil microbial communities. To address these challenges will require observations across multiple spatial and temporal scales to capture the dynamics and emergent behavior from complex and interdependent processes. The heterogeneity and complexity of the rhizosphere require advanced techniques that press the simultaneous frontiers of spatial resolution, analyte sensitivity and specificity, reproducibility, large dynamic range, and high throughput. Fortunately many exciting technical advancements are now available to inform and guide the development of new hypotheses. Themore » aim of this Special issue is to provide a holistic view of the rhizosphere in the perspective of modern molecular biology methodologies that enabled a highly-focused, detailed view on the processes in the rhizosphere, including numerous, strong and complex interactions between plant roots, soil constituents and microorganisms. We discuss the current rhizosphere research challenges and knowledge gaps, as well as perspectives and approaches using newly available state-of-the-art toolboxes. These new approaches and methodologies allow the study of rhizosphere processes and properties, and rhizosphere as a central component of ecosystems and biogeochemical cycles.« less

Hydrology or biology? Modeling simplistic physical constraints on lake carbon biogeochemistry to identify when and where biology is likely to matter

NASA Astrophysics Data System (ADS)

Jones, S.; Zwart, J. A.; Solomon, C.; Kelly, P. T.

2017-12-01

Current efforts to scale lake carbon biogeochemistry rely heavily on empirical observations and rarely consider physical or biological inter-lake heterogeneity that is likely to regulate terrestrial dissolved organic carbon (tDOC) decomposition in lakes. This may in part result from a traditional focus of lake ecologists on in-lake biological processes OR physical-chemical pattern across lake regions, rather than on process AND pattern across scales. To explore the relative importance of local biological processes and physical processes driven by lake hydrologic setting, we created a simple, analytical model of tDOC decomposition in lakes that focuses on the regulating roles of lake size and catchment hydrologic export. Our simplistic model can generally recreate patterns consistent with both local- and regional-scale patterns in tDOC concentration and decomposition. We also see that variation in lake hydrologic setting, including the importance of evaporation as a hydrologic export, generates significant, emergent variation in tDOC decomposition at a given hydrologic residence time, and creates patterns that have been historically attributed to variation in tDOC quality. Comparing predictions of this `biologically null model' to field observations and more biologically complex models could indicate when and where biology is likely to matter most.

The Biology of Bone Metastasis.

PubMed

Esposito, Mark; Guise, Theresa; Kang, Yibin

2018-06-01

Bone metastasis, or the development of secondary tumors within the bone of cancer patients, is a debilitating and incurable disease. Despite its morbidity, the biology of bone metastasis represents one of the most complex and intriguing of all oncogenic processes. This complexity derives from the intricately organized bone microenvironment in which the various stages of hematopoiesis, osteogenesis, and osteolysis are jointly regulated but spatially restricted. Disseminated tumor cells (DTCs) from various common malignancies such as breast, prostate, lung, and kidney cancers or myeloma are uniquely primed to subvert these endogenous bone stromal elements to grow into pathological osteolytic or osteoblastic lesions. This colonization process can be separated into three key steps: seeding, dormancy, and outgrowth. Targeting the processes of dormancy and initial outgrowth offers the most therapeutic promise. Here, we discuss the concepts of the bone metastasis niche, from controlling tumor-cell survival to growth into clinically detectable disease. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.

Glycobiology of Reproductive Processes in Marine Animals: The State of the Art

PubMed Central

Gallo, Alessandra; Costantini, Maria

2012-01-01

Glycobiology is the study of complex carbohydrates in biological systems and represents a developing field of science that has made huge advances in the last half century. In fact, it combines all branches of biomedical research, revealing the vast and diverse forms of carbohydrate structures that exist in nature. Advances in structure determination have enabled scientists to study the function of complex carbohydrates in more depth and to determine the role that they play in a wide range of biological processes. Glycobiology research in marine systems has primarily focused on reproduction, in particular for what concern the chemical communication between the gametes. The current status of marine glycobiology is primarily descriptive, devoted to characterizing marine glycoconjugates with potential biomedical and biotechnological applications. In this review, we describe the current status of the glycobiology in the reproductive processes from gametogenesis to fertilization and embryo development of marine animals. PMID:23247316

Visualization of Proton and Electron Transfer Processes of a Biochemical Reaction by μSR

NASA Astrophysics Data System (ADS)

Kiyotani, Tamiko; Kobayashi, Masayoshi; Tanaka, Ichiro; Niimura, Nobuo

For the last several years, we have discussed and conducted experiments toward realization of visualization of electron and proton transfer process in an enzyme reaction using muon. As the first step for exploring the useful application of the μSR for the biological system, which is "μSR in Biology". A first μSR experiment on biochemical reaction was conducted using the complex of a digestive enzyme, a kind of serine-protease and the inhibitor at J-PARC and PSI.

The fusion of biology, computer science, and engineering: towards efficient and successful synthetic biology.

PubMed

Linshiz, Gregory; Goldberg, Alex; Konry, Tania; Hillson, Nathan J

2012-01-01

Synthetic biology is a nascent field that emerged in earnest only around the turn of the millennium. It aims to engineer new biological systems and impart new biological functionality, often through genetic modifications. The design and construction of new biological systems is a complex, multistep process, requiring multidisciplinary collaborative efforts from "fusion" scientists who have formal training in computer science or engineering, as well as hands-on biological expertise. The public has high expectations for synthetic biology and eagerly anticipates the development of solutions to the major challenges facing humanity. This article discusses laboratory practices and the conduct of research in synthetic biology. It argues that the fusion science approach, which integrates biology with computer science and engineering best practices, including standardization, process optimization, computer-aided design and laboratory automation, miniaturization, and systematic management, will increase the predictability and reproducibility of experiments and lead to breakthroughs in the construction of new biological systems. The article also discusses several successful fusion projects, including the development of software tools for DNA construction design automation, recursive DNA construction, and the development of integrated microfluidics systems.

Extracting biomarkers of commitment to cancer development: potential role of vibrational spectroscopy in systems biology.

PubMed

Theophilou, Georgios; Paraskevaidi, Maria; Lima, Kássio M G; Kyrgiou, Maria; Martin-Hirsch, Pierre L; Martin, Francis L

2015-05-01

The complex processes driving cancer have so far impeded the discovery of dichotomous biomarkers associated with its initiation and progression. Reductionist approaches utilizing 'omics' technologies have met some success in identifying molecular alterations associated with carcinogenesis. Systems biology is an emerging science that combines high-throughput investigation techniques to define the dynamic interplay between regulatory biological systems in response to internal and external cues. Vibrational spectroscopy has the potential to play an integral role within systems biology research approaches. It is capable of examining global models of carcinogenesis by scrutinizing chemical bond alterations within molecules. The application of infrared or Raman spectroscopic approaches coupled with computational analysis under the systems biology umbrella can assist the transition of biomarker research from the molecular level to the system level. The comprehensive representation of carcinogenesis as a multilevel biological process will inevitably revolutionize cancer-related healthcare by personalizing risk prediction and prevention.

Managing unexpected events in the manufacturing of biologic medicines.

PubMed

Grampp, Gustavo; Ramanan, Sundar

2013-08-01

The manufacturing of biologic medicines (biologics) requires robust process and facility design, rigorous regulatory compliance, and a well-trained workforce. Because of the complex attributes of biologics and their sensitivity to production and handling conditions, manufacturing of these medicines also requires a high-reliability manufacturing organization. As required by regulators, such an organization must monitor the state-of-control for the manufacturing process. A high-reliability organization also invests in an experienced and fully engaged technical support staff and fosters a management culture that rewards in-depth analysis of unexpected results, robust risk assessments, and timely and effective implementation of mitigation measures. Such a combination of infrastructure, technology, human capital, management, and a science-based operations culture does not occur without a strong organizational and financial commitment. These attributes of a high-reliability biologics manufacturer are difficult to achieve and may be differentiating factors as the supply of biologics diversifies in future years.

Can a biologist fix a smartphone?-Just hack it!

PubMed

Kamoun, Sophien

2017-05-08

Biological systems integrate multiscale processes and networks and are, therefore, viewed as difficult to dissect. However, because of the clear-cut separation between the software code (the information encoded in the genome sequence) and hardware (organism), genome editors can operate as software engineers to hack biological systems without any particularly deep understanding of the complexity of the systems.

Red Onions, "Elodea," or Decalcified Chicken Eggs? Selecting & Sequencing Representations for Teaching Diffusion & Osmosis

ERIC Educational Resources Information Center

Lankford, Deanna; Friedrichsen, Patricia

2012-01-01

Diffusion and osmosis are important biological concepts that students often struggle to understand. These are important concepts because they are the basis for many complex biological processes, such as photosynthesis and cellular respiration. We examine a wide variety of representations used by experienced teachers to teach diffusion and osmosis.…

Arachidonic-acid-derived eicosanoids: roles in biology and immunopathology.

PubMed

Harizi, Hedi; Corcuff, Jean-Benoît; Gualde, Norbert

2008-10-01

Arachidonic acid (AA)-derived eicosanoids belong to a complex family of lipid mediators that regulate a wide variety of physiological responses and pathological processes. They are produced by various cell types through distinct enzymatic pathways and act on target cells via specific G-protein-coupled receptors. Although originally recognized for their capacity to elicit biological responses such as vascular homeostasis, protection of the gastric mucosa and platelet aggregation, eicosanoids are now understood to regulate immunopathological processes ranging from inflammatory responses to chronic tissue remodelling, cancer, asthma, rheumatoid arthritis and autoimmune disorders. Here, we review the major properties of eicosanoids and their expanding roles in biology and medicine.

The Unicellular State as a Point Source in a Quantum Biological System

PubMed Central

Torday, John S.; Miller, William B.

2016-01-01

A point source is the central and most important point or place for any group of cohering phenomena. Evolutionary development presumes that biological processes are sequentially linked, but neither directed from, nor centralized within, any specific biologic structure or stage. However, such an epigenomic entity exists and its transforming effects can be understood through the obligatory recapitulation of all eukaryotic lifeforms through a zygotic unicellular phase. This requisite biological conjunction can now be properly assessed as the focal point of reconciliation between biology and quantum phenomena, illustrated by deconvoluting complex physiologic traits back to their unicellular origins. PMID:27240413

The Mediator Complex and Lipid Metabolism.

PubMed

Zhang, Yi; Xiaoli; Zhao, Xiaoping; Yang, Fajun

2013-03-01

The precise control of gene expression is essential for all biological processes. In addition to DNA-binding transcription factors, numerous transcription cofactors contribute another layer of regulation of gene transcription in eukaryotic cells. One of such transcription cofactors is the highly conserved Mediator complex, which has multiple subunits and is involved in various biological processes through directly interacting with relevant transcription factors. Although the current understanding on the biological functions of Mediator remains incomplete, research in the past decade has revealed an important role of Mediator in regulating lipid metabolism. Such function of Mediator is dependent on specific transcription factors, including peroxisome proliferator-activated receptor-gamma (PPARγ) and sterol regulatory element-binding proteins (SREBPs), which represent the master regulators of lipid metabolism. The medical significance of these findings is apparent, as aberrant lipid metabolism is intimately linked to major human diseases, such as type 2 diabetes and cardiovascular disease. Here, we briefly review the functions and molecular mechanisms of Mediator in regulation of lipid metabolism.

Complexity and the Arrow of Time

NASA Astrophysics Data System (ADS)

Lineweaver, Charles H.; Davies, Paul C. W.; Ruse, Michael

2013-08-01

1. What is complexity? Is it increasing? Charles H. Lineweaver, Paul C. W. Davies and Michael Ruse; 2. Directionality principles from cancer to cosmology Paul C. W. Davies; 3. A simple treatment of complexity: cosmological entropic boundary conditions on increasing complexity Charles H. Lineweaver; 4. Using complexity science to search for unity in the natural sciences Eric J. Chaisson; 5. On the spontaneous generation of complexity in the universe Seth Lloyd; 6. Emergent spatiotemporal complexity in field theory Marcelo Gleiser; 7. Life: the final frontier for complexity? Simon Conway Morris; 8. Evolution beyond Newton, Darwin, and entailing law: the origin of complexity in the evolving biosphere Stuart A. Kauffman; 9. Emergent order in processes: the interplay of complexity, robustness, correlation, and hierarchy in the biosphere D. Eric Smith; 10. The inferential evolution of biological complexity: forgetting nature by learning to nurture David C. Krakauer; 11. Information width: a way for the second law to increase complexity David Wolpert; 12. Wrestling with biological complexity: from Darwin to Dawkins Michael Ruse; 13. The role of generative entrenchment and robustness in the evolution of complexity William C. Wimsatt; 14. On the plurality of complexity-producing mechanisms Philip Clayton; Index.

Using Storyboarding to Model Gene Expression

ERIC Educational Resources Information Center

Korb, Michele; Colton, Shannon; Vogt, Gina

2015-01-01

Students often find it challenging to create images of complex, abstract biological processes. Using modified storyboards, which contain predrawn images, students can visualize the process and anchor ideas from activities, labs, and lectures. Storyboards are useful in assessing students' understanding of content in larger contexts. They enable…

Mathematical modeling of physiological systems: an essential tool for discovery.

PubMed

Glynn, Patric; Unudurthi, Sathya D; Hund, Thomas J

2014-08-28

Mathematical models are invaluable tools for understanding the relationships between components of a complex system. In the biological context, mathematical models help us understand the complex web of interrelations between various components (DNA, proteins, enzymes, signaling molecules etc.) in a biological system, gain better understanding of the system as a whole, and in turn predict its behavior in an altered state (e.g. disease). Mathematical modeling has enhanced our understanding of multiple complex biological processes like enzyme kinetics, metabolic networks, signal transduction pathways, gene regulatory networks, and electrophysiology. With recent advances in high throughput data generation methods, computational techniques and mathematical modeling have become even more central to the study of biological systems. In this review, we provide a brief history and highlight some of the important applications of modeling in biological systems with an emphasis on the study of excitable cells. We conclude with a discussion about opportunities and challenges for mathematical modeling going forward. In a larger sense, the review is designed to help answer a simple but important question that theoreticians frequently face from interested but skeptical colleagues on the experimental side: "What is the value of a model?" Copyright © 2014 Elsevier Inc. All rights reserved.

BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.

PubMed

Rinaldi, Fabio; Ellendorff, Tilia Renate; Madan, Sumit; Clematide, Simon; van der Lek, Adrian; Mevissen, Theo; Fluck, Juliane

2016-01-01

Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text. © The Author(s) 2016. Published by Oxford University Press.

Nature versus design: synthetic biology or how to build a biological non-machine.

PubMed

Porcar, M; Peretó, J

2016-04-18

The engineering ideal of synthetic biology presupposes that organisms are composed of standard, interchangeable parts with a predictive behaviour. In one word, organisms are literally recognized as machines. Yet living objects are the result of evolutionary processes without any purposiveness, not of a design by external agents. Biological components show massive overlapping and functional degeneracy, standard-free complexity, intrinsic variation and context dependent performances. However, although organisms are not full-fledged machines, synthetic biologists may still be eager for machine-like behaviours from artificially modified biosystems.

Synthesis, characterization and toxicity studies of pyridinecarboxaldehydes and L-tryptophan derived Schiff bases and corresponding copper (II) complexes.

PubMed

Malakyan, Margarita; Babayan, Nelly; Grigoryan, Ruzanna; Sarkisyan, Natalya; Tonoyan, Vahan; Tadevosyan, Davit; Matosyan, Vladimir; Aroutiounian, Rouben; Arakelyan, Arsen

2016-01-01

Schiff bases and their metal-complexes are versatile compounds exhibiting a broad range of biological activities and thus actively used in the drug development process. The aim of the present study was the synthesis and characterization of new Schiff bases and their copper (II) complexes, derived from L-tryptophan and isomeric (2-; 3-; 4-) pyridinecarboxaldehydes, as well as the assessment of their toxicity in vitro . The optimal conditions of the Schiff base synthesis resulting in up to 75-85% yield of target products were identified. The structure-activity relationship analysis indicated that the location of the carboxaldehyde group at 2-, 3- or 4-position with regard to nitrogen of the pyridine ring in aldehyde component of the L-tryptophan derivative Schiff bases and corresponding copper complexes essentially change the biological activity of the compounds. The carboxaldehyde group at 2- and 4-positions leads to the higher cytotoxic activity, than that of at 3-position, and the presence of the copper in the complexes increases the cytotoxicity. Based on toxicity classification data, the compounds with non-toxic profile were identified, which can be used as new entities in the drug development process using Schiff base scaffold.

Synthesis, characterization and toxicity studies of pyridinecarboxaldehydes and L-tryptophan derived Schiff bases and corresponding copper (II) complexes

PubMed Central

Malakyan, Margarita; Babayan, Nelly; Grigoryan, Ruzanna; Sarkisyan, Natalya; Tonoyan, Vahan; Tadevosyan, Davit; Matosyan, Vladimir; Aroutiounian, Rouben; Arakelyan, Arsen

2016-01-01

Schiff bases and their metal-complexes are versatile compounds exhibiting a broad range of biological activities and thus actively used in the drug development process. The aim of the present study was the synthesis and characterization of new Schiff bases and their copper (II) complexes, derived from L-tryptophan and isomeric (2-; 3-; 4-) pyridinecarboxaldehydes, as well as the assessment of their toxicity in vitro. The optimal conditions of the Schiff base synthesis resulting in up to 75-85% yield of target products were identified. The structure-activity relationship analysis indicated that the location of the carboxaldehyde group at 2-, 3- or 4-position with regard to nitrogen of the pyridine ring in aldehyde component of the L-tryptophan derivative Schiff bases and corresponding copper complexes essentially change the biological activity of the compounds. The carboxaldehyde group at 2- and 4-positions leads to the higher cytotoxic activity, than that of at 3-position, and the presence of the copper in the complexes increases the cytotoxicity. Based on toxicity classification data, the compounds with non-toxic profile were identified, which can be used as new entities in the drug development process using Schiff base scaffold. PMID:28344771

Conformational Transitions in Molecular Systems

NASA Astrophysics Data System (ADS)

Bachmann, M.; Janke, W.

2008-11-01

Proteins are the "work horses" in biological systems. In almost all functions specific proteins are involved. They control molecular transport processes, stabilize the cell structure, enzymatically catalyze chemical reactions; others act as molecular motors in the complex machinery of molecular synthetization processes. Due to their significance, misfolds and malfunctions of proteins typically entail disastrous diseases, such as Alzheimer's disease and bovine spongiform encephalopathy (BSE). Therefore, the understanding of the trinity of amino acid composition, geometric structure, and biological function is one of the most essential challenges for the natural sciences. Here, we glance at conformational transitions accompanying the structure formation in protein folding processes.

Dancing with Swarms: Utilizing Swarm Intelligence to Build, Investigate, and Control Complex Systems

NASA Astrophysics Data System (ADS)

Jacob, Christian

We are surrounded by a natural world of massively parallel, decentralized biological "information processing" systems, a world that exhibits fascinating emergent properties in many ways. In fact, our very own bodies are the result of emergent patterns, as the development of any multi-cellular organism is determined by localized interactions among an enormous number of cells, carefully orchestrated by enzymes, signalling proteins and other molecular "agents". What is particularly striking about these highly distributed developmental processes is that a centralized control agency is completely absent. This is also the case for many other biological systems, such as termites which build their nests—without an architect that draws a plan, or brain cells evolving into a complex `mind machine'—without an explicit blueprint of a network layout.

SWI/SNF Chromatin-remodeling Factors: Multiscale Analyses and Diverse Functions*

PubMed Central

Euskirchen, Ghia; Auerbach, Raymond K.; Snyder, Michael

2012-01-01

Chromatin-remodeling enzymes play essential roles in many biological processes, including gene expression, DNA replication and repair, and cell division. Although one such complex, SWI/SNF, has been extensively studied, new discoveries are still being made. Here, we review SWI/SNF biochemistry; highlight recent genomic and proteomic advances; and address the role of SWI/SNF in human diseases, including cancer and viral infections. These studies have greatly increased our understanding of complex nuclear processes. PMID:22952240

Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.

PubMed

Bettenbühl, Mario; Rusconi, Marco; Engbert, Ralf; Holschneider, Matthias

2012-01-01

Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.

Advances in downstream processing of biologics - Spectroscopy: An emerging process analytical technology.

PubMed

Rüdt, Matthias; Briskot, Till; Hubbuch, Jürgen

2017-03-24

Process analytical technologies (PAT) for the manufacturing of biologics have drawn increased interest in the last decade. Besides being encouraged by the Food and Drug Administration's (FDA's) PAT initiative, PAT promises to improve process understanding, reduce overall production costs and help to implement continuous manufacturing. This article focuses on spectroscopic tools for PAT in downstream processing (DSP). Recent advances and future perspectives will be reviewed. In order to exploit the full potential of gathered data, chemometric tools are widely used for the evaluation of complex spectroscopic information. Thus, an introduction into the field will be given. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

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

Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

NASA Astrophysics Data System (ADS)

Mehta, Pankaj; Lang, Alex H.; Schwab, David J.

2016-03-01

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.

Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

PubMed Central

Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

PubMed

Beal, Jacob; Lu, Ting; Weiss, Ron

2011-01-01

The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

The degree of mutual anisotropy of biological liquids polycrystalline nets as a parameter in diagnostics and differentiations of hominal inflammatory processes

NASA Astrophysics Data System (ADS)

Angelsky, O. V.; Ushenko, Yu. A.; Balanetska, V. O.

2011-09-01

To characterize the degree of consistency of parameters of the optically uniaxial birefringent protein nets of blood plasma a new parameter - complex degree of mutual anisotropy is suggested. The technique of polarization measuring the coordinate distributions of the complex degree of mutual anisotropy of blood plasma is developed. It is shown that statistic approach to the analysis of the complex degree of mutual anisotropy distributions of blood plasma is effective during the diagnostics and differentiation of an acute inflammatory processes as well as acute and gangrenous appendicitis.

Physical Complexity and Cognitive Evolution

NASA Astrophysics Data System (ADS)

Jedlicka, Peter

Our intuition tells us that there is a general trend in the evolution of nature, a trend towards greater complexity. However, there are several definitions of complexity and hence it is difficult to argue for or against the validity of this intuition. Christoph Adami has recently introduced a novel measure called physical complexity that assigns low complexity to both ordered and random systems and high complexity to those in between. Physical complexity measures the amount of information that an organism stores in its genome about the environment in which it evolves. The theory of physical complexity predicts that evolution increases the amount of `knowledge' an organism accumulates about its niche. It might be fruitful to generalize Adami's concept of complexity to the entire evolution (including the evolution of man). Physical complexity fits nicely into the philosophical framework of cognitive biology which considers biological evolution as a progressing process of accumulation of knowledge (as a gradual increase of epistemic complexity). According to this paradigm, evolution is a cognitive `ratchet' that pushes the organisms unidirectionally towards higher complexity. Dynamic environment continually creates problems to be solved. To survive in the environment means to solve the problem, and the solution is an embodied knowledge. Cognitive biology (as well as the theory of physical complexity) uses the concepts of information and entropy and views the evolution from both the information-theoretical and thermodynamical perspective. Concerning humans as conscious beings, it seems necessary to postulate an emergence of a new kind of knowledge - a self-aware and self-referential knowledge. Appearence of selfreflection in evolution indicates that the human brain reached a new qualitative level in the epistemic complexity.

Automated and miniaturized detection of biological threats with a centrifugal microfluidic system

NASA Astrophysics Data System (ADS)

Mark, D.; van Oordt, T.; Strohmeier, O.; Roth, G.; Drexler, J.; Eberhard, M.; Niedrig, M.; Patel, P.; Zgaga-Griesz, A.; Bessler, W.; Weidmann, M.; Hufert, F.; Zengerle, R.; von Stetten, F.

2012-06-01

The world's growing mobility, mass tourism, and the threat of terrorism increase the risk of the fast spread of infectious microorganisms and toxins. Today's procedures for pathogen detection involve complex stationary devices, and are often too time consuming for a rapid and effective response. Therefore a robust and mobile diagnostic system is required. We present a microstructured LabDisk which performs complex biochemical analyses together with a mobile centrifugal microfluidic device which processes the LabDisk. This portable system will allow fully automated and rapid detection of biological threats at the point-of-need.

Graphics processing units in bioinformatics, computational biology and systems biology.

PubMed

Nobile, Marco S; Cazzaniga, Paolo; Tangherloni, Andrea; Besozzi, Daniela

2017-09-01

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools. © The Author 2016. Published by Oxford University Press.

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/

Visualizing chaperone-assisted protein folding

DOE PAGES

Horowitz, Scott; Salmon, Loïc; Koldewey, Philipp; ...

2016-05-30

We present that challenges in determining the structures of heterogeneous and dynamic protein complexes have greatly hampered past efforts to obtain a mechanistic understanding of many important biological processes. One such process is chaperone-assisted protein folding. Obtaining structural ensembles of chaperone–substrate complexes would ultimately reveal how chaperones help proteins fold into their native state. To address this problem, we devised a new structural biology approach based on X-ray crystallography, termed residual electron and anomalous density (READ). READ enabled us to visualize even sparsely populated conformations of the substrate protein immunity protein 7 (Im7) in complex with the Escherichia coli chaperonemore » Spy, and to capture a series of snapshots depicting the various folding states of Im7 bound to Spy. The ensemble shows that Spy-associated Im7 samples conformations ranging from unfolded to partially folded to native-like states and reveals how a substrate can explore its folding landscape while being bound to a chaperone.« less

Dance expertise modulates visual sensitivity to complex biological movements.

PubMed

Orlandi, Andrea; Zani, Alberto; Proverbio, Alice Mado

2017-09-01

Motor resonance processes that occur when observing an individual perform an action may be modulated by acquired visuomotor expertise. We used the event-related potential (EEG/ERP) technique to investigate the ability to automatically recognize a subtle difference between very similar novel contemporary dance movements. Twelve professional dancers and twelve non-dancers were shown 212 pairs of videos of complex whole-body movements that lasted 3s. The second of each pair was the repetition of the previous movement or a slight variation of it (deviance). The participants were engaged in a secondary attentional task. Modulation of a larger centro-parietal N400 effect and a reduction of the Late Positivity amplitude (repetition suppression effect) were identified in response to deviant stimuli only in the dancers. Source reconstruction (swLORETA) showed activations in biological motion, body and face processing related areas, and fronto-parietal and limbic systems. The current findings provide evidence that acquired dance expertise modifies the ability to visually code whole-body complex movements. Copyright © 2017 Elsevier Ltd. All rights reserved.

Connecting qualitative observation and quantitative measurement for enhancing quantitative literacy in plant anatomy course

NASA Astrophysics Data System (ADS)

Nuraeni, E.; Rahmat, A.

2018-05-01

Forming of cognitive schemes of plant anatomy concepts is performed by processing of qualitative and quantitative data obtained from microscopic observations. To enhancing student’s quantitative literacy, strategy of plant anatomy course was modified by adding the task to analyze quantitative data produced by quantitative measurement of plant anatomy guided by material course. Participant in this study was 24 biology students and 35 biology education students. Quantitative Literacy test, complex thinking in plant anatomy test and questioner used to evaluate the course. Quantitative literacy capability data was collected by quantitative literacy test with the rubric from the Association of American Colleges and Universities, Complex thinking in plant anatomy by test according to Marzano and questioner. Quantitative literacy data are categorized according to modified Rhodes and Finley categories. The results showed that quantitative literacy of biology education students is better than biology students.

A Computational Workflow for the Automated Generation of Models of Genetic Designs.

PubMed

Misirli, Göksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S; Densmore, Douglas; Myers, Chris; Wipat, Anil

2018-06-05

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

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

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.

Understanding the biological underpinnings of ecohydrological processes

NASA Astrophysics Data System (ADS)

Huxman, T. E.; Scott, R. L.; Barron-Gafford, G. A.; Hamerlynck, E. P.; Jenerette, D.; Tissue, D. T.; Breshears, D. D.; Saleska, S. R.

2012-12-01

Climate change presents a challenge for predicting ecosystem response, as multiple factors drive both the physical and life processes happening on the land surface and their interactions result in a complex, evolving coupled system. For example, changes in surface temperature and precipitation influence near-surface hydrology through impacts on system energy balance, affecting a range of physical processes. These changes in the salient features of the environment affect biological processes and elicit responses along the hierarchy of life (biochemistry to community composition). Many of these structural or process changes can alter patterns of soil water-use and influence land surface characteristics that affect local climate. Of the many features that affect our ability to predict the future dynamics of ecosystems, it is this hierarchical response of life that creates substantial complexity. Advances in the ability to predict or understand aspects of demography help describe thresholds in coupled ecohydrological system. Disentangling the physical and biological features that underlie land surface dynamics following disturbance are allowing a better understanding of the partitioning of water in the time-course of recovery. Better predicting the timing of phenology and key seasonal events allow for a more accurate description of the full functional response of the land surface to climate. In addition, explicitly considering the hierarchical structural features of life are helping to describe complex time-dependent behavior in ecosystems. However, despite this progress, we have yet to build an ability to fully account for the generalization of the main features of living systems into models that can describe ecohydrological processes, especially acclimation, assembly and adaptation. This is unfortunate, given that many key ecosystem services are functions of these coupled co-evolutionary processes. To date, both the lack of controlled measurements and experimentation has precluded determination of sufficient theoretical development. Understanding the land-surface response and feedback to climate change requires a mechanistic understanding of the coupling of ecological and hydrological processes and an expansion of theory from the life sciences to appropriately contribute to the broader Earth system science goal.

microRNAs Databases: Developmental Methodologies, Structural and Functional Annotations.

PubMed

Singh, Nagendra Kumar

2017-09-01

microRNA (miRNA) is an endogenous and evolutionary conserved non-coding RNA, involved in post-transcriptional process as gene repressor and mRNA cleavage through RNA-induced silencing complex (RISC) formation. In RISC, miRNA binds in complementary base pair with targeted mRNA along with Argonaut proteins complex, causes gene repression or endonucleolytic cleavage of mRNAs and results in many diseases and syndromes. After the discovery of miRNA lin-4 and let-7, subsequently large numbers of miRNAs were discovered by low-throughput and high-throughput experimental techniques along with computational process in various biological and metabolic processes. The miRNAs are important non-coding RNA for understanding the complex biological phenomena of organism because it controls the gene regulation. This paper reviews miRNA databases with structural and functional annotations developed by various researchers. These databases contain structural and functional information of animal, plant and virus miRNAs including miRNAs-associated diseases, stress resistance in plant, miRNAs take part in various biological processes, effect of miRNAs interaction on drugs and environment, effect of variance on miRNAs, miRNAs gene expression analysis, sequence of miRNAs, structure of miRNAs. This review focuses on the developmental methodology of miRNA databases such as computational tools and methods used for extraction of miRNAs annotation from different resources or through experiment. This study also discusses the efficiency of user interface design of every database along with current entry and annotations of miRNA (pathways, gene ontology, disease ontology, etc.). Here, an integrated schematic diagram of construction process for databases is also drawn along with tabular and graphical comparison of various types of entries in different databases. Aim of this paper is to present the importance of miRNAs-related resources at a single place.

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.

Towards an ontological representation of morbidity and mortality in Description Logics.

PubMed

Santana, Filipe; Freitas, Fred; Fernandes, Roberta; Medeiros, Zulma; Schober, Daniel

2012-09-21

Despite the high coverage of biomedical ontologies, very few sound definitions of death can be found. Nevertheless, this concept has its relevance in epidemiology, such as for data integration within mortality notification systems. We here introduce an ontological representation of the complex biological qualities and processes that inhere in organisms transitioning from life to death. We further characterize them by causal processes and their temporal borders. Several representational difficulties were faced, mainly regarding kinds of processes with blurred or fiat borders that change their type in a continuous rather than discrete mode. Examples of such hard to grasp concepts are life, death and its relationships with injuries and diseases. We illustrate an iterative optimization of definitions within four versions of the ontology, so as to stress the typical problems encountered in representing complex biological processes. We point out possible solutions for representing concepts related to biological life cycles, preserving identity of participating individuals, i.e. for a patient in transition from life to death. This solution however required the use of extended description logics not yet supported by tools. We also focus on the interdependencies and need to change further parts if one part is changed. The axiomatic definition of mortality we introduce allows the description of biologic processes related to the transition from healthy to diseased or injured, and up to a final death state. Exploiting such definitions embedded into descriptions of pathogen transmissions by arthropod vectors, the complete sequence of infection and disease processes can be described, starting from the inoculation of a pathogen by a vector, until the death of an individual, preserving the identity of the patient.

Genetic control of root growth: from genes to networks

PubMed Central

Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B.; Ristova, Daniela; Busch, Wolfgang

2016-01-01

Background Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. Scope This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. Conclusions While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. PMID:26558398

Sequence co-evolution gives 3D contacts and structures of protein complexes

PubMed Central

Hopf, Thomas A; Schärfe, Charlotta P I; Rodrigues, João P G L M; Green, Anna G; Kohlbacher, Oliver; Sander, Chris; Bonvin, Alexandre M J J; Marks, Debora S

2014-01-01

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DOI: http://dx.doi.org/10.7554/eLife.03430.001 PMID:25255213

The Diamond Light Source and the challenges ahead for structural biology: some informal remarks.

PubMed

Ramakrishnan, V

2015-03-06

The remarkable advances in structural biology in the past three decades have led to the determination of increasingly complex structures that lie at the heart of many important biological processes. Many of these advances have been made possible by the use of X-ray crystallography using synchrotron radiation. In this short article, some of the challenges and prospects that lie ahead will be summarized. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Visualizing land-use and management complexity within biogeochemical cycles of an agricultural landscape

Treesearch

Kai Nils Nitzsche; Gernot Verch; Katrin Premke; Arthur Gessler; Zachary Kayler

2016-01-01

Crop fields are cultivated across continuities of soil, topography, and local climate that drive biological processes and nutrient cycling at the landscape scale; yet land management and agricultural research are often performed at the field scale, potentially neglecting the context of the surrounding landscape. Adding to this complexity is the overlap of ecosystems...

Sequential chemical-biological processes for the treatment of industrial wastewaters: review of recent progresses and critical assessment.

PubMed

Guieysse, Benoit; Norvill, Zane N

2014-02-28

When direct wastewater biological treatment is unfeasible, a cost- and resource-efficient alternative to direct chemical treatment consists of combining biological treatment with a chemical pre-treatment aiming to convert the hazardous pollutants into more biodegradable compounds. Whereas the principles and advantages of sequential treatment have been demonstrated for a broad range of pollutants and process configurations, recent progresses (2011-present) in the field provide the basis for refining assessment of feasibility, costs, and environmental impacts. This paper thus reviews recent real wastewater demonstrations at pilot and full scale as well as new process configurations. It also discusses new insights on the potential impacts of microbial community dynamics on process feasibility, design and operation. Finally, it sheds light on a critical issue that has not yet been properly addressed in the field: integration requires complex and tailored optimization and, of paramount importance to full-scale application, is sensitive to uncertainty and variability in the inputs used for process design and operation. Future research is therefore critically needed to improve process control and better assess the real potential of sequential chemical-biological processes for industrial wastewater treatment. Copyright © 2013 Elsevier B.V. All rights reserved.

Sowing the Seeds of Creativity

ERIC Educational Resources Information Center

Briten, Elizabeth

2006-01-01

The exciting world of plants may be something of a mystery to many children, and the often-dry content of a curriculum taught indoors inhibits real understanding of many complex biological processes. Moving outdoors opens up an unexplored world and presents rich opportunities for imaginative learning. The "Life processes and living…

Biology Diagrams: Tools To Think With.

ERIC Educational Resources Information Center

Kindfield, Ann C. H.

Subcellular processes like meiosis are frequently problematic for learners because they are complex and, except for the extent that they can be observed under a light microscope, occur outside of our direct experience. More detailed characterization of what underlies various degrees of student understanding of a process is required to more fully…

Making the invisible visible.

PubMed

van Steensel, Maurice A M

2016-04-01

In this review, I will discuss how careful scrutiny of genetic skin disorders could help us to understand human biology. Like other organs, the skin and its appendages, such as hairs and teeth, experience fundamental biological processes ranging from lipid metabolism to vesicular transport and cellular migration. However, in contrast to other organ systems, they are accessible and can be studied with relative ease. By visually revealing the functional consequences of single gene defects, genetic skin diseases offer a unique opportunity to study human biology. Here, I will illustrate this concept by discussing how human genetic disorders of skin pigmentation reflect the mechanisms underlying this complex and vital process. Copyright © 2016 Elsevier Ltd. All rights reserved.

The cell biology of inflammasomes: Mechanisms of inflammasome activation and regulation

PubMed Central

2016-01-01

Over the past decade, numerous advances have been made in the role and regulation of inflammasomes during pathogenic and sterile insults. An inflammasome complex comprises a sensor, an adaptor, and a zymogen procaspase-1. The functional output of inflammasome activation includes secretion of cytokines, IL-1β and IL-18, and induction of an inflammatory form of cell death called pyroptosis. Recent studies have highlighted the intersection of this inflammatory response with fundamental cellular processes. Novel modulators and functions of inflammasome activation conventionally associated with the maintenance of homeostatic biological functions have been uncovered. In this review, we discuss the biological processes involved in the activation and regulation of the inflammasome. PMID:27325789

Brushing Your Spacecrafts Teeth: A Review of Biological Reduction Processes for Planetary Protection Missions

NASA Technical Reports Server (NTRS)

Pugel, D. E. (Betsy); Rummel, J. D.; Conley, Catharine

2017-01-01

Much like keeping your teeth clean, where you brush away biofilms that your dentist calls "plaque," there are various methods to clean spaceflight hardware of biological contamination, known as biological reduction processes. Different approaches clean your hardware's "teeth" in different ways and with different levels of effectiveness. We know that brushing at home with a simple toothbrush is convenient and has a different level of impact vs. getting your teeth cleaned at the dentist. In the same way, there are some approaches to biological reduction that may require simple tools or more complex implementation approaches (think about sonicating or just soaking your dentures, vs. brushing them). There are also some that are more effective for different degrees of cleanliness and still some that have materials compatibility concerns. In this article, we review known and NASA-certified approaches for biological reduction, pointing out materials compatibility concerns and areas where additional research is needed.

Brushing Your Spacecrafts Teeth: A Review of Biological Reduction Processes for Planetary Protection Missions

NASA Technical Reports Server (NTRS)

Pugel, D.E. (Betsy); Rummel, J. D.; Conley, C. A.

2017-01-01

Much like keeping your teeth clean, where you brush away biofilms that your dentist calls plaque, there are various methods to clean spaceflight hardware of biological contamination, known as biological reduction processes. Different approaches clean your hardwares teeth in different ways and with different levels of effectiveness. We know that brushing at home with a simple toothbrush is convenient and has a different level of impact vs. getting your teeth cleaned at the dentist. In the same way, there are some approaches to biological reduction that may require simple tools or more complex implementation approaches (think about sonicating or just soaking your dentures, vs. brushing them). There are also some that are more effective for different degrees of cleanliness and still some that have materials compatibility concerns. In this article, we review known and NASA-certified approaches for biological reduction, pointing out materials compatibility concerns and areas where additional research is needed.

Learning cell biology as a team: a project-based approach to upper-division cell biology.

PubMed

Wright, Robin; Boggs, James

2002-01-01

To help students develop successful strategies for learning how to learn and communicate complex information in cell biology, we developed a quarter-long cell biology class based on team projects. Each team researches a particular human disease and presents information about the cellular structure or process affected by the disease, the cellular and molecular biology of the disease, and recent research focused on understanding the cellular mechanisms of the disease process. To support effective teamwork and to help students develop collaboration skills useful for their future careers, we provide training in working in small groups. A final poster presentation, held in a public forum, summarizes what students have learned throughout the quarter. Although student satisfaction with the course is similar to that of standard lecture-based classes, a project-based class offers unique benefits to both the student and the instructor.

The Effectiveness of an Online Curriculum on High School Students' Understanding of Biological Evolution

NASA Astrophysics Data System (ADS)

Marsteller, Robert B.; Bodzin, Alec M.

2015-12-01

An online curriculum about biological evolution was designed to promote increased student content knowledge and evidentiary reasoning. A feasibility study was conducted with 77 rural high school biology students who learned with the online biological evolution unit. Data sources included the Biological Evolution Assessment Measure (BEAM), an analysis of discussion forum posts, and a post-implementation perceptions and attitudes questionnaire. BEAM posttest scores were significantly higher than the pretest scores. However, the findings revealed that the students required additional support to develop evidentiary reasoning. Many students perceived that the Web-based curriculum would have been enhanced by increased immediate interaction and feedback. Students required greater scaffolding to support complex, process-oriented tasks. Implications for designing Web-based science instruction with curriculum materials to support students' acquisition of content knowledge and science process skills in a Web-based setting are discussed.

Functional Genomics Assistant (FUGA): a toolbox for the analysis of complex biological networks

PubMed Central

2011-01-01

Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA) - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga. PMID:22035155

The Evolution of Biological Complexity in Digital Organisms

NASA Astrophysics Data System (ADS)

Ofria, Charles

2013-03-01

When Darwin first proposed his theory of evolution by natural selection, he realized that it had a problem explaining the origins of traits of ``extreme perfection and complication'' such as the vertebrate eye. Critics of Darwin's theory have latched onto this perceived flaw as a proof that Darwinian evolution is impossible. In anticipation of this issue, Darwin described the perfect data needed to understand this process, but lamented that such data are ``scarcely ever possible'' to obtain. In this talk, I will discuss research where we use populations of digital organisms (self-replicating and evolving computer programs) to elucidate the genetic and evolutionary processes by which new, highly-complex traits arise, drawing inspiration directly from Darwin's wistful thinking and hypotheses. During the process of evolution in these fully-transparent computational environments we can measure the incorporation of new information into the genome, a process akin to a natural Maxwell's Demon, and identify the original source of any such information. We show that, as Darwin predicted, much of the information used to encode a complex trait was already in the genome as part of simpler evolved traits, and that many routes must be possible for a new complex trait to have a high probability of successfully evolving. In even more extreme examples of the evolution of complexity, we are now using these same principles to examine the evolutionary dynamics the drive major transitions in evolution; that is transitions to higher-levels of organization, which are some of the most complex evolutionary events to occur in nature. Finally, I will explore some of the implications of this research to other aspects of evolutionary biology and as well as ways that these evolutionary principles can be applied toward solving computational and engineering problems.

A fluorescence turn-on sensor for iodide based on a thymine-Hg(II)-thymine complex.

PubMed

Ma, Boling; Zeng, Fang; Zheng, Fangyuan; Wu, Shuizhu

2011-12-23

Iodide plays a vital role in many biological processes, including neurological activity and thyroid function. Due to its physiological relevance, a method for the rapid, sensitive, and selective detection of iodide in food, pharmaceutical products, and biological samples such as urine is of great importance. Herein, we demonstrate a novel and facile strategy for constructing a fluorescence turn-on sensor for iodide based on a T-Hg(II)-T complex (T=thymine). A fluorescent anthracene-thymine dyad (An-T) was synthesized, the binding of which to a mercury(II) ion lead to the formation of a An-T-Hg(II)-T-An complex, thereby quenching the fluorescent emission of this dyad. In this respect, the dyad An-T constituted a fluorescence turn-off sensor for mercury(II) ions in aqueous media. More importantly, it was found that upon addition of iodide, the mercury(II) ion was extracted from the complex due to the even stronger binding between mercury(II) ions and iodide, leading to the release of the free dyad and restoration of the fluorescence. By virtue of this fluorescence quenching and recovery process, the An-T-Hg(II)-T-An complex constitutes a fluorescence turn-on sensor for iodide with a detection limit of 126 nM. Moreover, this sensor is highly selective for iodide over other common anions, and can be used in the determination of iodide in drinking water and biological samples such as urine. This strategy may provide a new approach for sensing some other anions. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biological network extraction from scientific literature: state of the art and challenges.

PubMed

Li, Chen; Liakata, Maria; Rebholz-Schuhmann, Dietrich

2014-09-01

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

On the sufficiency of pairwise interactions in maximum entropy models of networks

NASA Astrophysics Data System (ADS)

Nemenman, Ilya; Merchan, Lina

Biological information processing networks consist of many components, which are coupled by an even larger number of complex multivariate interactions. However, analyses of data sets from fields as diverse as neuroscience, molecular biology, and behavior have reported that observed statistics of states of some biological networks can be approximated well by maximum entropy models with only pairwise interactions among the components. Based on simulations of random Ising spin networks with p-spin (p > 2) interactions, here we argue that this reduction in complexity can be thought of as a natural property of some densely interacting networks in certain regimes, and not necessarily as a special property of living systems. This work was supported in part by James S. McDonnell Foundation Grant No. 220020321.

Drosophila melanogaster--the model organism of choice for the complex biology of multi-cellular organisms

NASA Technical Reports Server (NTRS)

Beckingham, Kathleen M.; Armstrong, J. Douglas; Texada, Michael J.; Munjaal, Ravi; Baker, Dean A.

2005-01-01

Drosophila melanogaster has been intensely studied for almost 100 years. The sophisticated array of genetic and molecular tools that have evolved for analysis of gene function in this organism are unique. Further, Drosophila is a complex multi-cellular organism in which many aspects of development and behavior parallel those in human beings. These combined advantages have permitted research in Drosophila to make seminal contributions to the understanding of fundamental biological processes and ensure that Drosophila will continue to provide unique insights in the genomic era. An overview of the genetic methodologies available in Drosophila is given here, together with examples of outstanding recent contributions of Drosophila to our understanding of cell and organismal biology. The growing contribution of Drosophila to our knowledge of gravity-related responses is addressed.

Supported inhibitor for fishing lipases in complex biological media and mass spectrometry identification.

PubMed

Delorme, Vincent; Raux, Brigitt; Puppo, Rémy; Leclaire, Julien; Cavalier, Jean-François; Marc, Sylvain; Kamarajugadda, Pavan-Kumar; Buono, Gérard; Fotiadu, Frédéric; Canaan, Stéphane; Carrière, Frédéric

2014-12-01

A synthetic phosphonate inhibitor designed for lipase inhibition but displaying a broader range of activity was covalently immobilized on a solid support to generate a function-directed tool targeting serine hydrolases. To achieve this goal, straightforward and reliable analytical techniques were developed, allowing the monitoring of the solid support's chemical functionalization, enzyme capture processes and physisorption artifacts. This grafted inhibitor was tested on pure lipases and serine proteases from various origins, and assayed for the selective capture of lipases from several complex biological extracts. The direct identification of captured enzymes by mass spectrometry brought the proof of concept on the efficiency of this supported covalent inhibitor. The features and limitations of this "enzyme-fishing" proteomic tool provide new insight on solid-liquid inhibition process. Copyright © 2014. Published by Elsevier B.V.

Understanding Randomness and its Impact on Student Learning: Lessons Learned from Building the Biology Concept Inventory (BCI)

PubMed Central

Garvin-Doxas, Kathy

2008-01-01

While researching student assumptions for the development of the Biology Concept Inventory (BCI; http://bioliteracy.net), we found that a wide class of student difficulties in molecular and evolutionary biology appears to be based on deep-seated, and often unaddressed, misconceptions about random processes. Data were based on more than 500 open-ended (primarily) college student responses, submitted online and analyzed through our Ed's Tools system, together with 28 thematic and think-aloud interviews with students, and the responses of students in introductory and advanced courses to questions on the BCI. Students believe that random processes are inefficient, whereas biological systems are very efficient. They are therefore quick to propose their own rational explanations for various processes, from diffusion to evolution. These rational explanations almost always make recourse to a driver, e.g., natural selection in evolution or concentration gradients in molecular biology, with the process taking place only when the driver is present, and ceasing when the driver is absent. For example, most students believe that diffusion only takes place when there is a concentration gradient, and that the mutational processes that change organisms occur only in response to natural selection pressures. An understanding that random processes take place all the time and can give rise to complex and often counterintuitive behaviors is almost totally absent. Even students who have had advanced or college physics, and can discuss diffusion correctly in that context, cannot make the transfer to biological processes, and passing through multiple conventional biology courses appears to have little effect on their underlying beliefs. PMID:18519614

Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering.

PubMed

Teixeira, Ana P; Carinhas, Nuno; Dias, João M L; Cruz, Pedro; Alves, Paula M; Carrondo, Manuel J T; Oliveira, Rui

2007-12-01

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.

The BioPlex Network: A Systematic Exploration of the Human Interactome.

PubMed

Huttlin, Edward L; Ting, Lily; Bruckner, Raphael J; Gebreab, Fana; Gygi, Melanie P; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E; De Camilli, Pietro; Paulo, Joao A; Harper, J Wade; Gygi, Steven P

2015-07-16

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors. Copyright © 2015 Elsevier Inc. All rights reserved.

The BioPlex Network: A Systematic Exploration of the Human Interactome

PubMed Central

Huttlin, Edward L.; Ting, Lily; Bruckner, Raphael J.; Gebreab, Fana; Gygi, Melanie P.; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K.; Wühr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A.; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E.; DeCamilli, Pietro; Paulo, Joao A.; Harper, J. Wade; Gygi, Steven P.

2015-01-01

SUMMARY Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally-related proteins. Finally, BioPlex, in combination with other approaches can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial Amyotrophic Lateral Sclerosis perturb a defined community of interactors. PMID:26186194

Thermal Quantum Correlations in Photosynthetic Light-Harvesting Complexes

NASA Astrophysics Data System (ADS)

Mahdian, M.; Kouhestani, H.

2015-08-01

Photosynthesis is one of the ancient biological processes, playing crucial role converting solar energy to cellular usable currency. Environmental factors and external perturbations has forced nature to choose systems with the highest efficiency and performance. Recent theoretical and experimental studies have proved the presence of quantum properties in biological systems. Energy transfer systems like Fenna-Matthews-Olson (FMO) complex shows quantum entanglement between sites of Bacteriophylla molecules in protein environment and presence of decoherence. Complex biological systems implement more truthful mechanisms beside chemical-quantum correlations to assure system's efficiency. In this study we investigate thermal quantum correlations in FMO protein of the photosynthetic apparatus of green sulfur bacteria by quantum discord measure. The results confirmed existence of remarkable quantum correlations of of BChla pigments in room temperature. This results approve involvement of quantum correlation mechanisms for information storage and retention in living organisms that could be useful for further evolutionary studies. Inspired idea of this study is potentially interesting to practice by the same procedure in genetic data transfer mechanisms.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

PubMed Central

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh; Webb-Robertson, Bobbie-Jo; Hafen, Ryan; Ramey, John; Rodland, Karin D.

2012-01-01

Introduction The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers. PMID:23335946

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

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

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh D.

2013-01-01

The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges thatmore » have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.« less

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

Hess, Nancy J.; Pasa-Tolic, Ljiljana; Bailey, Vanessa L.

Understanding the role played by microorganisms within soil systems is challenged by the unique intersection of physics, chemistry, mineralogy and biology in fostering habitat for soil microbial communities. To address these challenges will require observations across multiple spatial and temporal scales to capture the dynamics and emergent behavior from complex and interdependent processes. The heterogeneity and complexity of the rhizosphere require advanced techniques that press the simultaneous frontiers of spatial resolution, analyte sensitivity and specificity, reproducibility, large dynamic range, and high throughput. Fortunately many exciting technical advancements are now available to inform and guide the development of new hypotheses. Themore » aim of this Special issue is to provide a holistic view of the rhizosphere in the perspective of modern molecular biology methodologies that enabled a highly-focused, detailed view on the processes in the rhizosphere, including numerous, strong and complex interactions between plant roots, soil constituents and microorganisms. We discuss the current rhizosphere research challenges and knowledge gaps, as well as perspectives and approaches using newly available state-of-the-art toolboxes. These new approaches and methodologies allow the study of rhizosphere processes and properties, and rhizosphere as a central component of ecosystems and biogeochemical cycles.« less

Mathematics for understanding disease.

PubMed

Bies, R R; Gastonguay, M R; Schwartz, S L

2008-06-01

The application of mathematical models to reflect the organization and activity of biological systems can be viewed as a continuum of purpose. The far left of the continuum is solely the prediction of biological parameter values, wherein an understanding of the underlying biological processes is irrelevant to the purpose. At the far right of the continuum are mathematical models, the purposes of which are a precise understanding of those biological processes. No models in present use fall at either end of the continuum. Without question, however, the emphasis in regards to purpose has been on prediction, e.g., clinical trial simulation and empirical disease progression modeling. Clearly the model that ultimately incorporates a universal understanding of biological organization will also precisely predict biological events, giving the continuum the logical form of a tautology. Currently that goal lies at an immeasurable distance. Nonetheless, the motive here is to urge movement in the direction of that goal. The distance traveled toward understanding naturally depends upon the nature of the scientific question posed with respect to comprehending and/or predicting a particular disease process. A move toward mathematical models implies a move away from static empirical modeling and toward models that focus on systems biology, wherein modeling entails the systematic study of the complex pattern of organization inherent in biological systems.

Biomedically relevant chemical and physical properties of coal combustion products.

PubMed Central

Fisher, G L

1983-01-01

The evaluation of the potential public and occupational health hazards of developing and existing combustion processes requires a detailed understanding of the physical and chemical properties of effluents available for human and environmental exposures. These processes produce complex mixtures of gases and aerosols which may interact synergistically or antagonistically with biological systems. Because of the physicochemical complexity of the effluents, the biomedically relevant properties of these materials must be carefully assessed. Subsequent to release from combustion sources, environmental interactions further complicate assessment of the toxicity of combustion products. This report provides an overview of the biomedically relevant physical and chemical properties of coal fly ash. Coal fly ash is presented as a model complex mixture for health and safety evaluation of combustion processes. PMID:6337824

A taxonomy of visualization tasks for the analysis of biological pathway data.

PubMed

Murray, Paul; McGee, Fintan; Forbes, Angus G

2017-02-15

Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. Here, we present taxonomy of tasks that are regularly performed by researchers who work with biological pathway data. The generation of these tasks was done in conjunction with interviews with several domain experts in biology. These tasks require further classification than is provided by existing taxonomies. We also examine existing visualization techniques that support each task, and we discuss gaps in the existing visualization space revealed by our taxonomy. Our taxonomy is designed to support the development and design of future biological pathway visualization applications. We conclude by suggesting future research directions based on our taxonomy and motivated by the comments received by our domain experts.

Integrative mental health care: from theory to practice, Part 2.

PubMed

Lake, James

2008-01-01

Integrative approaches will lead to more accurate and different understandings of mental illness. Beneficial responses to complementary and alternative therapies provide important clues about the phenomenal nature of the human body in space-time and disparate biological, informational, and energetic factors associated with normal and abnormal psychological functioning. The conceptual framework of contemporary Western psychiatry includes multiple theoretical viewpoints, and there is no single best explanatory model of mental illness. Future theories of mental illness causation will not depend exclusively on empirical verification of strictly biological processes but will take into account both classically described biological processes and non-classical models, including complexity theory, resulting in more complete explanations of the characteristics and causes of symptoms and mechanisms of action that result in beneficial responses to treatments. Part 1 of this article examined the limitations of the theory and contemporary clinical methods employed in Western psychiatry and discussed implications of emerging paradigms in physics and the biological sciences for the future of psychiatry. In part 2, a practical methodology, for planning integrative assessment and treatment strategies in mental health care is proposed. Using this methodology the integrative management of moderate and severe psychiatric symptoms is reviewed in detail. As the conceptual framework of Western medicine evolves toward an increasingly integrative perspective, novel understanding of complex relationships between biological, informational, and energetic processes associated with normal psychological functioning and mental illness will lead to more effective integrative assessment and treatment strategies addressing the causes or meanings of symptoms at multiple hierarchic levels of body-brain-mind.

Integrative mental health care: from theory to practice, part 1.

PubMed

Lake, James

2007-01-01

Integrative approaches will lead to more accurate and different understandings of mental illness. Beneficial responses to complementary and alternative therapies provide important clues about the phenomenal nature of the human body in space-time and disparate biological, informational, and energetic factors associated with normal and abnormal psychological functioning. The conceptual framework of contemporary Western psychiatry includes multiple theoretical viewpoints, and there is no single best explanatory model of mental illness. Future theories of mental illness causation will not depend exclusively on empirical verification of strictly biological processes but will take into account both classically described biological processes and non-classical models, including complexity theory, resulting in more complete explanations of the characteristics and causes of symptoms and mechanisms of action that result in beneficial responses to treatments. Part 1 of this article examines the limitations of the theory and contemporary clinical methods employed in Western psychiatry and discusses implications of emerging paradigms in physics and the biological sciences for the future of psychiatry. In part 2, a practical methodology for planning integrative assessment and treatment strategies in mental health care is proposed. Using this methodology the integrative management of moderate and severe psychiatric symptoms is reviewed in detail. As the conceptual framework of Western medicine evolves toward an increasingly integrative perspective, novel understandings of complex relationships between biological, informational, and energetic processes associated with normal psychological functioning and mental illness will lead to more effective integrative assessment and treatment strategies addressing the causes or meanings of symptoms at multiple hierarchic levels of body-brain-mind.

Towards human-computer synergetic analysis of large-scale biological data.

PubMed

Singh, Rahul; Yang, Hui; Dalziel, Ben; Asarnow, Daniel; Murad, William; Foote, David; Gormley, Matthew; Stillman, Jonathan; Fisher, Susan

2013-01-01

Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/.

Towards human-computer synergetic analysis of large-scale biological data

PubMed Central

2013-01-01

Background Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. Results In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. Conclusions The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/. PMID:24267485

Chemistry meets biology in colitis-associated carcinogenesis

PubMed Central

Mangerich, Aswin; Dedon, Peter C.; Fox, James G.; Tannenbaum, Steven R.; Wogan, Gerald N.

2015-01-01

The intestine comprises an exceptional venue for a dynamic and complex interplay of numerous chemical and biological processes. Here, multiple chemical and biological systems, including the intestinal tissue itself, its associated immune system, the gut microbiota, xenobiotics, and metabolites meet and interact to form a sophisticated and tightly regulated state of tissue homoeostasis. Disturbance of this homeostasis can cause inflammatory bowel disease (IBD) – a chronic disease of multifactorial etiology that is strongly associated with increased risk for cancer development. This review addresses recent developments in research into chemical and biological mechanisms underlying the etiology of inflammation-induced colon cancer. Beginning with a general overview of reactive chemical species generated during colonic inflammation, the mechanistic interplay between chemical and biological mediators of inflammation, the role of genetic toxicology and microbial pathogenesis in disease development are discussed. When possible, we systematically compare evidence from studies utilizing human IBD patients with experimental investigations in mice. The comparison reveals that many strong pathological and mechanistic correlates exist between mouse models of colitis-associated cancer, and the clinically relevant situation in humans. We also summarize several emerging issues in the field, such as the carcinogenic potential of novel inflammation-related DNA adducts and genotoxic microbial factors, the systemic dimension of inflammation-induced genotoxicity, and the complex role of genome maintenance mechanisms during these processes. Taken together, current evidence points to the induction of genetic and epigenetic alterations by chemical and biological inflammatory stimuli ultimately leading to cancer formation. PMID:23926919

Conferring biological activity to native spider silk: A biofunctionalized protein-based microfiber.

PubMed

Wu, Hsuan-Chen; Quan, David N; Tsao, Chen-Yu; Liu, Yi; Terrell, Jessica L; Luo, Xiaolong; Yang, Jen-Chang; Payne, Gregory F; Bentley, William E

2017-01-01

Spider silk is an extraordinary material with physical properties comparable to the best scaffolding/structural materials, and as a fiber it can be manipulated with ease into a variety of configurations. Our work here demonstrates that natural spider silk fibers can also be used to organize biological components on and in devices through rapid and simple means. Micron scale spider silk fibers (5-10 μm in diameter) were surface modified with a variety of biological entities engineered with pentaglutamine tags via microbial transglutaminase (mTG). Enzymes, enzyme pathways, antibodies, and fluorescent proteins were all assembled onto spider silk fibers using this biomolecular engineering/biofabrication process. Additionally, arrangement of biofunctionalized fiber should in of itself generate a secondary level of biomolecular organization. Toward this end, as proofs of principle, spatially defined arrangement of biofunctionalized spider silk fiber was shown to generate effects specific to silk position in two cases. In one instance, arrangement perpendicular to a flow produced selective head and neck carcinoma cell capture on silk with antibodies complexed to conjugated protein G. In a second scenario, asymmetric bacterial chemotaxis arose from asymmetric conjugation of enzymes to arranged silk. Overall, the biofabrication processes used here were rapid, required no complex chemistries, were biologically benign, and also the resulting engineered silk microfibers were flexible, readily manipulated and functionally active. Deployed here in microfluidic environments, biofunctional spider silk fiber provides a means to convey complex biological functions over a range of scales, further extending its potential as a biomaterial in biotechnological settings. Biotechnol. Bioeng. 2017;114: 83-95. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions

PubMed Central

Khalil, Ahmad S.; Lu, Timothy K.; Bashor, Caleb J.; Ramirez, Cherie L.; Pyenson, Nora C.; Joung, J. Keith; Collins, James J.

2013-01-01

SUMMARY Eukaryotic transcription factors (TFs) perform complex and combinatorial functions within transcriptional networks. Here, we present a synthetic framework for systematically constructing eukaryotic transcription functions using artificial zinc fingers, modular DNA-binding domains found within many eukaryotic TFs. Utilizing this platform, we construct a library of orthogonal synthetic transcription factors (sTFs) and use these to wire synthetic transcriptional circuits in yeast. We engineer complex functions, such as tunable output strength and transcriptional cooperativity, by rationally adjusting a decomposed set of key component properties, e.g., DNA specificity, affinity, promoter design, protein-protein interactions. We show that subtle perturbations to these properties can transform an individual sTF between distinct roles (activator, cooperative factor, inhibitory factor) within a transcriptional complex, thus drastically altering the signal processing behavior of multi-input systems. This platform provides new genetic components for synthetic biology and enables bottom-up approaches to understanding the design principles of eukaryotic transcriptional complexes and networks. PMID:22863014

Novel strategies to construct complex synthetic vectors to produce DNA molecular weight standards.

PubMed

Chen, Zhe; Wu, Jianbing; Li, Xiaojuan; Ye, Chunjiang; Wenxing, He

2009-05-01

DNA molecular weight standards (DNA markers, nucleic acid ladders) are commonly used in molecular biology laboratories as references to estimate the size of various DNA samples in electrophoresis process. One method of DNA marker production is digestion of synthetic vectors harboring multiple DNA fragments of known sizes by restriction enzymes. In this article, we described three novel strategies-sequential DNA fragment ligation, screening of ligation products by polymerase chain reaction (PCR) with end primers, and "small fragment accumulation"-for constructing complex synthetic vectors and minimizing the mass differences between DNA fragments produced from restrictive digestion of synthetic vectors. The strategy could be applied to construct various complex synthetic vectors to produce any type of low-range DNA markers, usually available commercially. In addition, the strategy is useful for single-step ligation of multiple DNA fragments for construction of complex synthetic vectors and other applications in molecular biology field.

Constituent bioconcentration in rainbow trout exposed to a complex chemical mixture

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

Linder, G.; Bergman, H.L.; Meyer, J.S.

1984-09-01

Classically, aquatic contaminant fate models predicting a chemical's bioconcentration factor (BCF) are based upon single-compound derived models, yet such BCF predictions may deviate from observed BCFs when physicochemical interactions or biological responses to complex chemical mixture exposures are not adequately considered in the predictive model. Rainbow trout were exposed to oil-shale retort waters. Such a study was designed to model the potential biological effects precluded by exposure to complex chemical mixtures such as solid waste leachates, agricultural runoff, and industrial process waste waters. Chromatographic analysis of aqueous and nonaqueous liquid-liquid reservoir components yielded differences in mixed extraction solvent HPLC profilesmore » of whole fish exposed for 1 and 3 weeks to the highest dilution of the complex chemical mixture when compared to their corresponding control, yet subsequent whole fish extractions at 6, 9, 12, and 15 weeks into exposure demonstrated no qualitative differences between control and exposed fish. Liver extractions and deproteinized bile samples from exposed fish were qualitatively different than their corresponding controls. These findings support the projected NOEC of 0.0045% dilution, even though the differences in bioconcentration profiles suggest hazard assessment strategies may be useful in evaluating environmental fate processes associated with complex chemical mixtures. 12 references, 4 figures, 2 tables.« less

NMR studies of protein-nucleic acid interactions.

PubMed

Varani, Gabriele; Chen, Yu; Leeper, Thomas C

2004-01-01

Protein-DNA and protein-RNA complexes play key functional roles in every living organism. Therefore, the elucidation of their structure and dynamics is an important goal of structural and molecular biology. Nuclear magnetic resonance (NMR) studies of protein and nucleic acid complexes have common features with studies of protein-protein complexes: the interaction surfaces between the molecules must be carefully delineated, the relative orientation of the two species needs to be accurately and precisely determined, and close intermolecular contacts defined by nuclear Overhauser effects (NOEs) must be obtained. However, differences in NMR properties (e.g., chemical shifts) and biosynthetic pathways for sample productions generate important differences. Chemical shift differences between the protein and nucleic acid resonances can aid the NMR structure determination process; however, the relatively limited dispersion of the RNA ribose resonances makes the process of assigning intermolecular NOEs more difficult. The analysis of the resulting structures requires computational tools unique to nucleic acid interactions. This chapter summarizes the most important elements of the structure determination by NMR of protein-nucleic acid complexes and their analysis. The main emphasis is on recent developments (e.g., residual dipolar couplings and new Web-based analysis tools) that have facilitated NMR studies of these complexes and expanded the type of biological problems to which NMR techniques of structural elucidation can now be applied.

Time dependent-density functional theory (TD-DFT) and experimental studies of UV-Visible spectra and cyclic voltammetry for Cu(II) complex with Et2DTC

NASA Astrophysics Data System (ADS)

Valle, Eliana Maira A.; Maltarollo, Vinicius Gonçalves; Almeida, Michell O.; Honorio, Kathia Maria; dos Santos, Mauro Coelho; Cerchiaro, Giselle

2018-04-01

In this work, we studied the complexation mode between copper(II) ion and the specific ligand investigated as carriers of metals though biological membranes, diethyldithiocarbamate (Et2DTC). It is important to understand how this occurs because it is an important intracellular chelator with potential therapeutic applications. Theoretical and experimental UV visible studies were performed to investigate the complexation mode between copper and the ligand. Electrochemical studies were also performed to complement the spectroscopic analyses. According to the theoretical calculations, using TD-DFT (Time dependent density functional theory), with B3LYP functional and DGDVZP basis set, implemented in Gaussian 03 package, it was observed that the formation of the complex [Cu(Et2DTC)2] is favorable with higher electron density over the sulfur atoms of the ligand. UV/Vis spectra have a charge transfer band at 450 nm, with the DMSO-d6 band shift from 800 to 650 nm. The electrochemical experiments showed the formation of a new redox process, referring to the complex, where the reduction peak potential of copper is displaced to less positive region. Therefore, the results obtained from this study give important insights on possible mechanisms involved in several biological processes related to the studied system.

Structural Information Inference from Lanthanoid Complexing Systems: Photoluminescence Studies on Isolated Ions

NASA Astrophysics Data System (ADS)

Greisch, Jean Francois; Harding, Michael E.; Chmela, Jiri; Klopper, Willem M.; Schooss, Detlef; Kappes, Manfred M.

2016-06-01

The application of lanthanoid complexes ranges from photovoltaics and light-emitting diodes to quantum memories and biological assays. Rationalization of their design requires a thorough understanding of intramolecular processes such as energy transfer, charge transfer, and non-radiative decay involving their subunits. Characterization of the excited states of such complexes considerably benefits from mass spectrometric methods since the associated optical transitions and processes are strongly affected by stoichiometry, symmetry, and overall charge state. We report herein spectroscopic measurements on ensembles of ions trapped in the gas phase and soft-landed in neon matrices. Their interpretation is considerably facilitated by direct comparison with computations. The combination of energy- and time-resolved measurements on isolated species with density functional as well as ligand-field and Franck-Condon computations enables us to infer structural as well as dynamical information about the species studied. The approach is first illustrated for sets of model lanthanoid complexes whose structure and electronic properties are systematically varied via the substitution of one component (lanthanoid or alkali,alkali-earth ion): (i) systematic dependence of ligand-centered phosphorescence on the lanthanoid(III) promotion energy and its impact on sensitization, and (ii) structural changes induced by the substitution of alkali or alkali-earth ions in relation with structures inferred using ion mobility spectroscopy. The temperature dependence of sensitization is briefly discussed. The focus is then shifted to measurements involving europium complexes with doxycycline an antibiotic of the tetracycline family. Besides discussing the complexes' structural and electronic features, we report on their use to monitor enzymatic processes involving hydrogen peroxide or biologically relevant molecules such as adenosine triphosphate (ATP).

What experimental approaches (eg, in vivo, in vitro, tissue retrieval) are effective in investigating the biologic effects of particles?

PubMed Central

Bostrom, Mathias; O'Keefe, Regis

2009-01-01

Understanding the complex cellular and tissue mechanisms and interactions resulting in periprosthetic osteolysis requires a number of experimental approaches, each of which has its own set of advantages and limitations. In vitro models allow for the isolation of individual cell populations and have furthered our understanding of particle-cell interactions; however, they are limited because they do not mimic the complex tissue environment in which multiple cell interactions occur. In vivo animal models investigate the tissue interactions associated with periprosthetic osteolysis, but the choice of species and whether the implant system is subjected to mechanical load or to unloaded conditions are critical in assessing whether these models can be extrapolated to the clinical condition. Rigid analysis of retrieved tissue from clinical cases of osteolysis offers a different approach to studying the biologic process of osteolysis, but it is limited in that the tissue analyzed represents the end-stage of this process and, thus, may not reflect this process adequately. PMID:18612016

What experimental approaches (eg, in vivo, in vitro, tissue retrieval) are effective in investigating the biologic effects of particles?

PubMed

Bostrom, Mathias; O'Keefe, Regis

2008-01-01

Understanding the complex cellular and tissue mechanisms and interactions resulting in periprosthetic osteolysis requires a number of experimental approaches, each of which has its own set of advantages and limitations. In vitro models allow for the isolation of individual cell populations and have furthered our understanding of particle-cell interactions; however, they are limited because they do not mimic the complex tissue environment in which multiple cell interactions occur. In vivo animal models investigate the tissue interactions associated with periprosthetic osteolysis, but the choice of species and whether the implant system is subjected to mechanical load or to unloaded conditions are critical in assessing whether these models can be extrapolated to the clinical condition. Rigid analysis of retrieved tissue from clinical cases of osteolysis offers a different approach to studying the biologic process of osteolysis, but it is limited in that the tissue analyzed represents the end-stage of this process and, thus, may not reflect this process adequately.

Inflammasome complexes: emerging mechanisms and effector functions

PubMed Central

Rathinam, Vijay A. K.; Fitzgerald, Katherine A.

2017-01-01

Canonical activation of the inflammasome is critical to promote caspase-1-dependent maturation of the proinflammatory cytokines IL-1β and IL-18, as well as to induce pyroptotic cell death in response to pathogens and endogenous danger signals. Recent discoveries, however, are beginning to unveil new components of the inflammasome machinery, and the full spectrum of inflammasome functions, extending their influence beyond canonical functions, to regulation of eicosanoid storm, autophagy and metabolism. In addition, the receptor components of the inflammasome can also regulate diverse biological processes, such as cellular proliferation, gene transcription and tumorigenesis, all of which are independent of their inflammasome complex-forming capabilities. Here, we review these recent advances that are shaping our understanding of the complex biology of the inflammasome and its constituents. PMID:27153493

Metallic elements in fossil fuel combustion products: amounts and form of emissions and evaluation of carcinogenicity and mutagenicity.

PubMed

Vouk, V B; Piver, W T

1983-01-01

Metallic elements contained in coal, oil and gasoline are mobilized by combustion processes and may be emitted into the atmosphere, mainly as components of submicron particles. The information about the amounts, composition and form of metal compounds is reviewed for some fuels and combustion processes. Since metal compounds are always contained in urban air pollutants, they have to be considered whenever an evaluation of biological impact of air pollutants is made. The value of currently used bioassays for the evaluation of the role of trace metal compounds, either as major biologically active components or as modifiers of biological effects of organic compounds is assessed. The whole animal bioassays for carcinogenicity do not seem to be an appropriate approach. They are costly, time-consuming and not easily amenable to the testing of complex mixtures. Some problems related to the application and interpretation of short-term bioassays are considered, and the usefulness of such bioassays for the evaluation of trace metal components contained in complex air pollution mixtures is examined.

Systems biomarkers as acute diagnostics and chronic monitoring tools for traumatic brain injury

NASA Astrophysics Data System (ADS)

Wang, Kevin K. W.; Moghieb, Ahmed; Yang, Zhihui; Zhang, Zhiqun

2013-05-01

Traumatic brain injury (TBI) is a significant biomedical problem among military personnel and civilians. There exists an urgent need to develop and refine biological measures of acute brain injury and chronic recovery after brain injury. Such measures "biomarkers" can assist clinicians in helping to define and refine the recovery process and developing treatment paradigms for the acutely injured to reduce secondary injury processes. Recent biomarker studies in the acute phase of TBI have highlighted the importance and feasibilities of identifying clinically useful biomarkers. However, much less is known about the subacute and chronic phases of TBI. We propose here that for a complex biological problem such as TBI, multiple biomarker types might be needed to harness the wide range of pathological and systemic perturbations following injuries, including acute neuronal death, neuroinflammation, neurodegeneration and neuroregeneration to systemic responses. In terms of biomarker types, they range from brain-specific proteins, microRNA, genetic polymorphism, inflammatory cytokines and autoimmune markers and neuro-endocrine hormones. Furthermore, systems biology-driven biomarkers integration can help present a holistic approach to understanding scenarios and complexity pathways involved in brain injury.

[Parametabolism as Non-Specific Modifier of Supramolecular Interactions in Living Systems].

PubMed

Kozlov, V A; Sapozhnikov, S P; Sheptuhina, A I; Golenkov, A V

2015-01-01

As it became known recently, in addition to the enzyme (enzymes and/or ribozymes) in living organisms occur a large number of ordinary chemical reactions without the participation of biological catalysts. These reactions are distinguished by low speed and, as a rule, the irreversibility. For example, along with diabetes mellitus, glycation and fructosilation of proteins are observed resulted in posttranslational modification with the low- or nonfunctioning protein formation which is poorly exposed to enzymatic proteolysis and therefore accumulates in the body. In addition, the known processes such as the nonenzymatic carbomoylation, pyridoxylation and thiamiation proteins. There is a reasonable basis to believe that alcoholic injury also realized through parametabolic secondary metabolites synthesis such as acetaldehyde. At the same time, the progress in supramolecular chemistry proves that in biological objects there is another large group ofparametabolic reactions caused by the formation of supramolecular complexes. Obviously, known parameterizes interactions can modify the formation of supramolecular complexes in living objects. These processes are of considerable interest for fundamental biology and fundamental and practical medicine, but they remain unexplored due to a lack of awareness of a wide range of researchers.

Metallic elements in fossil fuel combustion products: amounts and form of emissions and evaluation of carcinogenicity and mutagenicity.

PubMed Central

Vouk, V B; Piver, W T

1983-01-01

Metallic elements contained in coal, oil and gasoline are mobilized by combustion processes and may be emitted into the atmosphere, mainly as components of submicron particles. The information about the amounts, composition and form of metal compounds is reviewed for some fuels and combustion processes. Since metal compounds are always contained in urban air pollutants, they have to be considered whenever an evaluation of biological impact of air pollutants is made. The value of currently used bioassays for the evaluation of the role of trace metal compounds, either as major biologically active components or as modifiers of biological effects of organic compounds is assessed. The whole animal bioassays for carcinogenicity do not seem to be an appropriate approach. They are costly, time-consuming and not easily amenable to the testing of complex mixtures. Some problems related to the application and interpretation of short-term bioassays are considered, and the usefulness of such bioassays for the evaluation of trace metal components contained in complex air pollution mixtures is examined. PMID:6337825

RARE EARTH ELEMENT IMPACTS ON BIOLOGICAL WASTEWATER TREATMENT

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

Fujita, Y.; Barnes, J.; Fox, S.

Increasing demand for rare earth elements (REE) is expected to lead to new development and expansion in industries processing and or recycling REE. For some industrial operators, sending aqueous waste streams to a municipal wastewater treatment plant, or publicly owned treatment works (POTW), may be a cost effective disposal option. However, wastewaters that adversely affect the performance of biological wastewater treatment at the POTW will not be accepted. The objective of our research is to assess the effects of wastewaters that might be generated by new rare earth element (REE) beneficiation or recycling processes on biological wastewater treatment systems. Wemore » have been investigating the impact of yttrium and europium on the biological activity of activated sludge collected from an operating municipal wastewater treatment plant. We have also examined the effect of an organic complexant that is commonly used in REE extraction and separations; similar compounds may be a component of newly developed REE recycling processes. Our preliminary results indicate that in the presence of Eu, respiration rates for the activated sludge decrease relative to the no-Eu controls, at Eu concentrations ranging from <10 to 660 µM. Yttrium appears to inhibit respiration as well, although negative impacts have been observed only at the highest Y amendment level tested (660 µM). The organic complexant appears to have a negative impact on activated sludge activity as well, although results are variable. Ultimately the intent of this research is to help REE industries to develop environmentally friendly and economically sustainable beneficiation and recycling processes.« less

Understanding Protein Synthesis: An Interactive Card Game Discussion

ERIC Educational Resources Information Center

Lewis, Alison; Peat, Mary; Franklin, Sue

2005-01-01

Protein synthesis is a complex process and students find it difficult to understand. This article describes an interactive discussion "game" used by first year biology students at the University of Sydney. The students, in small groups, use the game in which the processes of protein synthesis are actioned by the students during a…

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

Systems biology of personalized nutrition

PubMed Central

van Ommen, Ben; van den Broek, Tim; de Hoogh, Iris; van Erk, Marjan; van Someren, Eugene; Rouhani-Rankouhi, Tanja; Anthony, Joshua C; Hogenelst, Koen; Pasman, Wilrike; Boorsma, André; Wopereis, Suzan

2017-01-01

Abstract Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person’s health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology–based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of “systems flexibility” is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed. PMID:28969366

Theoretical aspects of cellular decision-making and information-processing.

PubMed

Kobayashi, Tetsuya J; Kamimura, Atsushi

2012-01-01

Microscopic biological processes have extraordinary complexity and variety at the sub-cellular, intra-cellular, and multi-cellular levels. In dealing with such complex phenomena, conceptual and theoretical frameworks are crucial, which enable us to understand seemingly different intra- and inter-cellular phenomena from unified viewpoints. Decision-making is one such concept that has attracted much attention recently. Since a number of cellular behavior can be regarded as processes to make specific actions in response to external stimuli, decision-making can cover and has been used to explain a broad range of different cellular phenomena [Balázsi et al. (Cell 144(6):910, 2011), Zeng et al. (Cell 141(4):682, 2010)]. Decision-making is also closely related to cellular information-processing because appropriate decisions cannot be made without exploiting the information that the external stimuli contain. Efficiency of information transduction and processing by intra-cellular networks determines the amount of information obtained, which in turn limits the efficiency of subsequent decision-making. Furthermore, information-processing itself can serve as another concept that is crucial for understanding of other biological processes than decision-making. In this work, we review recent theoretical developments on cellular decision-making and information-processing by focusing on the relation between these two concepts.

Rational design and optimization of downstream processes of virus particles for biopharmaceutical applications: current advances.

PubMed

Vicente, Tiago; Mota, José P B; Peixoto, Cristina; Alves, Paula M; Carrondo, Manuel J T

2011-01-01

The advent of advanced therapies in the pharmaceutical industry has moved the spotlight into virus-like particles and viral vectors produced in cell culture holding great promise in a myriad of clinical targets, including cancer prophylaxis and treatment. Even though a couple of cases have reached the clinic, these products have yet to overcome a number of biological and technological challenges before broad utilization. Concerning the manufacturing processes, there is significant research focusing on the optimization of current cell culture systems and, more recently, on developing scalable downstream processes to generate material for pre-clinical and clinical trials. We review the current options for downstream processing of these complex biopharmaceuticals and underline current advances on knowledge-based toolboxes proposed for rational optimization of their processing. Rational tools developed to increase the yet scarce knowledge on the purification processes of complex biologicals are discussed as alternative to empirical, "black-boxed" based strategies classically used for process development. Innovative methodologies based on surface plasmon resonance, dynamic light scattering, scale-down high-throughput screening and mathematical modeling for supporting ion-exchange chromatography show great potential for a more efficient and cost-effective process design, optimization and equipment prototyping. Copyright © 2011 Elsevier Inc. All rights reserved.

A brief introduction to mixed effects modelling and multi-model inference in ecology

PubMed Central

Donaldson, Lynda; Correa-Cano, Maria Eugenia; Goodwin, Cecily E.D.

2018-01-01

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions. PMID:29844961

A brief introduction to mixed effects modelling and multi-model inference in ecology.

PubMed

Harrison, Xavier A; Donaldson, Lynda; Correa-Cano, Maria Eugenia; Evans, Julian; Fisher, David N; Goodwin, Cecily E D; Robinson, Beth S; Hodgson, David J; Inger, Richard

2018-01-01

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

Chemotaxis in densely populated tissue determines germinal center anatomy and cell motility: a new paradigm for the development of complex tissues.

PubMed

Hawkins, Jared B; Jones, Mark T; Plassmann, Paul E; Thorley-Lawson, David A

2011-01-01

Germinal centers (GCs) are complex dynamic structures that form within lymph nodes as an essential process in the humoral immune response. They represent a paradigm for studying the regulation of cell movement in the development of complex anatomical structures. We have developed a simulation of a modified cyclic re-entry model of GC dynamics which successfully employs chemotaxis to recapitulate the anatomy of the primary follicle and the development of a mature GC, including correctly structured mantle, dark and light zones. We then show that correct single cell movement dynamics (including persistent random walk and inter-zonal crossing) arise from this simulation as purely emergent properties. The major insight of our study is that chemotaxis can only achieve this when constrained by the known biological properties that cells are incompressible, exist in a densely packed environment, and must therefore compete for space. It is this interplay of chemotaxis and competition for limited space that generates all the complex and biologically accurate behaviors described here. Thus, from a single simple mechanism that is well documented in the biological literature, we can explain both higher level structure and single cell movement behaviors. To our knowledge this is the first GC model that is able to recapitulate both correctly detailed anatomy and single cell movement. This mechanism may have wide application for modeling other biological systems where cells undergo complex patterns of movement to produce defined anatomical structures with sharp tissue boundaries.

The biological inorganic chemistry of zinc ions.

PubMed

Krężel, Artur; Maret, Wolfgang

2016-12-01

The solution and complexation chemistry of zinc ions is the basis for zinc biology. In living organisms, zinc is redox-inert and has only one valence state: Zn(II). Its coordination environment in proteins is limited by oxygen, nitrogen, and sulfur donors from the side chains of a few amino acids. In an estimated 10% of all human proteins, zinc has a catalytic or structural function and remains bound during the lifetime of the protein. However, in other proteins zinc ions bind reversibly with dissociation and association rates commensurate with the requirements in regulation, transport, transfer, sensing, signalling, and storage. In contrast to the extensive knowledge about zinc proteins, the coordination chemistry of the "mobile" zinc ions in these processes, i.e. when not bound to proteins, is virtually unexplored and the mechanisms of ligand exchange are poorly understood. Knowledge of the biological inorganic chemistry of zinc ions is essential for understanding its cellular biology and for designing complexes that deliver zinc to proteins and chelating agents that remove zinc from proteins, for detecting zinc ion species by qualitative and quantitative analysis, and for proper planning and execution of experiments involving zinc ions and nanoparticles such as zinc oxide (ZnO). In most investigations, reference is made to zinc or Zn 2+ without full appreciation of how biological zinc ions are buffered and how the d-block cation Zn 2+ differs from s-block cations such as Ca 2+ with regard to significantly higher affinity for ligands, preference for the donor atoms of ligands, and coordination dynamics. Zinc needs to be tightly controlled. The interaction with low molecular weight ligands such as water and inorganic and organic anions is highly relevant to its biology but in contrast to its coordination in proteins has not been discussed in the biochemical literature. From the discussion in this article, it is becoming evident that zinc ion speciation is important in zinc biochemistry and for biological recognition as a variety of low molecular weight zinc complexes have already been implicated in biological processes, e.g. with ATP, glutathione, citrate, ethylenediaminedisuccinic acid, nicotianamine, or bacillithiol. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Sender–receiver systems and applying information theory for quantitative synthetic biology

PubMed Central

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

2015-01-01

Sender–receiver (S–R) systems abound in biology, with communication systems sending information in various forms. Information theory provides a quantitative basis for analysing these processes and is being applied to study natural genetic, enzymatic and neural networks. Recent advances in synthetic biology are providing us with a wealth of artificial S–R systems, giving us quantitative control over networks with a finite number of well-characterised components. Combining the two approaches can help to predict how to maximise signalling robustness, and will allow us to make increasingly complex biological computers. Ultimately, pushing the boundaries of synthetic biology will require moving beyond engineering the flow of information and towards building more sophisticated circuits that interpret biological meaning. PMID:25282688

Protein-protein interaction predictions using text mining methods.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

Engineering scalable biological systems

PubMed Central

2010-01-01

Synthetic biology is focused on engineering biological organisms to study natural systems and to provide new solutions for pressing medical, industrial and environmental problems. At the core of engineered organisms are synthetic biological circuits that execute the tasks of sensing inputs, processing logic and performing output functions. In the last decade, significant progress has been made in developing basic designs for a wide range of biological circuits in bacteria, yeast and mammalian systems. However, significant challenges in the construction, probing, modulation and debugging of synthetic biological systems must be addressed in order to achieve scalable higher-complexity biological circuits. Furthermore, concomitant efforts to evaluate the safety and biocontainment of engineered organisms and address public and regulatory concerns will be necessary to ensure that technological advances are translated into real-world solutions. PMID:21468204

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.

Filtration device for rapid separation of biological particles from complex matrices

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

Kim, Sangil; Naraghi-Arani, Pejman; Liou, Megan

2018-01-09

Methods and systems for filtering of biological particles are disclosed. Filtering membranes separate adjacent chambers. Through osmotic or electrokinetic processes, flow of particles is carried out through the filtering membranes. Cells, viruses and cell waste can be filtered depending on the size of the pores of the membrane. A polymer brush can be applied to a surface of the membrane to enhance filtering and prevent fouling.

Roadmap on semiconductor-cell biointerfaces

NASA Astrophysics Data System (ADS)

Tian, Bozhi; Xu, Shuai; Rogers, John A.; Cestellos-Blanco, Stefano; Yang, Peidong; Carvalho-de-Souza, João L.; Bezanilla, Francisco; Liu, Jia; Bao, Zhenan; Hjort, Martin; Cao, Yuhong; Melosh, Nicholas; Lanzani, Guglielmo; Benfenati, Fabio; Galli, Giulia; Gygi, Francois; Kautz, Rylan; Gorodetsky, Alon A.; Kim, Samuel S.; Lu, Timothy K.; Anikeeva, Polina; Cifra, Michal; Krivosudský, Ondrej; Havelka, Daniel; Jiang, Yuanwen

2018-05-01

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world.

EMERGING BIOLOGICAL PRINCIPLES OF METASTASIS

PubMed Central

Lambert, Arthur W.; Pattabiraman, Diwakar R.; Weinberg, Robert A.

2016-01-01

Metastases account for the great majority of cancer-associated deaths, yet this complex process remains the least understood aspect of cancer biology. As the body of research concerning metastasis continues to grow at a rapid rate, the biological programs that underlie the dissemination and metastatic outgrowth of cancer cells are beginning to come into view. In this review we summarize the cellular and molecular mechanisms involved in metastasis, with a focus on carcinomas where the most is known, and highlight the general principles of metastasis that have begun to emerge. PMID:28187288

Programming Morphogenesis through Systems and Synthetic Biology.

PubMed

Velazquez, Jeremy J; Su, Emily; Cahan, Patrick; Ebrahimkhani, Mo R

2018-04-01

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids. Copyright © 2017 Elsevier Ltd. All rights reserved.

Near Infrared Dyes as Lifetime Solvatochromic Probes for Micropolarity Measurements of Biological Systems

PubMed Central

Berezin, Mikhail Y.; Lee, Hyeran; Akers, Walter; Achilefu, Samuel

2007-01-01

The polarity of biological mediums controls a host of physiological processes such as digestion, signaling, transportation, metabolism, and excretion. With the recent widespread use of near-infrared (NIR) fluorescent dyes for biological imaging of cells and living organisms, reporting medium polarity with these dyes would provide invaluable functional information in addition to conventional optical imaging parameters. Here, we report a new approach to determine polarities of macro- and microsystems for in vitro and potential in vivo applications using NIR polymethine molecular probes. Unlike the poor solvatochromic response of NIR dyes in solvents with diverse polarity, their fluorescence lifetimes are highly sensitive, increasing by a factor of up to 8 on moving from polar to nonpolar mediums. We also established a correlation between fluorescence lifetime and solvent orientation polarizability and developed a lifetime polarity index for determining the polarity of complex systems, including micelles and albumin binding sites. Because of the importance of medium polarity in molecular, cellular, and biochemical processes and the significance of reduced autofluorescence and deep tissue penetration of light in the NIR region, the findings reported herein represent an important advance toward using NIR molecular probes to measure the polarity of complex biological systems in vitro and in vivo. PMID:17573433

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

PubMed

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

2018-06-01

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

A Review of Podocyte Biology.

PubMed

Garg, Puneet

2018-05-31

Podocyte biology is a developing science that promises to help improve understanding of the mechanistic nature of multiple diseases associated with proteinuria. Proteinuria in nephrotic syndrome has been linked to mechanistic dysfunctions in the renal glomerulus involving the function of podocyte epithelial cells, including podocyte foot process effacement. Developments in imaging technology are improving knowledge of the detailed structure of the human renal glomerulus and cortex. Podocyte foot processes attach themselves to the glomerular capillaries at the glomerular basement membrane (GBM) forming intercellular junctions that form slit diaphragm filtration barriers that help maintain normal renal function. Damage in this area has been implicated in glomerular disease. Injured podocytes undergo effacement whereby they lose their structure and spread out, leading to a reduction in filtration barrier function. Effacement is typically associated with the presence of proteinuria in focal segmental glomerulosclerosis, minimal change disease, and diabetes. It is thought to be due to a breakdown in the actin cytoskeleton of the foot processes, complex contractile apparatuses that allow podocytes to dynamically reorganize according to changes in filtration requirements. The process of podocyte depletion correlates with the development of glomerular sclerosis and chronic kidney disease. Focal adhesion complexes that interact with the underlying GBM bind the podocytes within the glomerular structure and prevent their detachment. Key Messages: Knowledge of glomerular podocyte biology is helping to advance our understanding of the science and mechanics of the glomerular filtering process, opening the way to a variety of new potential applications for clinical targeting. © 2018 S. Karger AG, Basel.

CHEMICAL PROCESSES AND MODELING IN ECOSYSTEMS

EPA Science Inventory

Trends in regulatory strategies require EPA to understand better chemical behavior in natural and impacted ecosystems and in biological systems to carry out the increasingly complex array of exposure and risk assessments needed to develop scientifically defensible regulations (GP...

Application of linker technique to trap transiently interacting protein complexes for structural studies

PubMed Central

Reddy Chichili, Vishnu Priyanka; Kumar, Veerendra; Sivaraman, J.

2016-01-01

Protein-protein interactions are key events controlling several biological processes. We have developed and employed a method to trap transiently interacting protein complexes for structural studies using glycine-rich linkers to fuse interacting partners, one of which is unstructured. Initial steps involve isothermal titration calorimetry to identify the minimum binding region of the unstructured protein in its interaction with its stable binding partner. This is followed by computational analysis to identify the approximate site of the interaction and to design an appropriate linker length. Subsequently, fused constructs are generated and characterized using size exclusion chromatography and dynamic light scattering experiments. The structure of the chimeric protein is then solved by crystallization, and validated both in vitro and in vivo by substituting key interacting residues of the full length, unlinked proteins with alanine. This protocol offers the opportunity to study crucial and currently unattainable transient protein interactions involved in various biological processes. PMID:26985443

Image processing for cryogenic transmission electron microscopy of symmetry-mismatched complexes.

PubMed

Huiskonen, Juha T

2018-02-08

Cryogenic transmission electron microscopy (cryo-TEM) is a high-resolution biological imaging method, whereby biological samples, such as purified proteins, macromolecular complexes, viral particles, organelles and cells, are embedded in vitreous ice preserving their native structures. Due to sensitivity of biological materials to the electron beam of the microscope, only relatively low electron doses can be applied during imaging. As a result, the signal arising from the structure of interest is overpowered by noise in the images. To increase the signal-to-noise ratio, different image processing-based strategies that aim at coherent averaging of signal have been devised. In such strategies, images are generally assumed to arise from multiple identical copies of the structure. Prior to averaging, the images must be grouped according to the view of the structure they represent and images representing the same view must be simultaneously aligned relatively to each other. For computational reconstruction of the three-dimensional structure, images must contain different views of the original structure. Structures with multiple symmetry-related substructures are advantageous in averaging approaches because each image provides multiple views of the substructures. However, the symmetry assumption may be valid for only parts of the structure, leading to incoherent averaging of the other parts. Several image processing approaches have been adapted to tackle symmetry-mismatched substructures with increasing success. Such structures are ubiquitous in nature and further computational method development is needed to understanding their biological functions. ©2018 The Author(s).

Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us about How the Brain Might Represent Visual Information?

PubMed Central

Tsotsos, John K.

2017-01-01

Much has been written about how the biological brain might represent and process visual information, and how this might inspire and inform machine vision systems. Indeed, tremendous progress has been made, and especially during the last decade in the latter area. However, a key question seems too often, if not mostly, be ignored. This question is simply: do proposed solutions scale with the reality of the brain's resources? This scaling question applies equally to brain and to machine solutions. A number of papers have examined the inherent computational difficulty of visual information processing using theoretical and empirical methods. The main goal of this activity had three components: to understand the deep nature of the computational problem of visual information processing; to discover how well the computational difficulty of vision matches to the fixed resources of biological seeing systems; and, to abstract from the matching exercise the key principles that lead to the observed characteristics of biological visual performance. This set of components was termed complexity level analysis in Tsotsos (1987) and was proposed as an important complement to Marr's three levels of analysis. This paper revisits that work with the advantage that decades of hindsight can provide. PMID:28848458

Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us about How the Brain Might Represent Visual Information?

PubMed

Tsotsos, John K

2017-01-01

Much has been written about how the biological brain might represent and process visual information, and how this might inspire and inform machine vision systems. Indeed, tremendous progress has been made, and especially during the last decade in the latter area. However, a key question seems too often, if not mostly, be ignored. This question is simply: do proposed solutions scale with the reality of the brain's resources? This scaling question applies equally to brain and to machine solutions. A number of papers have examined the inherent computational difficulty of visual information processing using theoretical and empirical methods. The main goal of this activity had three components: to understand the deep nature of the computational problem of visual information processing; to discover how well the computational difficulty of vision matches to the fixed resources of biological seeing systems; and, to abstract from the matching exercise the key principles that lead to the observed characteristics of biological visual performance. This set of components was termed complexity level analysis in Tsotsos (1987) and was proposed as an important complement to Marr's three levels of analysis. This paper revisits that work with the advantage that decades of hindsight can provide.

Characterising the development of the understanding of human body systems in high-school biology students - a longitudinal study

NASA Astrophysics Data System (ADS)

Snapir, Zohar; Eberbach, Catherine; Ben-Zvi-Assaraf, Orit; Hmelo-Silver, Cindy; Tripto, Jaklin

2017-10-01

Science education today has become increasingly focused on research into complex natural, social and technological systems. In this study, we examined the development of high-school biology students' systems understanding of the human body, in a three-year longitudinal study. The development of the students' system understanding was evaluated using the Components Mechanisms Phenomena (CMP) framework for conceptual representation. We coded and analysed the repertory grid personal constructs of 67 high-school biology students at 4 points throughout the study. Our data analysis builds on the assumption that systems understanding entails a perception of all the system categories, including structures within the system (its Components), specific processes and interactions at the macro and micro levels (Mechanisms), and the Phenomena that present the macro scale of processes and patterns within a system. Our findings suggest that as the learning process progressed, the systems understanding of our students became more advanced, moving forward within each of the major CMP categories. Moreover, there was an increase in the mechanism complexity presented by the students, manifested by more students describing mechanisms at the molecular level. Thus, the 'mechanism' category and the micro level are critical components that enable students to understand system-level phenomena such as homeostasis.

Biological enhancement of graft-tunnel healing in anterior cruciate ligament reconstruction

PubMed Central

SACCOMANNO, MARISTELLA F.; CAPASSO, LUIGI; FRESTA, LUCA; MILANO, GIUSEPPE

2016-01-01

The sites where graft healing occurs within the bone tunnel and where the intra-articular ligamentization process takes place are the two most important sites of biological incorporation after anterior cruciate ligament (ACL) reconstruction, since they help to determine the mechanical behavior of the femur-ACL graft-tibia complex. Graft-tunnel healing is a complex process influenced by several factors, such as type of graft, preservation of remnants, bone quality, tunnel length and placement, fixation techniques and mechanical stress. In recent years, numerous experimental and clinical studies have been carried out to evaluate potential strategies designed to enhance and optimize the biological environment of the graft-tunnel interface. Modulation of inflammation, tissue engineering and gene transfer techniques have been applied in order to obtain a direct-type fibrocartilaginous insertion of the ACL graft, similar to that of native ligament, and to accelerate the healing process of tendon grafts within the bone tunnel. Although animal studies have given encouraging results, clinical studies are lacking and their results do not really support the use of the various strategies in clinical practice. Further investigations are therefore needed to optimize delivery techniques, therapeutic concentrations, maintenance of therapeutic effects over time, and to reduce the risk of undesirable effects in clinical practice. PMID:27900311

Characterizing local biological hotspots in the Gulf of Maine using remote sensing data

NASA Astrophysics Data System (ADS)

Ribera, Marta M.

Researchers increasingly advocate the use of ecosystem-based management (EBM) for managing complex marine ecosystems. This approach requires managers to focus on processes and cross-scale interactions, rather than individual components. However, they often lack appropriate tools and data sources to pursue this change in management approach. One method that has been proposed to understand the ecological complexity inherent in marine ecosystems is the study of biological hotspots. Biological hotspots are locations where organisms from different trophic levels aggregate to feed on abundant supplies, and they are considered a first step toward understanding the processes driving spatial and temporal heterogeneity in marine systems. Biological hotspots are supported by phytoplankton aggregations, which are characterized by high spatial and temporal variability. As a result, methods developed to locate biological hotspots in relatively stable terrestrial systems are not well suited for more dynamic marine ecosystems. The main objective of this thesis is thus to identify and characterize local-scale biological hotspots in the western side of the Gulf of Maine. The first chapter describes a new methodological framework with the steps needed to locate these types of hotspots in marine ecosystems using remote sensing datasets. Then, in the second chapter these hotspots are characterized using a novel metric that uses time series information and spatial statistics to account for both the temporal variability and spatial structure of these marine aggregations. This metric redefines biological hotspots as areas with a high probability of exhibiting positive anomalies of productivity compared to the expected regional seasonal pattern. Finally, the third chapter compares the resulting biological hotspots to fishery-dependent abundance indices of surface and benthic predators to determine the effect of the location and magnitude of phytoplankton aggregations on the rest of the ecosystem. Analyses indicate that the spatial scale and magnitude of biological hotspots in the Gulf of Maine depend on the location and time of the year. Results also show that these hotspots change over time in response to both short-term oceanographic processes and long-term climatic cycles. Finally, the new metric presented here facilitates the spatial comparison between different trophic levels, thus allowing interdisciplinary ecosystem-wide studies.

Complex networks with scale-free nature and hierarchical modularity

NASA Astrophysics Data System (ADS)

Shekatkar, Snehal M.; Ambika, G.

2015-09-01

Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

Exploring biological, chemical and geomorphological patterns in fluvial ecosystems with Structural Equation Modelling

NASA Astrophysics Data System (ADS)

Bizzi, S.; Surridge, B.; Lerner, D. N.:

2009-04-01

River ecosystems represent complex networks of interacting biological, chemical and geomorphological processes. These processes generate spatial and temporal patterns in biological, chemical and geomorphological variables, and a growing number of these variables are now being used to characterise the status of rivers. However, integrated analyses of these biological-chemical-geomorphological networks have rarely been undertaken, and as a result our knowledge of the underlying processes and how they generate the resulting patterns remains weak. The apparent complexity of the networks involved, and the lack of coherent datasets, represent two key challenges to such analyses. In this paper we describe the application of a novel technique, Structural Equation Modelling (SEM), to the investigation of biological, chemical and geomorphological data collected from rivers across England and Wales. The SEM approach is a multivariate statistical technique enabling simultaneous examination of direct and indirect relationships across a network of variables. Further, SEM allows a-priori conceptual or theoretical models to be tested against available data. This is a significant departure from the solely exploratory analyses which characterise other multivariate techniques. We took biological, chemical and river habitat survey data collected by the Environment Agency for 400 sites in rivers spread across England and Wales, and created a single, coherent dataset suitable for SEM analyses. Biological data cover benthic macroinvertebrates, chemical data relate to a range of standard parameters (e.g. BOD, dissolved oxygen and phosphate concentration), and geomorphological data cover factors such as river typology, substrate material and degree of physical modification. We developed a number of a-priori conceptual models, reflecting current research questions or existing knowledge, and tested the ability of these conceptual models to explain the variance and covariance within the dataset. The conceptual models we developed were able to explain correctly the variance and covariance shown by the datasets, proving to be a relevant representation of the processes involved. The models explained 65% of the variance in indices describing benthic macroinvertebrate communities. Dissolved oxygen was of primary importance, but geomorphological factors, including river habitat type and degree of habitat degradation, also had significant explanatory power. The addition of spatial variables, such as latitude or longitude, did not provide additional explanatory power. This suggests that the variables already included in the models effectively represented the eco-regions across which our data were distributed. The models produced new insights into the relative importance of chemical and geomorphological factors for river macroinvertebrate communities. The SEM technique proved a powerful tool for exploring complex biological-chemical-geomorphological networks, for example able to deal with the co-correlations that are common in rivers due to multiple feedback mechanisms.

System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

PubMed Central

Miao, Yingbo; Zhang, Liangcai; Wang, Yang; Feng, Rennan; Yang, Lei; Zhang, Shihua; Jiang, Yongshuai; Liu, Guiyou

2014-01-01

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM. PMID:25243143

Information processing in bacteria: memory, computation, and statistical physics: a key issues review

NASA Astrophysics Data System (ADS)

Lan, Ganhui; Tu, Yuhai

2016-05-01

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network—the main players (nodes) and their interactions (links)—in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.

Information processing in bacteria: memory, computation, and statistical physics: a key issues review.

PubMed

Lan, Ganhui; Tu, Yuhai

2016-05-01

Living systems have to constantly sense their external environment and adjust their internal state in order to survive and reproduce. Biological systems, from as complex as the brain to a single E. coli cell, have to process these data in order to make appropriate decisions. How do biological systems sense external signals? How do they process the information? How do they respond to signals? Through years of intense study by biologists, many key molecular players and their interactions have been identified in different biological machineries that carry out these signaling functions. However, an integrated, quantitative understanding of the whole system is still lacking for most cellular signaling pathways, not to say the more complicated neural circuits. To study signaling processes in biology, the key thing to measure is the input-output relationship. The input is the signal itself, such as chemical concentration, external temperature, light (intensity and frequency), and more complex signals such as the face of a cat. The output can be protein conformational changes and covalent modifications (phosphorylation, methylation, etc), gene expression, cell growth and motility, as well as more complex output such as neuron firing patterns and behaviors of higher animals. Due to the inherent noise in biological systems, the measured input-output dependence is often noisy. These noisy data can be analysed by using powerful tools and concepts from information theory such as mutual information, channel capacity, and the maximum entropy hypothesis. This information theory approach has been successfully used to reveal the underlying correlations between key components of biological networks, to set bounds for network performance, and to understand possible network architecture in generating observed correlations. Although the information theory approach provides a general tool in analysing noisy biological data and may be used to suggest possible network architectures in preserving information, it does not reveal the underlying mechanism that leads to the observed input-output relationship, nor does it tell us much about which information is important for the organism and how biological systems use information to carry out specific functions. To do that, we need to develop models of the biological machineries, e.g. biochemical networks and neural networks, to understand the dynamics of biological information processes. This is a much more difficult task. It requires deep knowledge of the underlying biological network-the main players (nodes) and their interactions (links)-in sufficient detail to build a model with predictive power, as well as quantitative input-output measurements of the system under different perturbations (both genetic variations and different external conditions) to test the model predictions to guide further development of the model. Due to the recent growth of biological knowledge thanks in part to high throughput methods (sequencing, gene expression microarray, etc) and development of quantitative in vivo techniques such as various florescence technology, these requirements are starting to be realized in different biological systems. The possible close interaction between quantitative experimentation and theoretical modeling has made systems biology an attractive field for physicists interested in quantitative biology. In this review, we describe some of the recent work in developing a quantitative predictive model of bacterial chemotaxis, which can be considered as the hydrogen atom of systems biology. Using statistical physics approaches, such as the Ising model and Langevin equation, we study how bacteria, such as E. coli, sense and amplify external signals, how they keep a working memory of the stimuli, and how they use these data to compute the chemical gradient. In particular, we will describe how E. coli cells avoid cross-talk in a heterogeneous receptor cluster to keep a ligand-specific memory. We will also study the thermodynamic costs of adaptation for cells to maintain an accurate memory. The statistical physics based approach described here should be useful in understanding design principles for cellular biochemical circuits in general.

UML as a cell and biochemistry modeling language.

PubMed

Webb, Ken; White, Tony

2005-06-01

The systems biology community is building increasingly complex models and simulations of cells and other biological entities, and are beginning to look at alternatives to traditional representations such as those provided by ordinary differential equations (ODE). The lessons learned over the years by the software development community in designing and building increasingly complex telecommunication and other commercial real-time reactive systems, can be advantageously applied to the problems of modeling in the biology domain. Making use of the object-oriented (OO) paradigm, the unified modeling language (UML) and Real-Time Object-Oriented Modeling (ROOM) visual formalisms, and the Rational Rose RealTime (RRT) visual modeling tool, we describe a multi-step process we have used to construct top-down models of cells and cell aggregates. The simple example model described in this paper includes membranes with lipid bilayers, multiple compartments including a variable number of mitochondria, substrate molecules, enzymes with reaction rules, and metabolic pathways. We demonstrate the relevance of abstraction, reuse, objects, classes, component and inheritance hierarchies, multiplicity, visual modeling, and other current software development best practices. We show how it is possible to start with a direct diagrammatic representation of a biological structure such as a cell, using terminology familiar to biologists, and by following a process of gradually adding more and more detail, arrive at a system with structure and behavior of arbitrary complexity that can run and be observed on a computer. We discuss our CellAK (Cell Assembly Kit) approach in terms of features found in SBML, CellML, E-CELL, Gepasi, Jarnac, StochSim, Virtual Cell, and membrane computing systems.

Click Chemistry in Complex Mixtures: Bioorthogonal Bioconjugation

PubMed Central

McKay, Craig S.; Finn, M.G.

2014-01-01

The selective chemical modification of biological molecules drives a good portion of modern drug development and fundamental biological research. While a few early examples of reactions that engage amine and thiol groups on proteins helped establish the value of such processes, the development of reactions that avoid most biological molecules so as to achieve selectivity in desired bond-forming events has revolutionized the field. We provide an update on recent developments in bioorthogonal chemistry that highlights key advances in reaction rates, biocompatibility, and applications. While not exhaustive, we hope this summary allows the reader to appreciate the rich continuing development of good chemistry that operates in the biological setting. PMID:25237856

Behavior of the gypsy moth life system model and development of synoptic model formulations

Treesearch

J. J. Colbert; Xu Rumei

1991-01-01

Aims of the research: The gypsy moth life system model (GMLSM) is a complex model which incorporates numerous components (both biotic and abiotic) and ecological processes. It is a detailed simulation model which has much biological reality. However, it has not yet been tested with life system data. For such complex models, evaluation and testing cannot be adequately...

(PS)2: protein structure prediction server version 3.0.

PubMed

Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

2015-07-01

Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens. | Office of Cancer Genomics

Cancer.gov

Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes, as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9-based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity.

The role of hydroxo-bridged dinuclear species and the influence of "innocent" buffers in the reactivity of cis-[Co(III)(cyclen)(H₂O)₂]³⁺ and [Co(III)(tren)(H₂O)₂]³⁺ complexes with biologically relevant ligands at physiological pH.

PubMed

Basallote, Manuel G; Martínez, Manuel; Vázquez, Marta

2014-07-28

In view of the relevance of the reactivity of inert tetraamine Co(III) complexes having two substitutionally active cis positions capable of interact with biologically relevant ligands, the study of the reaction of cis-[Co(cyclen)(H2O)2](3+) and [Co(tren)(H2O)2](3+) with chlorides, inorganic phosphate and 5'-CMP (5'-cytidinemonophosphate) has been pursued at physiological pH. The results indicate that, in addition to the actuation of the expected labilising conjugate-base mechanism, the formation of mono and inert bis hydroxo-bridged species is relevant for understanding their speciation and reactivity. The reactivity pattern observed also indicates the key role played by the "innocent" buffers frequently used in most in vitro studies, which can make the results unreliable in many cases. The differences between the reactivity of inorganic and biologically relevant phosphates has also been found to be remarkable, with outer-sphere hydrogen bonding interactions being a dominant factor for the process. While for the inorganic phosphate substitution process the formation of μ-η(2)-OPO2O represents the termination of the reactivity monitored, for 5'-CMP only the formation of η(1)-OPO3 species is observed, which evolve with time to the final dead-end bis hydroxo-bridged complexes. The promoted hydrolysis of the 5'-CMP phosphate has not been observed in any of the processes studied.

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

DNA codes for nanoscience.

PubMed

Samorì, Bruno; Zuccheri, Giampaolo

2005-02-11

The nanometer scale is a special place where all sciences meet and develop a particularly strong interdisciplinarity. While biology is a source of inspiration for nanoscientists, chemistry has a central role in turning inspirations and methods from biological systems to nanotechnological use. DNA is the biological molecule by which nanoscience and nanotechnology is mostly fascinated. Nature uses DNA not only as a repository of the genetic information, but also as a controller of the expression of the genes it contains. Thus, there are codes embedded in the DNA sequence that serve to control recognition processes on the atomic scale, such as the base pairing, and others that control processes taking place on the nanoscale. From the chemical point of view, DNA is the supramolecular building block with the highest informational content. Nanoscience has therefore the opportunity of using DNA molecules to increase the level of complexity and efficiency in self-assembling and self-directing processes.

Biodegradation of CuTETA, an effluent by-product in mineral processing.

PubMed

Cushing, Alexander M L; Kelebek, Sadan; Yue, Siqing; Ramsay, Juliana A

2018-04-13

Polyamines such as triethylenetetramine (TETA) and other amine chelators are used in mineral processing applications. Formation of heavy metal complexes of these reagents as a by-product in effluent water is a recent environmental concern. In this study, Paecilomyces sp. was enriched from soil on TETA as the sole source of carbon and nitrogen and was found to degrade > 96 and 90% CuTETA complexes at initial concentrations of 0.32 and 0.79 mM respectively, following 96-h incubation. After destabilization, most of the copper (> 78%) was complexed extracellularly and the rest was associated with the cell. Mass spectroscopy results provided confirmation that copper re-complexed with small, extracellular, and organic molecules. There are no reports in the literature that Paecilomyces or any other organism can grow on TETA or CuTETA. This study is the first to show that biological destabilization of CuTETA complexes in mineral processing effluents is feasible.

Nanomaterial interactions with biomembranes: Bridging the gap between soft matter models and biological context.

PubMed

Werner, Marco; Auth, Thorsten; Beales, Paul A; Fleury, Jean Baptiste; Höök, Fredrik; Kress, Holger; Van Lehn, Reid C; Müller, Marcus; Petrov, Eugene P; Sarkisov, Lev; Sommer, Jens-Uwe; Baulin, Vladimir A

2018-04-03

Synthetic polymers, nanoparticles, and carbon-based materials have great potential in applications including drug delivery, gene transfection, in vitro and in vivo imaging, and the alteration of biological function. Nature and humans use different design strategies to create nanomaterials: biological objects have emerged from billions of years of evolution and from adaptation to their environment resulting in high levels of structural complexity; in contrast, synthetic nanomaterials result from minimalistic but controlled design options limited by the authors' current understanding of the biological world. This conceptual mismatch makes it challenging to create synthetic nanomaterials that possess desired functions in biological media. In many biologically relevant applications, nanomaterials must enter the cell interior to perform their functions. An essential transport barrier is the cell-protecting plasma membrane and hence the understanding of its interaction with nanomaterials is a fundamental task in biotechnology. The authors present open questions in the field of nanomaterial interactions with biological membranes, including: how physical mechanisms and molecular forces acting at the nanoscale restrict or inspire design options; which levels of complexity to include next in computational and experimental models to describe how nanomaterials cross barriers via passive or active processes; and how the biological media and protein corona interfere with nanomaterial functionality. In this Perspective, the authors address these questions with the aim of offering guidelines for the development of next-generation nanomaterials that function in biological media.

Dynamic-landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

Treesearch

Thomas W. Bonnot; Frank R. Thompson; Joshua J. Millspaugh

2017-01-01

The increasing need to predict how climate change will impact wildlife species has exposed limitations in how well current approaches model important biological processes at scales at which those processes interact with climate. We used a comprehensive approach that combined recent advances in landscape and population modeling into dynamic-landscape metapopulation...

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339

Using phosphine ligands with a biological role to modulate reactivity in novel platinum complexes

NASA Astrophysics Data System (ADS)

Echeverri, Marcelo; Alvarez-Valdés, Amparo; Navas, Francisco; Perles, Josefina; Sánchez-Pérez, Isabel; Quiroga, A. G.

2018-02-01

Three platinum complexes with cis and trans configuration cis-[Pt(TCEP)2Cl2], cis-[Pt(tmTCEP)2Cl2] and trans-[Pt(TCEP)2Cl2], where TCEP is tris(2-carboxyethyl)phosphine, have been synthesized and fully characterized by usual techniques including single-crystal X-ray diffraction for trans-[Pt(TCEP)2Cl2] and cis-[Pt(tmTCEP)2Cl2]. Here, we also report on an esterification process of TCEP, which takes place in the presence of alcohols, leading to a platinum complex coordinated to an ester tmTCEP (2-methoxycarbonylethyl phosphine) ligand. The stability in solution of the three compounds and their interaction with biological models such as DNA (pBR322 and calf thymus DNA) and proteins (lysozyme and RNase) have also been studied.

Biosimilars--global issues, national solutions.

PubMed

Knezevic, Ivana; Griffiths, Elwyn

2011-09-01

Biotechnology derived medicinal products are presently the best characterized biologicals with considerable production and clinical experience, and have revolutionized the treatment of some of the most difficult-to-treat diseases, prolonging and improving the quality of life and patient care. They are also currently one of the fastest growing segments of the pharmaceutical industry market. The critical challenge that the biopharmaceutical industry is facing is the expiry of patents for the first generation of biopharmaceuticals, mainly recombinant DNA derived products, such as interferons, growth hormone and erythropoetin. The question that immediately arose was how should such copies of the originator products be licensed, bearing in mind that they are highly complex biological molecules produced by equally complex biological production processes with their inherent problem of biological variability. Copying biologicals is much more complex than copying small molecules and the critical issue was how to handle the licensing of products if relying in part on data from an innovator product. Since 2004 there has been considerable international consultation on how to deal with biosimilars and biological copy products. This has led to a better understanding of the challenges in the regulatory evaluation of the quality, safety and efficacy of "biosimilars", to the exchange of information between regulators, as well as to the identification of key issues. The aim of this article is to provide a brief overview of the scientific and regulatory challenges faced in developing and evaluating similar biotherapeutic products for global use. It is intended as an introduction to the series of articles in this special issue of Biologicals devoted to similar biotherapeutic products. Copyright © 2011. Published by Elsevier Ltd.

Integrated Decision Strategies for Skin Sensitization Hazard

EPA Science Inventory

One of the top priorities of the Interagency Coordinating Committee for the Validation of Alternative Methods (ICCVAM) is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biologi...

A Computational Model Predicting Disruption of Blood Vessel Development

EPA Science Inventory

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 varie...

Molecular locks and keys: the role of small molecules in phytohormone research

PubMed Central

Fonseca, Sandra; Rosado, Abel; Vaughan-Hirsch, John; Bishopp, Anthony; Chini, Andrea

2014-01-01

Plant adaptation, growth and development rely on the integration of many environmental and endogenous signals that collectively determine the overall plant phenotypic plasticity. Plant signaling molecules, also known as phytohormones, are fundamental to this process. These molecules act at low concentrations and regulate multiple aspects of plant fitness and development via complex signaling networks. By its nature, phytohormone research lies at the interface between chemistry and biology. Classically, the scientific community has always used synthetic phytohormones and analogs to study hormone functions and responses. However, recent advances in synthetic and combinational chemistry, have allowed a new field, plant chemical biology, to emerge and this has provided a powerful tool with which to study phytohormone function. Plant chemical biology is helping to address some of the most enduring questions in phytohormone research such as: Are there still undiscovered plant hormones? How can we identify novel signaling molecules? How can plants activate specific hormone responses in a tissue-specific manner? How can we modulate hormone responses in one developmental context without inducing detrimental effects on other processes? The chemical genomics approaches rely on the identification of small molecules modulating different biological processes and have recently identified active forms of plant hormones and molecules regulating many aspects of hormone synthesis, transport and response. We envision that the field of chemical genomics will continue to provide novel molecules able to elucidate specific aspects of hormone-mediated mechanisms. In addition, compounds blocking specific responses could uncover how complex biological responses are regulated. As we gain information about such compounds we can design small alterations to the chemical structure to further alter specificity, enhance affinity or modulate the activity of these compounds. PMID:25566283

Dinitrosyl iron complexes with thiol-containing ligands as a "working form" of endogenous nitric oxide.

PubMed

Vanin, Anatoly F

2016-04-01

The material presented herein is an overview of the results obtained by our research team during the many years' study of biological activities and occurrence of dinitrosyl iron complexes (DNIC) with thiol-containing ligands in human and animal organisms. With regard to their dose dependence and vast diversity of biological activities, DNIC are similar to the system of endogenous NO, one of the most universal regulators of biological processes. The role of biologically active components in DNIC is played by their iron-dinitrosyl fragments, [Fe(NO)2], endowed with the ability to generate neutral NO molecules and nitrosonium ions (NO(+)). Their release is effected by heme-and thiol-containing proteins, which fulfill the function of biological targets and acceptors of NO and NO(+). Beneficial regulatory effects of DNIC on physiological and metabolic processes are numerous and diverse and include, among other things, lowering of arterial pressure and accelerated healing of skin wounds. In the course of fast decomposition of their Fe(NO)2 fragments (e.g., in the presence of iron chelators), DNIC produce adverse (cytotoxic) effects, which can best be exemplified by their ability to suppress the development of experimental endometriosis in animals. In animal tissues, DNIC with thiol-containing ligands are predominantly represented by the binuclear form, which, contrary to mononuclear DNIC detectable by the 2.03 signal, is EPR-silent. The ample body of evidence on biological activities and occurrence of DNIC gained so far clearly demonstrates that in human and animal organisms DNIC with thiol-containing ligands represent a "working form" of the system of endogenous NO responsible for its accumulation and stabilization in animal tissues as well as its further transfer to its biological targets. Copyright © 2016 Elsevier Inc. 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.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

PubMed Central

Forsberg, Erica M; Huan, Tao; Rinehart, Duane; Benton, H Paul; Warth, Benedikt; Hilmers, Brian; Siuzdak, Gary

2018-01-01

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min. PMID:29494574

Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces.

PubMed

Rodrigo, Daniel; Tittl, Andreas; Ait-Bouziad, Nadine; John-Herpin, Aurelian; Limaj, Odeta; Kelly, Christopher; Yoo, Daehan; Wittenberg, Nathan J; Oh, Sang-Hyun; Lashuel, Hilal A; Altug, Hatice

2018-06-04

A multitude of biological processes are enabled by complex interactions between lipid membranes and proteins. To understand such dynamic processes, it is crucial to differentiate the constituent biomolecular species and track their individual time evolution without invasive labels. Here, we present a label-free mid-infrared biosensor capable of distinguishing multiple analytes in heterogeneous biological samples with high sensitivity. Our technology leverages a multi-resonant metasurface to simultaneously enhance the different vibrational fingerprints of multiple biomolecules. By providing up to 1000-fold near-field intensity enhancement over both amide and methylene bands, our sensor resolves the interactions of lipid membranes with different polypeptides in real time. Significantly, we demonstrate that our label-free chemically specific sensor can analyze peptide-induced neurotransmitter cargo release from synaptic vesicle mimics. Our sensor opens up exciting possibilities for gaining new insights into biological processes such as signaling or transport in basic research as well as provides a valuable toolkit for bioanalytical and pharmaceutical applications.

A practitioner's perspective on the application and research needs of membrane bioreactors for municipal wastewater treatment.

PubMed

Kraemer, Jeremy T; Menniti, Adrienne L; Erdal, Zeynep K; Constantine, Timothy A; Johnson, Bruce R; Daigger, Glen T; Crawford, George V

2012-10-01

The application of membrane bioreactors (MBRs) for municipal wastewater treatment has increased dramatically over the last decade. From a practitioner's perspective, design practice has evolved over five "generations" in the areas of biological process optimization, separating process design from equipment supply, and reliability/redundancy thereby facilitating "large" MBRs (e.g. 150,000 m(3)/day). MBR advantages and disadvantages, and process design to accommodate biological nutrient removal, high mixed liquor suspended solids concentrations, operation and maintenance, peak flows, and procurement are reviewed from the design practitioner's perspective. Finally, four knowledge areas are identified as important to practitioners meriting further research and development: (i) membrane design and performance such as improving peak flow characteristics and decreasing operating costs; (ii) process design and performance such as managing the fluid properties of the biological solids, disinfection, and microcontaminant removal; (iii) facility design such as equipment standardization and decreasing mechanical complexity; and (iv) sustainability such as anaerobic MBRs. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

Integrated omics for the identification of key functionalities in biological wastewater treatment microbial communities.

PubMed

Narayanasamy, Shaman; Muller, Emilie E L; Sheik, Abdul R; Wilmes, Paul

2015-05-01

Biological wastewater treatment plants harbour diverse and complex microbial communities which prominently serve as models for microbial ecology and mixed culture biotechnological processes. Integrated omic analyses (combined metagenomics, metatranscriptomics, metaproteomics and metabolomics) are currently gaining momentum towards providing enhanced understanding of community structure, function and dynamics in situ as well as offering the potential to discover novel biological functionalities within the framework of Eco-Systems Biology. The integration of information from genome to metabolome allows the establishment of associations between genetic potential and final phenotype, a feature not realizable by only considering single 'omes'. Therefore, in our opinion, integrated omics will become the future standard for large-scale characterization of microbial consortia including those underpinning biological wastewater treatment processes. Systematically obtained time and space-resolved omic datasets will allow deconvolution of structure-function relationships by identifying key members and functions. Such knowledge will form the foundation for discovering novel genes on a much larger scale compared with previous efforts. In general, these insights will allow us to optimize microbial biotechnological processes either through better control of mixed culture processes or by use of more efficient enzymes in bioengineering applications. © 2015 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Live Cell Genomics: RNA Exon-Specific RNA-Binding Protein Isolation.

PubMed

Bell, Thomas J; Eberwine, James

2015-01-01

RNA-binding proteins (RBPs) are essential regulatory proteins that control all modes of RNA processing and regulation. New experimental approaches to isolate these indispensable proteins under in vivo conditions are needed to advance the field of RBP biology. Historically, in vitro biochemical approaches to isolate RBP complexes have been useful and productive, but biological relevance of the identified RBP complexes can be imprecise or erroneous. Here we review an inventive experimental to isolate RBPs under the in vivo conditions. The method is called peptide nucleic acid (PNA)-assisted identification of RBP (PAIR) technology and it uses cell-penetrating peptides (CPPs) to deliver photo-activatible RBP-capture molecule to the cytoplasm of the live cells. The PAIR methodology provides two significant advantages over the most commonly used approaches: (1) it overcomes the in vitro limitation of standard biochemical approaches and (2) the PAIR RBP-capture molecule is highly selective and adaptable which allows investigators to isolate exon-specific RBP complexes. Most importantly, the in vivo capture conditions and selectivity of the RBP-capture molecule yield biologically accurate and relevant RBP data.

Presenilin-1 affects trafficking and processing of βAPP and is targeted in a complex with nicastrin to the plasma membrane

PubMed Central

Kaether, Christoph; Lammich, Sven; Edbauer, Dieter; Ertl, Michaela; Rietdorf, Jens; Capell, Anja; Steiner, Harald; Haass, Christian

2002-01-01

Amyloid β-peptide (Aβ) is generated by the consecutive cleavages of β- and γ-secretase. The intramembraneous γ-secretase cleavage critically depends on the activity of presenilins (PS1 and PS2). Although there is evidence that PSs are aspartyl proteases with γ-secretase activity, it remains controversial whether their subcellular localization overlaps with the cellular sites of Aβ production. We now demonstrate that biologically active GFP-tagged PS1 as well as endogenous PS1 are targeted to the plasma membrane (PM) of living cells. On the way to the PM, PS1 binds to nicastrin (Nct), an essential component of the γ-secretase complex. This complex is targeted through the secretory pathway where PS1-bound Nct becomes endoglycosidase H resistant. Moreover, surface-biotinylated Nct can be coimmunoprecipitated with PS1 antibodies, demonstrating that this complex is located to cellular sites with γ-secretase activity. Inactivating PS1 or PS2 function by mutagenesis of one of the critical aspartate residues or by γ-secretase inhibitors results in delayed reinternalization of the β-amyloid precursor protein and its accumulation at the cell surface. Our data suggest that PS is targeted as a biologically active complex with Nct through the secretory pathway to the cell surface and suggest a dual function of PS in γ-secretase processing and in trafficking. PMID:12147673

Molecular Biology of Proteins Acting in Immune Response Mechanisms

DTIC Science & Technology

1988-06-01

Studies of the bigsynthesis and processing of LAMP-i and LAMP-2 by pulse -labeling with [ S]methionine showed that the proteins were synthesized as...about Mr 90,000, and post-translationally processed by the addition of a heterogeneous mixture of complex-type oligosaccharides to form mature...centrifugation, and analysis of oligosaccharide processing (D’Souza et -l., 1986). Synthesis and glycosylation of the core polypeptide in the rough

Hydrological mixing and geochemical processes characterization in an estuarine/mangrove system using environmental tracers in Babitonga Bay (Santa Catarina, Brazil)

NASA Astrophysics Data System (ADS)

Barros Grace, Virgínia; Mas-Pla, Josep; Oliveira Novais, Therezinha; Sacchi, Elisa; Zuppi, Gian Maria

2008-03-01

The hydrologic complex of Babitonga Bay (Brazil) forms a vast environmental complex where agriculture, shellfish farming, and industries coexist with a unique natural area of Atlantic rain forest and mangrove systems. The origin of different continental hydrological components, the environmental transition between saline and freshwaters, and the influence of the seasonality on Babitonga Bay waters are evaluated using isotopes and chemistry. End-member mixing analysis is used to explore hydrological processes in the bay. We show that a mixing of waters from different origins takes place in the bay modifying its chemical characteristics. Furthermore, biogeochemical processes related to well-developed mangrove systems are responsible for an efficient bromide uptake, which limit its use as a tracer as commonly used in non-biologically active environments. Seasonal behaviours are also distinguished from our datasets. The rainy season (April) provides a homogenization of the hydrological processes that is not seen after the dry season (October), when larger spatial differences appear and when the effects of biological processes on the bay hydrochemistry are more dynamic, or can be better recognized. Moreover, Cl/Br and stable isotopes of water molecule allow a neat definition of the hydrological and biogeochemical processes that control chemical composition in coastal and transition areas.

Theoretical aspects of Systems Biology.

PubMed

Bizzarri, Mariano; Palombo, Alessandro; Cucina, Alessandra

2013-05-01

The natural world consists of hierarchical levels of complexity that range from subatomic particles and molecules to ecosystems and beyond. This implies that, in order to explain the features and behavior of a whole system, a theory might be required that would operate at the corresponding hierarchical level, i.e. where self-organization processes take place. In the past, biological research has focused on questions that could be answered by a reductionist program of genetics. The organism (and its development) was considered an epiphenomenona of its genes. However, a profound rethinking of the biological paradigm is now underway and it is likely that such a process will lead to a conceptual revolution emerging from the ashes of reductionism. This revolution implies the search for general principles on which a cogent theory of biology might rely. Because much of the logic of living systems is located at higher levels, it is imperative to focus on them. Indeed, both evolution and physiology work on these levels. Thus, by no means Systems Biology could be considered a 'simple' 'gradual' extension of Molecular Biology. Copyright © 2013 Elsevier Ltd. All rights reserved.

The interactions of peripheral membrane proteins with biological membranes

DOE PAGES

Johs, Alexander; Whited, A. M.

2015-07-29

The interactions of peripheral proteins with membrane surfaces are critical to many biological processes, including signaling, recognition, membrane trafficking, cell division and cell structure. On a molecular level, peripheral membrane proteins can modulate lipid composition, membrane dynamics and protein-protein interactions. Biochemical and biophysical studies have shown that these interactions are in fact highly complex, dominated by several different types of interactions, and have an interdependent effect on both the protein and membrane. Here we examine three major mechanisms underlying the interactions between peripheral membrane proteins and membranes: electrostatic interactions, hydrophobic interactions, and fatty acid modification of proteins. While experimental approachesmore » continue to provide critical insights into specific interaction mechanisms, emerging bioinformatics resources and tools contribute to a systems-level picture of protein-lipid interactions. Through these recent advances, we begin to understand the pivotal role of protein-lipid interactions underlying complex biological functions at membrane interfaces.« less

Sequential Injection Analysis for Optimization of Molecular Biology Reactions

PubMed Central

Allen, Peter B.; Ellington, Andrew D.

2011-01-01

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

Unraveling the Pathogenesis of Hoyeraal-Hreidarsson Syndrome, a Complex Telomere Biology Disorder

PubMed Central

Glousker, Galina; Touzot, Fabien; Revy, Patrick; Tzfati, Yehuda; Savage, Sharon A.

2015-01-01

SUMMARY Hoyeraal-Hreidarsson (HH) syndrome is a multisystem genetic disorder characterized by very short telomeres and considered a clinically severe variant of dyskeratosis congenita (DC). The main cause of mortality, usually in early childhood, is bone marrow failure. Mutations in several telomere biology genes have been reported to cause HH in about 60% of the HH patients, but the genetic defects in the rest of the patients are still unknown. Understanding the aetiology of HH and its diverse manifestations is challenging because of the complexity of telomere biology and the multiple telomeric and non-telomeric functions played by telomere-associated proteins in processes such as telomere replication, telomere protection, DNA damage response and ribosome and spliceosome assembly. Here we review the known clinical complications, molecular defects and germline mutations associated with HH, and elucidate possible mechanistic explanations and remaining questions in our understanding of the disease. PMID:25940403

Harnessing QbD, Programming Languages, and Automation for Reproducible Biology.

PubMed

Sadowski, Michael I; Grant, Chris; Fell, Tim S

2016-03-01

Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Review on Physicochemical, Chemical, and Biological Processes for Pharmaceutical Wastewater

NASA Astrophysics Data System (ADS)

Li, Zhenchen; Yang, Ping

2018-02-01

Due to the needs of human life and health, pharmaceutical industry has made great progress in recent years, but it has also brought about severe environmental problems. The presence of pharmaceuticals in natural waters which might pose potential harm to the ecosystems and humans raised increasing concern worldwide. Pharmaceuticals cannot be effectively removed by conventional wastewater treatment plants (WWTPs) owing to the complex composition, high concentration of organic contaminants, high salinity and biological toxicity of pharmaceutical wastewater. Therefore, the development of efficient methods is needed to improve the removal effect of pharmaceuticals. This review provides an overview on three types of treatment technologies including physicochemical, chemical and biological processes and their advantages and disadvantages respectively. In addition, the future perspectives of pharmaceutical wastewater treatment are given.

Genetic control of root growth: from genes to networks.

PubMed

Slovak, Radka; Ogura, Takehiko; Satbhai, Santosh B; Ristova, Daniela; Busch, Wolfgang

2016-01-01

Roots are essential organs for higher plants. They provide the plant with nutrients and water, anchor the plant in the soil, and can serve as energy storage organs. One remarkable feature of roots is that they are able to adjust their growth to changing environments. This adjustment is possible through mechanisms that modulate a diverse set of root traits such as growth rate, diameter, growth direction and lateral root formation. The basis of these traits and their modulation are at the cellular level, where a multitude of genes and gene networks precisely regulate development in time and space and tune it to environmental conditions. This review first describes the root system and then presents fundamental work that has shed light on the basic regulatory principles of root growth and development. It then considers emerging complexities and how they have been addressed using systems-biology approaches, and then describes and argues for a systems-genetics approach. For reasons of simplicity and conciseness, this review is mostly limited to work from the model plant Arabidopsis thaliana, in which much of the research in root growth regulation at the molecular level has been conducted. While forward genetic approaches have identified key regulators and genetic pathways, systems-biology approaches have been successful in shedding light on complex biological processes, for instance molecular mechanisms involving the quantitative interaction of several molecular components, or the interaction of large numbers of genes. However, there are significant limitations in many of these methods for capturing dynamic processes, as well as relating these processes to genotypic and phenotypic variation. The emerging field of systems genetics promises to overcome some of these limitations by linking genotypes to complex phenotypic and molecular data using approaches from different fields, such as genetics, genomics, systems biology and phenomics. © The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

Predicting embryo presence and viability

USDA-ARS?s Scientific Manuscript database

Pregnancy establishment, followed by birth of live offspring, is essential to all mammals. The biological processes leading up to pregnancy establishment, maintenance, and birth are complex and dependent on the coordinated timing of a series of events at the molecular, cellular, and physiological le...

Pectin in foods

USDA-ARS?s Scientific Manuscript database

Pectin, a complex polysaccharide, is a major component of non-lignified cell walls of dicotyledonous and some monocotyledonous plants. Its food-related technological functions are numerous and mirror many of its biological functions. As a naturally occurring component of raw or processed foods and a...

Invasive Processes, Mosaics and the Structure of Helminth Parasite Faunas

USDA-ARS?s Scientific Manuscript database

The biosphere in evolutionary and ecological time has been structured by episodes of geographic and host colonization that have determined distributions for complex assemblages of microparasites and macroparasites including helminths circulating among vertebrates. Biological invasion is an intricat...

Inhibition of microbiological sulfide oxidation by methanethiol and dimethyl polysulfides at natron-alkaline conditions.

PubMed

van den Bosch, Pim L F; de Graaff, Marco; Fortuny-Picornell, Marc; van Leerdam, Robin C; Janssen, Albert J H

2009-06-01

To avoid problems related to the discharge of sulfidic spent caustics, a biotechnological process is developed for the treatment of gases containing both hydrogen sulfide and methanethiol. The process operates at natron-alkaline conditions (>1 mol L(-1) of sodium- and potassium carbonates and a pH of 8.5-10) to enable the treatment of gases with a high partial CO(2) pressure. In the process, methanethiol reacts with biologically produced sulfur particles to form a complex mixture predominantly consisting of inorganic polysulfides, dimethyl disulfide (DMDS), and dimethyl trisulfide (DMTS). The effect of these organic sulfur compounds on the biological oxidation of sulfide to elemental sulfur was studied with natron-alkaliphilic bacteria belonging to the genus Thioalkalivibrio. Biological oxidation rates were reduced by 50% at 0.05 mM methanethiol, while for DMDS and DMTS, this was estimated to occur at 1.5 and 1.0 mM, respectively. The inhibiting effect of methanethiol on biological sulfide oxidation diminished due to its reaction with biologically produced sulfur particles. This reaction increases the feasibility of biotechnological treatment of gases containing both hydrogen sulfide and methanethiol at natron-alkaline conditions.

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

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

Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We havemore » applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. Furthermore, a more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.« less

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing

DOE PAGES

Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette; ...

2017-01-17

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We havemore » applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. Furthermore, a more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.« less

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.

PubMed

Mizrachi, Eshchar; Verbeke, Lieven; Christie, Nanette; Fierro, Ana C; Mansfield, Shawn D; Davis, Mark F; Gjersing, Erica; Tuskan, Gerald A; Van Montagu, Marc; Van de Peer, Yves; Marchal, Kathleen; Myburg, Alexander A

2017-01-31

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.

Computer Simulation of Developmental Processes and ...

EPA Pesticide Factsheets

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of

Charge Inversion by Electrostatic Complexation: Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Faraudo, Jordi; Travesset, Alex

2007-03-01

Ions near interfaces play an important role in many biological and physico-chemical processes and exhibit a fascinating diverse range of phenomena. A relevant example is charge inversion, where interfacial charges attract counterions in excess of their own nominal charge, thus leading to an inversion of the sign of the interfacial charge. In this work, we argue that in the case of amphiphilic interfaces, charge inversion can be generated by complexation, that is, electrostatic complexes containing several counterions bound to amphiphilic molecules. The formation of these complexes require the presence at the interface of groups with conformational degrees of freedom with many electronegative atoms. We illustrate this mechanism by analyzing all atomic molecular dynamics simulations of a DMPA (Dimirystoil-Phosphatidic acid) phospholipid monolayer in contact with divalent counterions. The results are found to be in agreement with recent experimental results on Langmuir monolayers. We also discuss the implications for biological systems, as Phosphatidic acid is emerging as a key signaling phospholipid.

Ammonia formation by a thiolate-bridged diiron amide complex as a nitrogenase mimic

NASA Astrophysics Data System (ADS)

Li, Yang; Li, Ying; Wang, Baomin; Luo, Yi; Yang, Dawei; Tong, Peng; Zhao, Jinfeng; Luo, Lun; Zhou, Yuhan; Chen, Si; Cheng, Fang; Qu, Jingping

2013-04-01

Although nitrogenase enzymes routinely convert molecular nitrogen into ammonia under ambient temperature and pressure, this reaction is currently carried out industrially using the Haber-Bosch process, which requires extreme temperatures and pressures to activate dinitrogen. Biological fixation occurs through dinitrogen and reduced NxHy species at multi-iron centres of compounds bearing sulfur ligands, but it is difficult to elucidate the mechanistic details and to obtain stable model intermediate complexes for further investigation. Metal-based synthetic models have been applied to reveal partial details, although most models involve a mononuclear system. Here, we report a diiron complex bridged by a bidentate thiolate ligand that can accommodate HN=NH. Following reductions and protonations, HN=NH is converted to NH3 through pivotal intermediate complexes bridged by N2H3- and NH2- species. Notably, the final ammonia release was effected with water as the proton source. Density functional theory calculations were carried out, and a pathway of biological nitrogen fixation is proposed.

A Chemical Engineer's Perspective on Health and Disease

PubMed Central

Androulakis, Ioannis P.

2014-01-01

Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems. PMID:25506103

Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

PubMed Central

2010-01-01

Background Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. Results We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. Conclusions We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community. PMID:20696053

Balancing Broad Ideas with Context: An Evaluation of Student Accuracy in Describing Ecosystem Processes after a System-Level Intervention

ERIC Educational Resources Information Center

Jordan, Rebecca C.; Brooks, Wesley R.; Hmelo-Silver, Cindy; Eberbach, Catherine; Sinha, Suparna

2014-01-01

Promoting student understanding of ecosystem processes is critical to biological education. Yet, teaching complex life systems can be difficult because systems are dynamic and often behave in a non-linear manner. In this paper, we discuss assessment results from a middle school classroom intervention in which a conceptual representation framework…

Adolescent African American Male Self Esteem: Suggestions for Mentoring Program Content. Mentoring Program Structures for Young Minority Males, Conference Paper Series.

ERIC Educational Resources Information Center

Spencer, Margaret Beale

The processes by which mentors might improve the self-esteem of economically vulnerable African American male youth are explored, drawing on previous research. The combination of biological, behavioral, and societal factors faced by young black males is complex, and has implications for identity processes. The initial longitudinal study of urban…

Development of an Ontology for Periodontitis.

PubMed

Suzuki, Asami; Takai-Igarashi, Takako; Nakaya, Jun; Tanaka, Hiroshi

2015-01-01

In the clinical dentists and periodontal researchers' community, there is an obvious demand for a systems model capable of linking the clinical presentation of periodontitis to underlying molecular knowledge. A computer-readable representation of processes on disease development will give periodontal researchers opportunities to elucidate pathways and mechanisms of periodontitis. An ontology for periodontitis can be a model for integration of large variety of factors relating to a complex disease such as chronic inflammation in different organs accompanied by bone remodeling and immune system disorders, which has recently been referred to as osteoimmunology. Terms characteristic of descriptions related to the onset and progression of periodontitis were manually extracted from 194 review articles and PubMed abstracts by experts in periodontology. We specified all the relations between the extracted terms and constructed them into an ontology for periodontitis. We also investigated matching between classes of our ontology and that of Gene Ontology Biological Process. We developed an ontology for periodontitis called Periodontitis-Ontology (PeriO). The pathological progression of periodontitis is caused by complex, multi-factor interrelationships. PeriO consists of all the required concepts to represent the pathological progression and clinical treatment of periodontitis. The pathological processes were formalized with reference to Basic Formal Ontology and Relation Ontology, which accounts for participants in the processes realized by biological objects such as molecules and cells. We investigated the peculiarity of biological processes observed in pathological progression and medical treatments for the disease in comparison with Gene Ontology Biological Process (GO-BP) annotations. The results indicated that peculiarities of Perio existed in 1) granularity and context dependency of both the conceptualizations, and 2) causality intrinsic to the pathological processes. PeriO defines more specific concepts than GO-BP, and thus can be added as descendants of GO-BP leaf nodes. PeriO defines causal relationships between the process concepts, which are not shown in GO-BP. The difference can be explained by the goal of conceptualization: PeriO focuses on mechanisms of the pathogenic progress, while GO-BP focuses on cataloguing all of the biological processes observed in experiments. The goal of conceptualization in PeriO may reflect the domain knowledge where a consequence in the causal relationships is a primary interest. We believe the peculiarities can be shared among other diseases when comparing processes in disease against GO-BP. This is the first open biomedical ontology of periodontitis capable of providing a foundation for an ontology-based model of aspects of molecular biology and pathological processes related to periodontitis, as well as its relations with systemic diseases. PeriO is available at http://bio-omix.tmd.ac.jp/periodontitis/.

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.

C. elegans network biology: a beginning.

PubMed Central

Piano, Fabio; Gunsalus, Kristin C; Hill, David E; Vidal, Marc

2006-01-01

The architecture and dynamics of molecular networks can provide an understanding of complex biological processes complementary to that obtained from the in-depth study of single genes and proteins. With a completely sequenced and well-annotated genome, a fully characterized cell lineage, and powerful tools available to dissect development, Caenorhabditis elegans, among metazoans, provides an optimal system to bridge cellular and organismal biology with the global properties of macromolecular networks. This chapter considers omic technologies available for C. elegans to describe molecular networks--encompassing transcriptional and phenotypic profiling as well as physical interaction mapping--and discusses how their individual and integrated applications are paving the way for a network-level understanding of C. elegans biology. PMID:18050437

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.

Diversification and enrichment of clinical biomaterials inspired by Darwinian evolution.

PubMed

Green, D W; Watson, G S; Watson, J A; Lee, D-J; Lee, J-M; Jung, H-S

2016-09-15

Regenerative medicine and biomaterials design are driven by biomimicry. There is the essential requirement to emulate human cell, tissue, organ and physiological complexity to ensure long-lasting clinical success. Biomimicry projects for biomaterials innovation can be re-invigorated with evolutionary insights and perspectives, since Darwinian evolution is the original dynamic process for biological organisation and complexity. Many existing human inspired regenerative biomaterials (defined as a nature generated, nature derived and nature mimicking structure, produced within a biological system, which can deputise for, or replace human tissues for which it closely matches) are without important elements of biological complexity such as, hierarchy and autonomous actions. It is possible to engineer these essential elements into clinical biomaterials via bioinspired implementation of concepts, processes and mechanisms played out during Darwinian evolution; mechanisms such as, directed, computational, accelerated evolutions and artificial selection contrived in the laboratory. These dynamos for innovation can be used during biomaterials fabrication, but also to choose optimal designs in the regeneration process. Further evolutionary information can help at the design stage; gleaned from the historical evolution of material adaptations compared across phylogenies to changes in their environment and habitats. Taken together, harnessing evolutionary mechanisms and evolutionary pathways, leading to ideal adaptations, will eventually provide a new class of Darwinian and evolutionary biomaterials. This will provide bioengineers with a more diversified and more efficient innovation tool for biomaterial design, synthesis and function than currently achieved with synthetic materials chemistry programmes and rational based materials design approach, which require reasoned logic. It will also inject further creativity, diversity and richness into the biomedical technologies that we make. All of which are based on biological principles. Such evolution-inspired biomaterials have the potential to generate innovative solutions, which match with existing bioengineering problems, in vital areas of clinical materials translation that include tissue engineering, gene delivery, drug delivery, immunity modulation, and scar-less wound healing. Evolution by natural selection is a powerful generator of innovations in molecular, materials and structures. Man has influenced evolution for thousands of years, to create new breeds of farm animals and crop plants, but now molecular and materials can be molded in the same way. Biological molecules and simple structures can be evolved, literally in the laboratory. Furthermore, they are re-designed via lessons learnt from evolutionary history. Through a 3-step process to (1) create variants in material building blocks, (2) screen the variants with beneficial traits/properties and (3) select and support their self-assembly into usable materials, improvements in design and performance can emerge. By introducing biological molecules and small organisms into this process, it is possible to make increasingly diversified, sophisticated and clinically relevant materials for multiple roles in biomedicine. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Cox process representation and inference for stochastic reaction-diffusion processes

NASA Astrophysics Data System (ADS)

Schnoerr, David; Grima, Ramon; Sanguinetti, Guido

2016-05-01

Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate to observational data. Here we use ideas from statistical physics and machine learning to provide a solution to the inverse problem of learning a stochastic reaction-diffusion process from data. Our solution relies on a non-trivial connection between stochastic reaction-diffusion processes and spatio-temporal Cox processes, a well-studied class of models from computational statistics. This connection leads to an efficient and flexible algorithm for parameter inference and model selection. Our approach shows excellent accuracy on numeric and real data examples from systems biology and epidemiology. Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.

The Evolving Contribution of Mass Spectrometry to Integrative Structural Biology

NASA Astrophysics Data System (ADS)

Faini, Marco; Stengel, Florian; Aebersold, Ruedi

2016-06-01

Protein complexes are key catalysts and regulators for the majority of cellular processes. Unveiling their assembly and structure is essential to understanding their function and mechanism of action. Although conventional structural techniques such as X-ray crystallography and NMR have solved the structure of important protein complexes, they cannot consistently deal with dynamic and heterogeneous assemblies, limiting their applications to small scale experiments. A novel methodological paradigm, integrative structural biology, aims at overcoming such limitations by combining complementary data sources into a comprehensive structural model. Recent applications have shown that a range of mass spectrometry (MS) techniques are able to generate interaction and spatial restraints (cross-linking MS) information on native complexes or to study the stoichiometry and connectivity of entire assemblies (native MS) rapidly, reliably, and from small amounts of substrate. Although these techniques by themselves do not solve structures, they do provide invaluable structural information and are thus ideally suited to contribute to integrative modeling efforts. The group of Brian Chait has made seminal contributions in the use of mass spectrometric techniques to study protein complexes. In this perspective, we honor the contributions of the Chait group and discuss concepts and milestones of integrative structural biology. We also review recent examples of integration of structural MS techniques with an emphasis on cross-linking MS. We then speculate on future MS applications that would unravel the dynamic nature of protein complexes upon diverse cellular states.

Investigating Processes of Materials Formation via Liquid Phase and Cryogenic TEM

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

De Yoreo, James J.; Sommerdijk, Nico

2016-06-14

The formation of materials in solutions is a widespread phenomenon in synthetic, biological and geochemical systems, occurring through dynamic processes of nucleation, self-assembly, crystal growth, and coarsening. The recent advent of liquid phase TEM and advances in cryogenic TEM are transforming our understanding of these phenomena by providing new insights into the underlying physical and chemical mechanisms. The techniques have been applied to metallic and semiconductor nanoparticles, geochemical and biological minerals, electrochemical systems, macromolecular complexes, and selfassembling systems, both organic and inorganic. New instrumentation and methodologies currently on the horizon promise new opportunities for advancing the science of materials synthesis.

Energy deposition processes in biological tissue: nonthermal biohazards seem unlikely in the ultra-high frequency range.

PubMed

Pickard, W F; Moros, E G

2001-02-01

The prospects of ultra high frequency (UHF, 300--3000 MHz) irradiation producing a nonthermal bioeffect are considered theoretically and found to be small. First, a general formula is derived within the framework of macroscopic electrodynamics for the specific absorption rate of microwaves in a biological tissue; this involves the complex Poynting vector, the mass density of the medium, the angular frequency of the electromagnetic field, and the three complex electromagnetic constitutive parameters of the medium. In the frequency ranges used for cellular telephony and personal communication systems, this model predicts that the chief physical loss mechanism will be ionic conduction, with increasingly important contributions from dielectric relaxation as the frequency rises. However, even in a magnetite unit cell within a magnetosome the deposition rate should not exceed 1/10 k(B)T per second. This supports previous arguments for the improbability of biological effects at UHF frequencies unless a mechanism can be found for accumulating energy over time and space and focussing it. Second, three possible nonthermal accumulation mechanisms are then considered and shown to be unlikely: (i) multiphoton absorption processes; (ii) direct electric field effects on ions; (iii) cooperative effects and/or coherent excitations. Finally, it is concluded that the rate of energy deposition from a typical field and within a typical tissue is so small as to make unlikely any significant nonthermal biological effect. Copyright 2001 Wiley-Liss, Inc.

A multimodal imaging workflow to visualize metal mixtures in the human placenta and explore colocalization with biological response markers.

PubMed

Niedzwiecki, Megan M; Austin, Christine; Remark, Romain; Merad, Miriam; Gnjatic, Sacha; Estrada-Gutierrez, Guadalupe; Espejel-Nuñez, Aurora; Borboa-Olivares, Hector; Guzman-Huerta, Mario; Wright, Rosalind J; Wright, Robert O; Arora, Manish

2016-04-01

Fetal exposure to essential and toxic metals can influence life-long health trajectories. The placenta regulates chemical transmission from maternal circulation to the fetus and itself exhibits a complex response to environmental stressors. The placenta can thus be a useful matrix to monitor metal exposures and stress responses in utero, but strategies to explore the biologic effects of metal mixtures in this organ are not well-developed. In this proof-of-concept study, we used laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to measure the distributions of multiple metals in placental tissue from a low-birth-weight pregnancy, and we developed an approach to identify the components of metal mixtures that colocalized with biological response markers. Our novel workflow, which includes custom-developed software tools and algorithms for spatial outlier identification and background subtraction in multidimensional elemental image stacks, enables rapid image processing and seamless integration of data from elemental imaging and immunohistochemistry. Using quantitative spatial statistics, we identified distinct patterns of metal accumulation at sites of inflammation. Broadly, our multiplexed approach can be used to explore the mechanisms mediating complex metal exposures and biologic responses within placentae and other tissue types. Our LA-ICP-MS image processing workflow can be accessed through our interactive R Shiny application 'shinyImaging', which is available at or through our laboratory's website, .

Auditory biological marker of concussion in children

PubMed Central

Kraus, Nina; Thompson, Elaine C.; Krizman, Jennifer; Cook, Katherine; White-Schwoch, Travis; LaBella, Cynthia R.

2016-01-01

Concussions carry devastating potential for cognitive, neurologic, and socio-emotional disease, but no objective test reliably identifies a concussion and its severity. A variety of neurological insults compromise sound processing, particularly in complex listening environments that place high demands on brain processing. The frequency-following response captures the high computational demands of sound processing with extreme granularity and reliably reveals individual differences. We hypothesize that concussions disrupt these auditory processes, and that the frequency-following response indicates concussion occurrence and severity. Specifically, we hypothesize that concussions disrupt the processing of the fundamental frequency, a key acoustic cue for identifying and tracking sounds and talkers, and, consequently, understanding speech in noise. Here we show that children who sustained a concussion exhibit a signature neural profile. They have worse representation of the fundamental frequency, and smaller and more sluggish neural responses. Neurophysiological responses to the fundamental frequency partially recover to control levels as concussion symptoms abate, suggesting a gain in biological processing following partial recovery. Neural processing of sound correctly identifies 90% of concussion cases and clears 95% of control cases, suggesting this approach has practical potential as a scalable biological marker for sports-related concussion and other types of mild traumatic brain injuries. PMID:28005070

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.

Synthetic mixed-signal computation in living cells

PubMed Central

Rubens, Jacob R.; Selvaggio, Gianluca; Lu, Timothy K.

2016-01-01

Living cells implement complex computations on the continuous environmental signals that they encounter. These computations involve both analogue- and digital-like processing of signals to give rise to complex developmental programs, context-dependent behaviours and homeostatic activities. In contrast to natural biological systems, synthetic biological systems have largely focused on either digital or analogue computation separately. Here we integrate analogue and digital computation to implement complex hybrid synthetic genetic programs in living cells. We present a framework for building comparator gene circuits to digitize analogue inputs based on different thresholds. We then demonstrate that comparators can be predictably composed together to build band-pass filters, ternary logic systems and multi-level analogue-to-digital converters. In addition, we interface these analogue-to-digital circuits with other digital gene circuits to enable concentration-dependent logic. We expect that this hybrid computational paradigm will enable new industrial, diagnostic and therapeutic applications with engineered cells. PMID:27255669

Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels

PubMed Central

Hinton, Thomas J.; Jallerat, Quentin; Palchesko, Rachelle N.; Park, Joon Hyung; Grodzicki, Martin S.; Shue, Hao-Jan; Ramadan, Mohamed H.; Hudson, Andrew R.; Feinberg, Adam W.

2015-01-01

We demonstrate the additive manufacturing of complex three-dimensional (3D) biological structures using soft protein and polysaccharide hydrogels that are challenging or impossible to create using traditional fabrication approaches. These structures are built by embedding the printed hydrogel within a secondary hydrogel that serves as a temporary, thermoreversible, and biocompatible support. This process, termed freeform reversible embedding of suspended hydrogels, enables 3D printing of hydrated materials with an elastic modulus <500 kPa including alginate, collagen, and fibrin. Computer-aided design models of 3D optical, computed tomography, and magnetic resonance imaging data were 3D printed at a resolution of ~200 μm and at low cost by leveraging open-source hardware and software tools. Proof-of-concept structures based on femurs, branched coronary arteries, trabeculated embryonic hearts, and human brains were mechanically robust and recreated complex 3D internal and external anatomical architectures. PMID:26601312

Searching and Mining Visually Observed Phenotypes of Maize Mutants

USDA-ARS?s Scientific Manuscript database

There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most curr...

UNDERSTANDING THE ROLE OF OZONE STRESS IN ALTERING BELOWGROUND PROCESSES

EPA Science Inventory

Forested ecosystems are comprised of tremendous biological diversity and functional complexity both above and belowground. Soil ecosystems are known to contain thousands of species, with many more that have not yet been identified. Soil heterotrophic organisms depend on green p...

SYMPOSIUM SESSION PROPOSAL: INCORPORATION OF MODE OF ACTION INTO MECHANISTICALLY-BASED QUANTITATIVE MODELS

EPA Science Inventory

The biological processes by which environmental pollutants induce adverse health effects is most likely regulated by complex interactions dependent upon the route of exposure, dose, kinetics of distribution, and multiple cellular responses. To further complicate deciphering thes...

Multivariate models for skin sensitization hazard and potency

EPA Science Inventory

One of the top priorities being addressed by ICCVAM is the identification and validation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events have been well characterized in an adverse outcome pathw...

MECHANISMS OF MALE REPRODUCTIVE TOXICITY: BED, BATH AND BEYOND

EPA Science Inventory

Male reproductive function depends upon the integration of a great number of highly complex biological processes and their endocrine support. Therefore it is not surprising that male reproductive health can be impaired by exposures to drugs and environmental toxicants that impact...

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

PubMed

Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

2018-01-01

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

Small-angle neutron scattering reveals the assembly mode and oligomeric architecture of TET, a large, dodecameric aminopeptidase

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

Appolaire, Alexandre; Girard, Eric; Colombo, Matteo

2014-11-01

The present work illustrates that small-angle neutron scattering, deuteration and contrast variation, combined with in vitro particle reconstruction, constitutes a very efficient approach to determine subunit architectures in large, symmetric protein complexes. In the case of the 468 kDa heterododecameric TET peptidase machine, it was demonstrated that the assembly of the 12 subunits is a highly controlled process and represents a way to optimize the catalytic efficiency of the enzyme. The specific self-association of proteins into oligomeric complexes is a common phenomenon in biological systems to optimize and regulate their function. However, de novo structure determination of these important complexesmore » is often very challenging for atomic-resolution techniques. Furthermore, in the case of homo-oligomeric complexes, or complexes with very similar building blocks, the respective positions of subunits and their assembly pathways are difficult to determine using many structural biology techniques. Here, an elegant and powerful approach based on small-angle neutron scattering is applied, in combination with deuterium labelling and contrast variation, to elucidate the oligomeric organization of the quaternary structure and the assembly pathways of 468 kDa, hetero-oligomeric and symmetric Pyrococcus horikoshii TET2–TET3 aminopeptidase complexes. The results reveal that the topology of the PhTET2 and PhTET3 dimeric building blocks within the complexes is not casual but rather suggests that their quaternary arrangement optimizes the catalytic efficiency towards peptide substrates. This approach bears important potential for the determination of quaternary structures and assembly pathways of large oligomeric and symmetric complexes in biological systems.« less

Prediction of lung cells oncogenic transformation for induced radon progeny alpha particles using sugarscape cellular automata.

PubMed

Baradaran, Samaneh; Maleknasr, Niaz; Setayeshi, Saeed; Akbari, Mohammad Esmaeil

2014-01-01

Alpha particle irradiation from radon progeny is one of the major natural sources of effective dose in the public population. Oncogenic transformation is a biological effectiveness of radon progeny alpha particle hits. The biological effects which has caused by exposure to radon, were the main result of a complex series of physical, chemical, biological and physiological interactions. The cellular and molecular mechanisms for radon-induced carcinogenesis have not been clear yet. Various biological models, including cultured cells and animals, have been found useful for studying the carcinogenesis effects of radon progeny alpha particles. In this paper, sugars cape cellular automata have been presented for computational study of complex biological effect of radon progeny alpha particles in lung bronchial airways. The model has included mechanism of DNA damage, which has been induced alpha particles hits, and then formation of transformation in the lung cells. Biomarkers were an objective measure or evaluation of normal or abnormal biological processes. In the model, the metabolism rate of infected cell has been induced alpha particles traversals, as a biomarker, has been followed to reach oncogenic transformation. The model results have successfully validated in comparison with "in vitro oncogenic transformation data" for C3H 10T1/2 cells. This model has provided an opportunity to study the cellular and molecular changes, at the various stages in radiation carcinogenesis, involving human cells. It has become well known that simulation could be used to investigate complex biomedical systems, in situations where traditional methodologies were difficult or too costly to employ.

The Biological Big Bang model for the major transitions in evolution.

PubMed

Koonin, Eugene V

2007-08-20

Major transitions in biological evolution show the same pattern of sudden emergence of diverse forms at a new level of complexity. The relationships between major groups within an emergent new class of biological entities are hard to decipher and do not seem to fit the tree pattern that, following Darwin's original proposal, remains the dominant description of biological evolution. The cases in point include the origin of complex RNA molecules and protein folds; major groups of viruses; archaea and bacteria, and the principal lineages within each of these prokaryotic domains; eukaryotic supergroups; and animal phyla. In each of these pivotal nexuses in life's history, the principal "types" seem to appear rapidly and fully equipped with the signature features of the respective new level of biological organization. No intermediate "grades" or intermediate forms between different types are detectable. Usually, this pattern is attributed to cladogenesis compressed in time, combined with the inevitable erosion of the phylogenetic signal. I propose that most or all major evolutionary transitions that show the "explosive" pattern of emergence of new types of biological entities correspond to a boundary between two qualitatively distinct evolutionary phases. The first, inflationary phase is characterized by extremely rapid evolution driven by various processes of genetic information exchange, such as horizontal gene transfer, recombination, fusion, fission, and spread of mobile elements. These processes give rise to a vast diversity of forms from which the main classes of entities at the new level of complexity emerge independently, through a sampling process. In the second phase, evolution dramatically slows down, the respective process of genetic information exchange tapers off, and multiple lineages of the new type of entities emerge, each of them evolving in a tree-like fashion from that point on. This biphasic model of evolution incorporates the previously developed concepts of the emergence of protein folds by recombination of small structural units and origin of viruses and cells from a pre-cellular compartmentalized pool of recombining genetic elements. The model is extended to encompass other major transitions. It is proposed that bacterial and archaeal phyla emerged independently from two distinct populations of primordial cells that, originally, possessed leaky membranes, which made the cells prone to rampant gene exchange; and that the eukaryotic supergroups emerged through distinct, secondary endosymbiotic events (as opposed to the primary, mitochondrial endosymbiosis). This biphasic model of evolution is substantially analogous to the scenario of the origin of universes in the eternal inflation version of modern cosmology. Under this model, universes like ours emerge in the infinite multiverse when the eternal process of exponential expansion, known as inflation, ceases in a particular region as a result of false vacuum decay, a first order phase transition process. The result is the nucleation of a new universe, which is traditionally denoted Big Bang, although this scenario is radically different from the Big Bang of the traditional model of an expanding universe. Hence I denote the phase transitions at the end of each inflationary epoch in the history of life Biological Big Bangs (BBB). A Biological Big Bang (BBB) model is proposed for the major transitions in life's evolution. According to this model, each transition is a BBB such that new classes of biological entities emerge at the end of a rapid phase of evolution (inflation) that is characterized by extensive exchange of genetic information which takes distinct forms for different BBBs. The major types of new forms emerge independently, via a sampling process, from the pool of recombining entities of the preceding generation. This process is envisaged as being qualitatively different from tree-pattern cladogenesis.

The Biological Big Bang model for the major transitions in evolution

PubMed Central

Koonin, Eugene V

2007-01-01

Background Major transitions in biological evolution show the same pattern of sudden emergence of diverse forms at a new level of complexity. The relationships between major groups within an emergent new class of biological entities are hard to decipher and do not seem to fit the tree pattern that, following Darwin's original proposal, remains the dominant description of biological evolution. The cases in point include the origin of complex RNA molecules and protein folds; major groups of viruses; archaea and bacteria, and the principal lineages within each of these prokaryotic domains; eukaryotic supergroups; and animal phyla. In each of these pivotal nexuses in life's history, the principal "types" seem to appear rapidly and fully equipped with the signature features of the respective new level of biological organization. No intermediate "grades" or intermediate forms between different types are detectable. Usually, this pattern is attributed to cladogenesis compressed in time, combined with the inevitable erosion of the phylogenetic signal. Hypothesis I propose that most or all major evolutionary transitions that show the "explosive" pattern of emergence of new types of biological entities correspond to a boundary between two qualitatively distinct evolutionary phases. The first, inflationary phase is characterized by extremely rapid evolution driven by various processes of genetic information exchange, such as horizontal gene transfer, recombination, fusion, fission, and spread of mobile elements. These processes give rise to a vast diversity of forms from which the main classes of entities at the new level of complexity emerge independently, through a sampling process. In the second phase, evolution dramatically slows down, the respective process of genetic information exchange tapers off, and multiple lineages of the new type of entities emerge, each of them evolving in a tree-like fashion from that point on. This biphasic model of evolution incorporates the previously developed concepts of the emergence of protein folds by recombination of small structural units and origin of viruses and cells from a pre-cellular compartmentalized pool of recombining genetic elements. The model is extended to encompass other major transitions. It is proposed that bacterial and archaeal phyla emerged independently from two distinct populations of primordial cells that, originally, possessed leaky membranes, which made the cells prone to rampant gene exchange; and that the eukaryotic supergroups emerged through distinct, secondary endosymbiotic events (as opposed to the primary, mitochondrial endosymbiosis). This biphasic model of evolution is substantially analogous to the scenario of the origin of universes in the eternal inflation version of modern cosmology. Under this model, universes like ours emerge in the infinite multiverse when the eternal process of exponential expansion, known as inflation, ceases in a particular region as a result of false vacuum decay, a first order phase transition process. The result is the nucleation of a new universe, which is traditionally denoted Big Bang, although this scenario is radically different from the Big Bang of the traditional model of an expanding universe. Hence I denote the phase transitions at the end of each inflationary epoch in the history of life Biological Big Bangs (BBB). Conclusion A Biological Big Bang (BBB) model is proposed for the major transitions in life's evolution. According to this model, each transition is a BBB such that new classes of biological entities emerge at the end of a rapid phase of evolution (inflation) that is characterized by extensive exchange of genetic information which takes distinct forms for different BBBs. The major types of new forms emerge independently, via a sampling process, from the pool of recombining entities of the preceding generation. This process is envisaged as being qualitatively different from tree-pattern cladogenesis. Reviewers This article was reviewed by William Martin, Sergei Maslov, and Leonid Mirny. PMID:17708768

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Sensitivity analysis of dynamic biological systems with time-delays.

PubMed

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2010-10-15

Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary. We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention. By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

Biological determination of mental disorders: a discussion based on recent hypotheses from neuroscience.

PubMed

Freitas-Silva, Luna Rodrigues; Ortega, Francisco

2016-08-29

Understanding the processes involved in the development of mental disorders has proven challenging ever since psychiatry was founded as a field. Neuroscience has provided new expectations that an explanation will be found for the development of mental disorders based on biological functioning alone. However, such a goal has not been that easy to achieve, and new hypotheses have begun to appear in neuroscience research. In this article we identify epigenetics, neurodevelopment, and plasticity as the principal avenues for a new understanding of the biology of mental phenomena. Genetic complexity, the environment's formative role, and variations in vulnerability involve important changes in the principal hypotheses on biological determination of mental disorders, suggesting a reconfiguration of the limits between the "social" and the "biological" in neuroscience research.

Teaching Electrostatics and Entropy in Introductory Physics

NASA Astrophysics Data System (ADS)

Reeves, Mark

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 courses is important contribution of the entropy in driving fundamental biological processes towards equilibrium. I will present material developed to teach electrostatic screening in solutions and the function of nerve cells where entropic effects act to counterbalance electrostatic attraction. These ideas are taught in an introductory, calculus-based physics course to biomedical engineers using SCALEUP pedagogy. Results of student mastering of complex problems that cross disciplinary boundaries between biology and physics, as well as the challenges that they face in learning this material will be presented.

General and craniofacial development are complex adaptive processes influenced by diversity.

PubMed

Brook, A H; O'Donnell, M Brook; Hone, A; Hart, E; Hughes, T E; Smith, R N; Townsend, G C

2014-06-01

Complex systems are present in such diverse areas as social systems, economies, ecosystems and biology and, therefore, are highly relevant to dental research, education and practice. A Complex Adaptive System in biological development is a dynamic process in which, from interacting components at a lower level, higher level phenomena and structures emerge. Diversity makes substantial contributions to the performance of complex adaptive systems. It enhances the robustness of the process, allowing multiple responses to external stimuli as well as internal changes. From diversity comes variation in outcome and the possibility of major change; outliers in the distribution enhance the tipping points. The development of the dentition is a valuable, accessible model with extensive and reliable databases for investigating the role of complex adaptive systems in craniofacial and general development. The general characteristics of such systems are seen during tooth development: self-organization; bottom-up emergence; multitasking; self-adaptation; variation; tipping points; critical phases; and robustness. Dental findings are compatible with the Random Network Model, the Threshold Model and also with the Scale Free Network Model which has a Power Law distribution. In addition, dental development shows the characteristics of Modularity and Clustering to form Hierarchical Networks. The interactions between the genes (nodes) demonstrate Small World phenomena, Subgraph Motifs and Gene Regulatory Networks. Genetic mechanisms are involved in the creation and evolution of variation during development. The genetic factors interact with epigenetic and environmental factors at the molecular level and form complex networks within the cells. From these interactions emerge the higher level tissues, tooth germs and mineralized teeth. Approaching development in this way allows investigation of why there can be variations in phenotypes from identical genotypes; the phenotype is the outcome of perturbations in the cellular systems and networks, as well as of the genotype. Understanding and applying complexity theory will bring about substantial advances not only in dental research and education but also in the organization and delivery of oral health care. © 2014 Australian Dental Association.

Studying light-harvesting models with superconducting circuits.

PubMed

Potočnik, Anton; Bargerbos, Arno; Schröder, Florian A Y N; Khan, Saeed A; Collodo, Michele C; Gasparinetti, Simone; Salathé, Yves; Creatore, Celestino; Eichler, Christopher; Türeci, Hakan E; Chin, Alex W; Wallraff, Andreas

2018-03-02

The process of photosynthesis, the main source of energy in the living world, converts sunlight into chemical energy. The high efficiency of this process is believed to be enabled by an interplay between the quantum nature of molecular structures in photosynthetic complexes and their interaction with the environment. Investigating these effects in biological samples is challenging due to their complex and disordered structure. Here we experimentally demonstrate a technique for studying photosynthetic models based on superconducting quantum circuits, which complements existing experimental, theoretical, and computational approaches. We demonstrate a high degree of freedom in design and experimental control of our approach based on a simplified three-site model of a pigment protein complex with realistic parameters scaled down in energy by a factor of 10 5 . We show that the excitation transport between quantum-coherent sites disordered in energy can be enabled through the interaction with environmental noise. We also show that the efficiency of the process is maximized for structured noise resembling intramolecular phononic environments found in photosynthetic complexes.

Antiquity of the biological sulphur cycle: evidence from sulphur and carbon isotopes in 2700 million-year-old rocks of the Belingwe Belt, Zimbabwe.

PubMed Central

Grassineau, N V; Nisbet, E G; Bickle, M J; Fowler, C M; Lowry, D; Mattey, D P; Abell, P; Martin, A

2001-01-01

Sulphur and carbon isotopic analyses on small samples of kerogens and sulphide minerals from biogenic and non-biogenic sediments of the 2.7 x 10(9) years(Ga)-old Belingwe Greenstone Belt (Zimbabwe) imply that a complex biological sulphur cycle was in operation. Sulphur isotopic compositions display a wider range of biological fractionation than hitherto reported from the Archaean. Carbon isotopic values in kerogen record fractionations characteristic of rubisco activity methanogenesis and methylotrophy and possibly anoxygenic photosynthesis. Carbon and sulphur isotopic fractionations have been interpreted in terms of metabolic processes in 2.7 Ga prokaryote mat communities, and indicate the operation of a diverse array of metabolic processes. The results are consistent with models of early molecular evolution derived from ribosomal RNA. PMID:11209879

Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits.

PubMed

Ippolito, A; Todeschini, R; Vighi, M

2012-03-01

Assessing the sensitivity of different species to chemicals is one of the key points in predicting the effects of toxic compounds in the environment. Trait-based predicting methods have proved to be extremely efficient for assessing the sensitivity of macroinvertebrates toward compounds with non specific toxicity (narcotics). Nevertheless, predicting the sensitivity of organisms toward compounds with specific toxicity is much more complex, since it depends on the mode of action of the chemical. The aim of this work was to predict the sensitivity of several freshwater macroinvertebrates toward three classes of plant protection products: organophosphates, carbamates and pyrethroids. Two databases were built: one with sensitivity data (retrieved, evaluated and selected from the U.S. Environmental Protection Agency ECOTOX database) and the other with biological traits. Aside from the "traditional" traits usually considered in ecological analysis (i.e. body size, respiration technique, feeding habits, etc.), multivariate analysis was used to relate the sensitivity of organisms to some other characteristics which may be involved in the process of intoxication. Results confirmed that, besides traditional biological traits, related to uptake capability (e.g. body size and body shape) some traits more related to particular metabolic characteristics or patterns have a good predictive capacity on the sensitivity to these kinds of toxic substances. For example, behavioral complexity, assumed as an indicator of nervous system complexity, proved to be an important predictor of sensitivity towards these compounds. These results confirm the need for more complex traits to predict effects of highly specific substances. One key point for achieving a complete mechanistic understanding of the process is the choice of traits, whose role in the discrimination of sensitivity should be clearly interpretable, and not only statistically significant.

Pubertal Timing and Adolescent Sexual Behavior in Girls

ERIC Educational Resources Information Center

Moore, Sarah R.; Harden, K. Paige; Mendle, Jane

2014-01-01

Girls who experience earlier pubertal timing relative to peers also exhibit earlier timing of sexual intercourse and more unstable sexual relationships. Although pubertal development initiates feelings of physical desire, the transition into romantic and sexual relationships involves complex biological and social processes contributing both to…

Reduction of N2 by supported tungsten clusters gives a model of the process by nitrogenase

PubMed Central

Murakami, Junichi; Yamaguchi, Wataru

2012-01-01

Metalloenzymes catalyze difficult chemical reactions under mild conditions. Mimicking their functions is a challenging task and it has been investigated using homogeneous systems containing metal complexes. The nitrogenase that converts N2 to NH3 under mild conditions is one of such enzymes. Efforts to realize the biological function have continued for more than four decades, which has resulted in several reports of reduction of N2, ligated to metal complexes in solutions, to NH3 by protonation under mild conditions. Here, we show that seemingly distinct supported small tungsten clusters in a dry environment reduce N2 under mild conditions like the nitrogenase. N2 is reduced to NH3 via N2H4 by addition of neutral H atoms, which agrees with the mechanism recently proposed for the N2 reduction on the active site of nitrogenase. The process on the supported clusters gives a model of the biological N2 reduction. PMID:22586517

Hierarchical coordinate systems for understanding complexity and its evolution, with applications to genetic regulatory networks.

PubMed

Egri-Nagy, Attila; Nehaniv, Chrystopher L

2008-01-01

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

Excitation energy transfer in photosynthetic protein-pigment complexes

NASA Astrophysics Data System (ADS)

Yeh, Shu-Hao

Quantum biology is a relatively new research area which investigates the rules that quantum mechanics plays in biology. One of the most intriguing systems in this field is the coherent excitation energy transport (EET) in photosynthesis. In this document I will discuss the theories that are suitable for describing the photosynthetic EET process and the corresponding numerical results on several photosynthetic protein-pigment complexes (PPCs). In some photosynthetic EET processes, because of the electronic coupling between the chromophores within the system is about the same order of magnitude as system-bath coupling (electron-phonon coupling), a non-perturbative method called hierarchy equation of motion (HEOM) is applied to study the EET dynamics. The first part of this thesis includes brief introduction and derivation to the HEOM approach. The second part of this thesis the HEOM method will be applied to investigate the EET process within the B850 ring of the light harvesting complex 2 (LH2) from purple bacteria, Rhodopseudomonas acidophila. The dynamics of the exciton population and coherence will be analyzed under different initial excitation configurations and temperatures. Finally, how HEOM can be implemented to simulate the two-dimensional electronic spectra of photosynthetic PPCs will be discussed. Two-dimensional electronic spectroscopy is a crucial experimental technique to probe EET dynamics in multi-chromophoric systems. The system we are interested in is the 7-chromophore Fenna-Matthews-Olson (FMO) complex from green sulfur bacteria, Prosthecochloris aestuarii. Recent crystallographic studies report the existence of an additional (eighth) chromophore in some of the FMO monomers. By applying HEOM we are able to calculate the two-dimensional electronic spectra of the 7-site and 8-site FMO complexes and investigate the functionality of the eighth chromophore.

Potential of Continuous Manufacturing for Liposomal Drug Products.

PubMed

Worsham, Robert D; Thomas, Vaughan; Farid, Suzanne S

2018-05-21

Over the last several years, continuous manufacturing of pharmaceuticals has evolved from bulk APIs and solid oral dosages into the more complex realm of biologics. The development of continuous downstream processing techniques has allowed biologics manufacturing to realize the benefits (e.g. improved economics, more consistent quality) that come with continuous processing. If relevant processing techniques and principles are selected, the opportunity arises to develop continuous manufacturing designs for additional pharmaceutical products including liposomal drug formulations. Liposome manufacturing has some inherent aspects that make it favorable for a continuous process. Other aspects such as formulation refinement, materials of construction, and aseptic processing need development, but present an achievable challenge. This paper reviews the current state of continuous manufacturing technology applicable to liposomal drug product manufacturing and an assessment of the challenges and potential of this application. This article is protected by copyright. All rights reserved.

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information.

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

Aimone, James Bradley; Bernard, Michael Lewis; Vineyard, Craig Michael

2014-10-01

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach bymore » which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.« less

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.

The nearly neutral and selection theories of molecular evolution under the fisher geometrical framework: substitution rate, population size, and complexity.

PubMed

Razeto-Barry, Pablo; Díaz, Javier; Vásquez, Rodrigo A

2012-06-01

The general theories of molecular evolution depend on relatively arbitrary assumptions about the relative distribution and rate of advantageous, deleterious, neutral, and nearly neutral mutations. The Fisher geometrical model (FGM) has been used to make distributions of mutations biologically interpretable. We explored an FGM-based molecular model to represent molecular evolutionary processes typically studied by nearly neutral and selection models, but in which distributions and relative rates of mutations with different selection coefficients are a consequence of biologically interpretable parameters, such as the average size of the phenotypic effect of mutations and the number of traits (complexity) of organisms. A variant of the FGM-based model that we called the static regime (SR) represents evolution as a nearly neutral process in which substitution rates are determined by a dynamic substitution process in which the population's phenotype remains around a suboptimum equilibrium fitness produced by a balance between slightly deleterious and slightly advantageous compensatory substitutions. As in previous nearly neutral models, the SR predicts a negative relationship between molecular evolutionary rate and population size; however, SR does not have the unrealistic properties of previous nearly neutral models such as the narrow window of selection strengths in which they work. In addition, the SR suggests that compensatory mutations cannot explain the high rate of fixations driven by positive selection currently found in DNA sequences, contrary to what has been previously suggested. We also developed a generalization of SR in which the optimum phenotype can change stochastically due to environmental or physiological shifts, which we called the variable regime (VR). VR models evolution as an interplay between adaptive processes and nearly neutral steady-state processes. When strong environmental fluctuations are incorporated, the process becomes a selection model in which evolutionary rate does not depend on population size, but is critically dependent on the complexity of organisms and mutation size. For SR as well as VR we found that key parameters of molecular evolution are linked by biological factors, and we showed that they cannot be fixed independently by arbitrary criteria, as has usually been assumed in previous molecular evolutionary models.

The Nearly Neutral and Selection Theories of Molecular Evolution Under the Fisher Geometrical Framework: Substitution Rate, Population Size, and Complexity

PubMed Central

Razeto-Barry, Pablo; Díaz, Javier; Vásquez, Rodrigo A.

2012-01-01

The general theories of molecular evolution depend on relatively arbitrary assumptions about the relative distribution and rate of advantageous, deleterious, neutral, and nearly neutral mutations. The Fisher geometrical model (FGM) has been used to make distributions of mutations biologically interpretable. We explored an FGM-based molecular model to represent molecular evolutionary processes typically studied by nearly neutral and selection models, but in which distributions and relative rates of mutations with different selection coefficients are a consequence of biologically interpretable parameters, such as the average size of the phenotypic effect of mutations and the number of traits (complexity) of organisms. A variant of the FGM-based model that we called the static regime (SR) represents evolution as a nearly neutral process in which substitution rates are determined by a dynamic substitution process in which the population’s phenotype remains around a suboptimum equilibrium fitness produced by a balance between slightly deleterious and slightly advantageous compensatory substitutions. As in previous nearly neutral models, the SR predicts a negative relationship between molecular evolutionary rate and population size; however, SR does not have the unrealistic properties of previous nearly neutral models such as the narrow window of selection strengths in which they work. In addition, the SR suggests that compensatory mutations cannot explain the high rate of fixations driven by positive selection currently found in DNA sequences, contrary to what has been previously suggested. We also developed a generalization of SR in which the optimum phenotype can change stochastically due to environmental or physiological shifts, which we called the variable regime (VR). VR models evolution as an interplay between adaptive processes and nearly neutral steady-state processes. When strong environmental fluctuations are incorporated, the process becomes a selection model in which evolutionary rate does not depend on population size, but is critically dependent on the complexity of organisms and mutation size. For SR as well as VR we found that key parameters of molecular evolution are linked by biological factors, and we showed that they cannot be fixed independently by arbitrary criteria, as has usually been assumed in previous molecular evolutionary models. PMID:22426879

Cardiac Arrhythmia: In vivo screening in the zebrafish to overcome complexity in drug discovery.

PubMed

Macrae, Calum A

2010-07-01

IMPORTANCE OF THE FIELD: Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. AREAS COVERED IN THIS REVIEW: Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. WHAT THE READER WILL GAIN: An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. TAKE HOME MESSAGE: In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease biology, but emphasizes the importance of the rigor of the disease model.

Editorial Commentary: Paving a Road Requires a Well-Mixed Cement Stem Cells, Platelet-Rich Plasma, and Shoulder Rotator Cuff Healing.

PubMed

Maffulli, Nicola

2018-03-01

The process of healing in musculoskeletal tissues is complex, and the addition of devices, including platelet-rich plasma and mesenchymal stem cells, to biologically enhance it may favor its optimization. This work shows in a compelling fashion that it is possible to produce the right admixture of physical and biological factors to make it happen in rotator cuff repair. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Divergent Synthesis and Real-Time Biological Annotation of Optically Active Tetrahydrocyclopenta[c]pyranone Derivatives

PubMed Central

2016-01-01

Sp3-rich compounds are underrepresented in libraries for probe- and drug-discovery, despite their promise of extending the range of accessible molecular shapes beyond planar geometries. With this in mind, a collection of single-enantiomer bicyclic, fused cyclopentenones underpinned by a complexity-generating Pauson–Khand cyclization was synthesized. A fingerprint of biological actions of these compounds was determined immediately after synthesis using real-time annotation−a process relying on multiplexed measurements of alterations in cell morphological features. PMID:27978655

Two-Dimensional Spectroscopy Is Being Used to Address Core Scientific Questions in Biology and Materials Science.

PubMed

Petti, Megan K; Lomont, Justin P; Maj, Michał; Zanni, Martin T

2018-02-15

Two-dimensional spectroscopy is a powerful tool for extracting structural and dynamic information from a wide range of chemical systems. We provide a brief overview of the ways in which two-dimensional visible and infrared spectroscopies are being applied to elucidate fundamental details of important processes in biological and materials science. The topics covered include amyloid proteins, photosynthetic complexes, ion channels, photovoltaics, batteries, as well as a variety of promising new methods in two-dimensional spectroscopy.

Optical radiation measurements: instrumentation and sources of error.

PubMed

Landry, R J; Andersen, F A

1982-07-01

Accurate measurement of optical radiation is required when sources of this radiation are used in biological research. The most difficult measurements of broadband noncoherent optical radiations usually must be performed by a highly trained specialist using sophisticated, complex, and expensive instruments. Presentation of the results of such measurement requires correct use of quantities and units with which many biological researchers are unfamiliar. The measurement process, physical quantities and units, measurement systems with instruments, and sources of error and uncertainties associated with optical radiation measurements are reviewed.

78 FR 58311 - Complex Issues in Developing Drug and Biological Products for Rare Diseases; Public Workshop...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-23

...] Complex Issues in Developing Drug and Biological Products for Rare Diseases; Public Workshop; Request for... Issues in Developing Drug and Biological Products for Rare Diseases.'' The purpose of the public workshop is twofold: To discuss complex issues in clinical trials for developing drug and biological products...

What is the evidence for the use of biologic or biosynthetic meshes in abdominal wall reconstruction?

PubMed

Köckerling, F; Alam, N N; Antoniou, S A; Daniels, I R; Famiglietti, F; Fortelny, R H; Heiss, M M; Kallinowski, F; Kyle-Leinhase, I; Mayer, F; Miserez, M; Montgomery, A; Morales-Conde, S; Muysoms, F; Narang, S K; Petter-Puchner, A; Reinpold, W; Scheuerlein, H; Smietanski, M; Stechemesser, B; Strey, C; Woeste, G; Smart, N J

2018-04-01

Although many surgeons have adopted the use of biologic and biosynthetic meshes in complex abdominal wall hernia repair, others have questioned the use of these products. Criticism is addressed in several review articles on the poor standard of studies reporting on the use of biologic meshes for different abdominal wall repairs. The aim of this consensus review is to conduct an evidence-based analysis of the efficacy of biologic and biosynthetic meshes in predefined clinical situations. A European working group, "BioMesh Study Group", composed of invited surgeons with a special interest in surgical meshes, formulated key questions, and forwarded them for processing in subgroups. In January 2016, a workshop was held in Berlin where the findings were presented, discussed, and voted on for consensus. Findings were set out in writing by the subgroups followed by consensus being reached. For the review, 114 studies and background analyses were used. The cumulative data regarding biologic mesh under contaminated conditions do not support the claim that it is better than synthetic mesh. Biologic mesh use should be avoided when bridging is needed. In inguinal hernia repair biologic and biosynthetic meshes do not have a clear advantage over the synthetic meshes. For prevention of incisional or parastomal hernias, there is no evidence to support the use of biologic/biosynthetic meshes. In complex abdominal wall hernia repairs (incarcerated hernia, parastomal hernia, infected mesh, open abdomen, enterocutaneous fistula, and component separation technique), biologic and biosynthetic meshes do not provide a superior alternative to synthetic meshes. The routine use of biologic and biosynthetic meshes cannot be recommended.

Integrated Information Increases with Fitness in the Evolution of Animats

PubMed Central

Edlund, Jeffrey A.; Chaumont, Nicolas; Hintze, Arend; Koch, Christof; Tononi, Giulio; Adami, Christoph

2011-01-01

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent (“animat”) evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its “fit” to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data. PMID:22028639

Binding of small molecules at interface of protein-protein complex - A newer approach to rational drug design.

PubMed

Gurung, A B; Bhattacharjee, A; Ajmal Ali, M; Al-Hemaid, F; Lee, Joongku

2017-02-01

Protein-protein interaction is a vital process which drives many important physiological processes in the cell and has also been implicated in several diseases. Though the protein-protein interaction network is quite complex but understanding its interacting partners using both in silico as well as molecular biology techniques can provide better insights for targeting such interactions. Targeting protein-protein interaction with small molecules is a challenging task because of druggability issues. Nevertheless, several studies on the kinetics as well as thermodynamic properties of protein-protein interactions have immensely contributed toward better understanding of the affinity of these complexes. But, more recent studies on hot spots and interface residues have opened up new avenues in the drug discovery process. This approach has been used in the design of hot spot based modulators targeting protein-protein interaction with the objective of normalizing such interactions.

Bio-jETI: a service integration, design, and provisioning platform for orchestrated bioinformatics processes.

PubMed

Margaria, Tiziana; Kubczak, Christian; Steffen, Bernhard

2008-04-25

With Bio-jETI, we introduce a service platform for interdisciplinary work on biological application domains and illustrate its use in a concrete application concerning statistical data processing in R and xcms for an LC/MS analysis of FAAH gene knockout. Bio-jETI uses the jABC environment for service-oriented modeling and design as a graphical process modeling tool and the jETI service integration technology for remote tool execution. As a service definition and provisioning platform, Bio-jETI has the potential to become a core technology in interdisciplinary service orchestration and technology transfer. Domain experts, like biologists not trained in computer science, directly define complex service orchestrations as process models and use efficient and complex bioinformatics tools in a simple and intuitive way.

Biosimilars for Immune-Mediated Chronic Diseases in Primary Care: What a Practicing Physician Needs to Know.

PubMed

Feldman, Steven R; Bagel, Jerry; Namak, Shahla

2018-05-01

The introduction of biologics has revolutionized the treatment of immune-mediated diseases, but high cost and limited patient access remain hurdles, and some physicians are concerned that biosimilars are not similar enough. The purpose of this narrative review is to describe biosimilar safety, efficacy, nomenclature, extrapolation and interchangeability. In the United States, the Biologics Price Competition and Innovation Act created an abbreviated pathway for licensing of a biologic that is biosimilar to another licensed product (i.e., the reference product). This approval pathway differs from that of generic small-molecule drugs because biologics are too complex to be perfectly duplicated, and follows a process designed to demonstrate that any differences between the biosimilar and its reference product have no significant impact on safety and efficacy. The US approval process requires extensive analytical assessments, animal studies and clinical trials, assuring that biosimilar products provide clinical results similar to those of the reference product. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Remotely controlled fusion of selected vesicles and living cells: a key issue review

NASA Astrophysics Data System (ADS)

Bahadori, Azra; Moreno-Pescador, Guillermo; Oddershede, Lene B.; Bendix, Poul M.

2018-03-01

Remote control over fusion of single cells and vesicles has a great potential in biological and chemical research allowing both transfer of genetic material between cells and transfer of molecular content between vesicles. Membrane fusion is a critical process in biology that facilitates molecular transport and mixing of cellular cytoplasms with potential formation of hybrid cells. Cells precisely regulate internal membrane fusions with the aid of specialized fusion complexes that physically provide the energy necessary for mediating fusion. Physical factors like membrane curvature, tension and temperature, affect biological membrane fusion by lowering the associated energy barrier. This has inspired the development of physical approaches to harness the fusion process at a single cell level by using remotely controlled electromagnetic fields to trigger membrane fusion. Here, we critically review various approaches, based on lasers or electric pulses, to control fusion between individual cells or between individual lipid vesicles and discuss their potential and limitations for present and future applications within biochemistry, biology and soft matter.

Biological treatment of winery wastewater: an overview.

PubMed

Andreottola, G; Foladori, P; Ziglio, G

2009-01-01

The treatment of winery wastewater can realised using several biological processes based both on aerobic or anaerobic systems using suspended biomass or biofilms. Several systems are currently offered by technology providers and current research envisages the availability of new promising technologies for winery wastewater treatment. The present paper intends to present a brief state of the art of the existing status and advances in biological treatment of winery wastewater in the last decade, considering both lab, pilot and full-scale studies. Advantages, drawbacks, applied organic loads, removal efficiency and emerging aspects of the main biological treatments were considered and compared. Nevertheless in most treatments the COD removal efficiency was around 90-95% (remaining COD is due to the un-biodegradable soluble fraction), the applied organic loads are very different depending on the applied technology, varying for an order of magnitude. Applied organic loads are higher in biofilm systems than in suspended biomass while anaerobic biofilm processes have the smaller footprint but in general a higher level of complexity.

Biomimetics: lessons from nature--an overview.

PubMed

Bhushan, Bharat

2009-04-28

Nature has developed materials, objects and processes that function from the macroscale to the nanoscale. These have gone through evolution over 3.8 Gyr. The emerging field of biomimetics allows one to mimic biology or nature to develop nanomaterials, nanodevices and processes. Properties of biological materials and surfaces result from a complex interplay between surface morphology and physical and chemical properties. Hierarchical structures with dimensions of features ranging from the macroscale to the nanoscale are extremely common in nature to provide properties of interest. Molecular-scale devices, superhydrophobicity, self-cleaning, drag reduction in fluid flow, energy conversion and conservation, high adhesion, reversible adhesion, aerodynamic lift, materials and fibres with high mechanical strength, biological self-assembly, antireflection, structural coloration, thermal insulation, self-healing and sensory-aid mechanisms are some of the examples found in nature that are of commercial interest. This paper provides a broad overview of the various objects and processes of interest found in nature and applications under development or available in the marketplace.

Satellite Cells and the Muscle Stem Cell Niche

PubMed Central

Yin, Hang; Price, Feodor

2013-01-01

Adult skeletal muscle in mammals is a stable tissue under normal circumstances but has remarkable ability to repair after injury. Skeletal muscle regeneration is a highly orchestrated process involving the activation of various cellular and molecular responses. As skeletal muscle stem cells, satellite cells play an indispensible role in this process. The self-renewing proliferation of satellite cells not only maintains the stem cell population but also provides numerous myogenic cells, which proliferate, differentiate, fuse, and lead to new myofiber formation and reconstitution of a functional contractile apparatus. The complex behavior of satellite cells during skeletal muscle regeneration is tightly regulated through the dynamic interplay between intrinsic factors within satellite cells and extrinsic factors constituting the muscle stem cell niche/microenvironment. For the last half century, the advance of molecular biology, cell biology, and genetics has greatly improved our understanding of skeletal muscle biology. Here, we review some recent advances, with focuses on functions of satellite cells and their niche during the process of skeletal muscle regeneration. PMID:23303905

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.

Bone fracture healing in mechanobiological modeling: A review of principles and methods.

PubMed

Ghiasi, Mohammad S; Chen, Jason; Vaziri, Ashkan; Rodriguez, Edward K; Nazarian, Ara

2017-06-01

Bone fracture is a very common body injury. The healing process is physiologically complex, involving both biological and mechanical aspects. Following a fracture, cell migration, cell/tissue differentiation, tissue synthesis, and cytokine and growth factor release occur, regulated by the mechanical environment. Over the past decade, bone healing simulation and modeling has been employed to understand its details and mechanisms, to investigate specific clinical questions, and to design healing strategies. The goal of this effort is to review the history and the most recent work in bone healing simulations with an emphasis on both biological and mechanical properties. Therefore, we provide a brief review of the biology of bone fracture repair, followed by an outline of the key growth factors and mechanical factors influencing it. We then compare different methodologies of bone healing simulation, including conceptual modeling (qualitative modeling of bone healing to understand the general mechanisms), biological modeling (considering only the biological factors and processes), and mechanobiological modeling (considering both biological aspects and mechanical environment). Finally we evaluate different components and clinical applications of bone healing simulation such as mechanical stimuli, phases of bone healing, and angiogenesis.

Biological Perspectives of Delayed Fracture Healing

PubMed Central

Hankenson, KD; Zmmerman, G; Marcucio, R

2015-01-01

Fracture healing is a complex biological process that requires interaction among a series of different cell types. Maintaining the appropriate temporal progression and spatial pattern is essential to achieve robust healing. We can temporally assess the biological phases via gene expression, protein analysis, histologically, or non-invasively using biomarkers as well as imaging techniques. However, determining what leads to normal verses abnormal healing is more challenging. Since the ultimate outcome of the process of fracture healing is to restore the original functions of bone, assessment of fracture healing should include not only monitoring the restoration of structure and mechanical function, but also an evaluation of the restoration of normal bone biology. Currently very few non-invasive measures of the biology of healing exist; however, recent studies that have correlated non-invasive measures with fracture healing outcome in humans have shown that serum TGFbeta1 levels appear to be an indicator of healing vs non-healing. In the future, developing additional serum measures to assess biological healing will improve the reliability and permit us to assess stages of fracture healing. Additionally, new functional imaging technologies could prove useful for better understanding both normal fracture healing and predicting dysfunctional healing in human patients. PMID:24857030

Agent-based models in translational systems biology

PubMed Central

An, Gary; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram

2013-01-01

Effective translational methodologies for knowledge representation are needed in order to make strides against the constellation of diseases that affect the world today. These diseases are defined by their mechanistic complexity, redundancy, and nonlinearity. Translational systems biology aims to harness the power of computational simulation to streamline drug/device design, simulate clinical trials, and eventually to predict the effects of drugs on individuals. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggests that this modeling framework is well suited for translational systems biology. This review describes agent-based modeling and gives examples of its translational applications in the context of acute inflammation and wound healing. PMID:20835989

High-Content Screening for Quantitative Cell Biology.

PubMed

Mattiazzi Usaj, Mojca; Styles, Erin B; Verster, Adrian J; Friesen, Helena; Boone, Charles; Andrews, Brenda J

2016-08-01

High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future. Copyright © 2016 Elsevier Ltd. All rights reserved.

[The Biology of Learning].

PubMed

Campo-Cabal, Gerardo

2012-01-01

The effort to relate mental and biological functioning has fluctuated between two doctrines: 1) an attempt to explain mental functioning as a collective property of the brain and 2) as one relatied to other mental processes associated with specific regions of the brain. The article reviews the main theories developed over the last 200 years: phrenology, the psuedo study of the brain, mass action, cellular connectionism and distributed processing among others. In addition, approaches have emerged in recent years that allows for an understanding of the biological determinants and individual differences in complex mental processes through what is called cognitive neuroscience. Knowing the definition of neuroscience, the learning of memory, the ways in which learning occurs, the principles of the neural basis of memory and learning and its effects on brain function, among other things, allows us the basic understanding of the processes of memory and learning and is an important requirement to address the best manner to commit to the of training future specialists in Psychiatry. Copyright © 2012 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

How to Train a Cell - Cutting-Edge Molecular Tools

NASA Astrophysics Data System (ADS)

Czapiński, Jakub; Kiełbus, Michał; Kałafut, Joanna; Kos, Michał; Stepulak, Andrzej; Rivero-Müller, Adolfo

2017-03-01

In biological systems, the formation of molecular complexes is the currency for all cellular processes. Traditionally, functional experimentation was targeted to single molecular players in order to understand its effects in a cell or animal phenotype. In the last few years, we have been experiencing rapid progress in the development of ground-breaking molecular biology tools that affect the metabolic, structural, morphological, and (epi)genetic instructions of cells by chemical, optical (optogenetic) and mechanical inputs. Such precise dissection of cellular processes is not only essential for a better understanding of biological systems, but will also allow us to better diagnose and fix common dysfunctions. Here, we present several of these emerging and innovative techniques by providing the reader with elegant examples on how these tools have been implemented in cells, and, in some cases, organisms, to unravel molecular processes in minute detail. We also discuss their advantages and disadvantages with particular focus on their translation to multicellular organisms for in vivo spatiotemporal regulation. We envision that further developments of these tools will not only help solve the processes of life, but will give rise to novel clinical and industrial applications.

A Systems Biology Approach to Iron Metabolism

PubMed Central

Chifman, J.; Laubenbacher, R.; Torti, S.V.

2015-01-01

Iron is critical to the survival of almost all living organisms. However, inappropriately low or high levels of iron are detrimental and contribute to a wide range of diseases. Recent advances in the study of iron metabolism have revealed multiple intricate pathways that are essential to the maintenance of iron homeostasis. Further, iron regulation involves processes at several scales, ranging from the subcellular to the organismal. This complexity makes a systems biology approach crucial, with its enabling technology of computational models based on a mathematical description of regulatory systems. Systems biology may represent a new strategy for understanding imbalances in iron metabolism and their underlying causes. PMID:25480643

[History and biology: possible dialogues, necessary distances].

PubMed

Duarte, Regina Horta

2009-01-01

Evolution has often been rejected as a theory incompatible with proper historical reflection. While there are undoubtedly insurmountable barriers between biology and the study of man and society, a rigorous analysis of Darwinist theory demonstrates epistemological areas of contact between history and evolutionary biology. The amazing temporal perspective shared by both areas of knowledge points to some bridges of communication, like the importance of the event and of creation processes, the rejection of teleology and the idea of progress, the complexity of events between chance and necessity, and the impossibility of making predictions. This affords an opportunity for a transdisciplinary approach at a moment of various contemporary challenges.

Enzyme-Activated Fluorogenic Probes for Live-Cell and in Vivo Imaging.

PubMed

Chyan, Wen; Raines, Ronald T

2018-06-20

Fluorogenic probes, small-molecule sensors that unmask brilliant fluorescence upon exposure to specific stimuli, are powerful tools for chemical biology. Those probes that respond to enzymatic activity illuminate the complex dynamics of biological processes at a level of spatiotemporal detail and sensitivity unmatched by other techniques. Here, we review recent advances in enzyme-activated fluorogenic probes for biological imaging. We organize our survey by enzyme classification, with emphasis on fluorophore masking strategies, modes of enzymatic activation, and the breadth of current and future applications. Key challenges such as probe selectivity and spectroscopic requirements are described alongside of therapeutic, diagnostic, and theranostic opportunities.

Hybrid deterministic/stochastic simulation of complex biochemical systems.

PubMed

Lecca, Paola; Bagagiolo, Fabio; Scarpa, Marina

2017-11-21

In a biological cell, cellular functions and the genetic regulatory apparatus are implemented and controlled by complex networks of chemical reactions involving genes, proteins, and enzymes. Accurate computational models are indispensable means for understanding the mechanisms behind the evolution of a complex system, not always explored with wet lab experiments. To serve their purpose, computational models, however, should be able to describe and simulate the complexity of a biological system in many of its aspects. Moreover, it should be implemented by efficient algorithms requiring the shortest possible execution time, to avoid enlarging excessively the time elapsing between data analysis and any subsequent experiment. Besides the features of their topological structure, the complexity of biological networks also refers to their dynamics, that is often non-linear and stiff. The stiffness is due to the presence of molecular species whose abundance fluctuates by many orders of magnitude. A fully stochastic simulation of a stiff system is computationally time-expensive. On the other hand, continuous models are less costly, but they fail to capture the stochastic behaviour of small populations of molecular species. We introduce a new efficient hybrid stochastic-deterministic computational model and the software tool MoBioS (MOlecular Biology Simulator) implementing it. The mathematical model of MoBioS uses continuous differential equations to describe the deterministic reactions and a Gillespie-like algorithm to describe the stochastic ones. Unlike the majority of current hybrid methods, the MoBioS algorithm divides the reactions' set into fast reactions, moderate reactions, and slow reactions and implements a hysteresis switching between the stochastic model and the deterministic model. Fast reactions are approximated as continuous-deterministic processes and modelled by deterministic rate equations. Moderate reactions are those whose reaction waiting time is greater than the fast reaction waiting time but smaller than the slow reaction waiting time. A moderate reaction is approximated as a stochastic (deterministic) process if it was classified as a stochastic (deterministic) process at the time at which it crosses the threshold of low (high) waiting time. A Gillespie First Reaction Method is implemented to select and execute the slow reactions. The performances of MoBios were tested on a typical example of hybrid dynamics: that is the DNA transcription regulation. The simulated dynamic profile of the reagents' abundance and the estimate of the error introduced by the fully deterministic approach were used to evaluate the consistency of the computational model and that of the software tool.

Proteogenomics | Office of Cancer Clinical Proteomics Research

Cancer.gov

Proteogenomics, or the integration of proteomics with genomics and transcriptomics, is an emerging approach that promises to advance basic, translational and clinical research.  By combining genomic and proteomic information, leading scientists are gaining new insights due to a more complete and unified understanding of complex biological processes.

Ecdysone receptor agonism leading to lethal molting disruption in arthropods: Review and adverse outcome pathway development

EPA Science Inventory

Molting is a key biological process in growth, development, reproduction and survival in arthropods. Complex neuroendocrine pathways are involved in the regulation of molting and may potentially become targets of environmental endocrine disrupting compounds (EDCs). For example, s...

Chapter 5: Thermal Indices and Their Applications for Livestock Environments

USDA-ARS?s Scientific Manuscript database

Heat exchanges with the environment are a crucial process for maintaining homeothermy by humans and other animals. These exchanges involve heat production, conservation and dissipation, and are dependent on both biological and physical factors. The complexity of these exchanges has led to many attem...

The Role of Pictures in Learning Biology: Part 2, Picture-Text Processing.

ERIC Educational Resources Information Center

Reid, David

1990-01-01

The complex interactions between picture, text, and learner are examined, based on a 3-D model which describes the context of the learning task. The different strategies that children of various ability levels use in reading from illustrated texts are described. (KR)

Advanced in situ Spectroscopic Techniques And Their Applications In Environmental Biogeochemistry: Introduction To The Special Section

EPA Science Inventory

Understanding the molecular-scale complexities and interplay of chemical and biological processes of contaminants at solid, liquid, and gas interfaces is a fundamental and crucial element to enhance our understanding of anthropogenic environmental impacts. The ability to describ...

Towards Complex Abiotic Systems for Chemical and Biological Sensing

DTIC Science & Technology

2009-11-01

such as phage display, cell surface display, and Systematic Evolution of Ligands by Exponential Enrichment (SELEX). Other processes necessary to...Directed evolution by in vitro compartmentalization. Nat Methods 2006, 3, 561-570. l7Chelliserrykattil, J.; Ellington, A.D. Evolution of a T7 RNA

A comparative approach for the investigation of biological information processing: An examination of the structure and function of computer hard drives and DNA

PubMed Central

2010-01-01

Background The robust storage, updating and utilization of information are necessary for the maintenance and perpetuation of dynamic systems. These systems can exist as constructs of metal-oxide semiconductors and silicon, as in a digital computer, or in the "wetware" of organic compounds, proteins and nucleic acids that make up biological organisms. We propose that there are essential functional properties of centralized information-processing systems; for digital computers these properties reside in the computer's hard drive, and for eukaryotic cells they are manifest in the DNA and associated structures. Methods Presented herein is a descriptive framework that compares DNA and its associated proteins and sub-nuclear structure with the structure and function of the computer hard drive. We identify four essential properties of information for a centralized storage and processing system: (1) orthogonal uniqueness, (2) low level formatting, (3) high level formatting and (4) translation of stored to usable form. The corresponding aspects of the DNA complex and a computer hard drive are categorized using this classification. This is intended to demonstrate a functional equivalence between the components of the two systems, and thus the systems themselves. Results Both the DNA complex and the computer hard drive contain components that fulfill the essential properties of a centralized information storage and processing system. The functional equivalence of these components provides insight into both the design process of engineered systems and the evolved solutions addressing similar system requirements. However, there are points where the comparison breaks down, particularly when there are externally imposed information-organizing structures on the computer hard drive. A specific example of this is the imposition of the File Allocation Table (FAT) during high level formatting of the computer hard drive and the subsequent loading of an operating system (OS). Biological systems do not have an external source for a map of their stored information or for an operational instruction set; rather, they must contain an organizational template conserved within their intra-nuclear architecture that "manipulates" the laws of chemistry and physics into a highly robust instruction set. We propose that the epigenetic structure of the intra-nuclear environment and the non-coding RNA may play the roles of a Biological File Allocation Table (BFAT) and biological operating system (Bio-OS) in eukaryotic cells. Conclusions The comparison of functional and structural characteristics of the DNA complex and the computer hard drive leads to a new descriptive paradigm that identifies the DNA as a dynamic storage system of biological information. This system is embodied in an autonomous operating system that inductively follows organizational structures, data hierarchy and executable operations that are well understood in the computer science industry. Characterizing the "DNA hard drive" in this fashion can lead to insights arising from discrepancies in the descriptive framework, particularly with respect to positing the role of epigenetic processes in an information-processing context. Further expansions arising from this comparison include the view of cells as parallel computing machines and a new approach towards characterizing cellular control systems. PMID:20092652

A comparative approach for the investigation of biological information processing: an examination of the structure and function of computer hard drives and DNA.

PubMed

D'Onofrio, David J; An, Gary

2010-01-21

The robust storage, updating and utilization of information are necessary for the maintenance and perpetuation of dynamic systems. These systems can exist as constructs of metal-oxide semiconductors and silicon, as in a digital computer, or in the "wetware" of organic compounds, proteins and nucleic acids that make up biological organisms. We propose that there are essential functional properties of centralized information-processing systems; for digital computers these properties reside in the computer's hard drive, and for eukaryotic cells they are manifest in the DNA and associated structures. Presented herein is a descriptive framework that compares DNA and its associated proteins and sub-nuclear structure with the structure and function of the computer hard drive. We identify four essential properties of information for a centralized storage and processing system: (1) orthogonal uniqueness, (2) low level formatting, (3) high level formatting and (4) translation of stored to usable form. The corresponding aspects of the DNA complex and a computer hard drive are categorized using this classification. This is intended to demonstrate a functional equivalence between the components of the two systems, and thus the systems themselves. Both the DNA complex and the computer hard drive contain components that fulfill the essential properties of a centralized information storage and processing system. The functional equivalence of these components provides insight into both the design process of engineered systems and the evolved solutions addressing similar system requirements. However, there are points where the comparison breaks down, particularly when there are externally imposed information-organizing structures on the computer hard drive. A specific example of this is the imposition of the File Allocation Table (FAT) during high level formatting of the computer hard drive and the subsequent loading of an operating system (OS). Biological systems do not have an external source for a map of their stored information or for an operational instruction set; rather, they must contain an organizational template conserved within their intra-nuclear architecture that "manipulates" the laws of chemistry and physics into a highly robust instruction set. We propose that the epigenetic structure of the intra-nuclear environment and the non-coding RNA may play the roles of a Biological File Allocation Table (BFAT) and biological operating system (Bio-OS) in eukaryotic cells. The comparison of functional and structural characteristics of the DNA complex and the computer hard drive leads to a new descriptive paradigm that identifies the DNA as a dynamic storage system of biological information. This system is embodied in an autonomous operating system that inductively follows organizational structures, data hierarchy and executable operations that are well understood in the computer science industry. Characterizing the "DNA hard drive" in this fashion can lead to insights arising from discrepancies in the descriptive framework, particularly with respect to positing the role of epigenetic processes in an information-processing context. Further expansions arising from this comparison include the view of cells as parallel computing machines and a new approach towards characterizing cellular control systems.

Darwinian evolution in the light of genomics

PubMed Central

Koonin, Eugene V.

2009-01-01

Comparative genomics and systems biology offer unprecedented opportunities for testing central tenets of evolutionary biology formulated by Darwin in the Origin of Species in 1859 and expanded in the Modern Synthesis 100 years later. Evolutionary-genomic studies show that natural selection is only one of the forces that shape genome evolution and is not quantitatively dominant, whereas non-adaptive processes are much more prominent than previously suspected. Major contributions of horizontal gene transfer and diverse selfish genetic elements to genome evolution undermine the Tree of Life concept. An adequate depiction of evolution requires the more complex concept of a network or ‘forest’ of life. There is no consistent tendency of evolution towards increased genomic complexity, and when complexity increases, this appears to be a non-adaptive consequence of evolution under weak purifying selection rather than an adaptation. Several universals of genome evolution were discovered including the invariant distributions of evolutionary rates among orthologous genes from diverse genomes and of paralogous gene family sizes, and the negative correlation between gene expression level and sequence evolution rate. Simple, non-adaptive models of evolution explain some of these universals, suggesting that a new synthesis of evolutionary biology might become feasible in a not so remote future. PMID:19213802

AlignNemo: a local network alignment method to integrate homology and topology.

PubMed

Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro H; Cannataro, Mario; Guerra, Concettina

2012-01-01

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

Self-transcending meditation is good for mental health: why this should be the case.

PubMed

Hankey, Alex; Shetkar, Rashmi

2016-06-01

A simple theory of health has recently been proposed: while poor quality regulation corresponds to poor quality health so that improving regulation should improve health, optimal regulation optimizes function and optimizes health. Examining the term 'optimal regulation' in biological systems leads to a straightforward definition in terms of 'criticality' in complexity biology, a concept that seems to apply universally throughout biology. Criticality maximizes information processing and sensitivity of response to external stimuli, and for these reasons may be held to optimize regulation. In this way a definition of health has been given in terms of regulation, a scientific concept, which ties into detailed properties of complex systems, including brain cortices, and mental health. Models of experience and meditation built on complexity also point to criticality: it represents the condition making self-awareness possible, and is strengthened by meditation practices leading to the state of pure consciousness-the content-free state of mind in deep meditation. From this it follows that healthy function of the brain cortex, its sensitivity,y and consistency of response to external challenges should improve by practicing techniques leading to content-free awareness-transcending the original focus introduced during practice. Evidence for this is reviewed.

EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data.

PubMed

Linard, Benjamin; Nguyen, Ngoc Hoan; Prosdocimi, Francisco; Poch, Olivier; Thompson, Julie D

2012-01-01

Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.

Beyond tRNA cleavage: novel essential function for yeast tRNA splicing endonuclease unrelated to tRNA processing

PubMed Central

Dhungel, Nripesh; Hopper, Anita K.

2012-01-01

Pre-tRNA splicing is an essential process in all eukaryotes. In yeast and vertebrates, the enzyme catalyzing intron removal from pre-tRNA is a heterotetrameric complex (splicing endonuclease [SEN] complex). Although the SEN complex is conserved, the subcellular location where pre-tRNA splicing occurs is not. In yeast, the SEN complex is located at the cytoplasmic surface of mitochondria, whereas in vertebrates, pre-tRNA splicing is nuclear. We engineered yeast to mimic the vertebrate cell biology and demonstrate that all three steps of pre-tRNA splicing, as well as tRNA nuclear export and aminoacylation, occur efficiently when the SEN complex is nuclear. However, nuclear pre-tRNA splicing fails to complement growth defects of cells with defective mitochondrial-located splicing, suggesting that the yeast SEN complex surprisingly serves a novel and essential function in the cytoplasm that is unrelated to tRNA splicing. The novel function requires all four SEN complex subunits and the catalytic core. A subset of pre-rRNAs accumulates when the SEN complex is restricted to the nucleus, indicating that the SEN complex moonlights in rRNA processing. Thus, findings suggest that selection for the subcellular distribution of the SEN complex may reside not in its canonical, but rather in a novel, activity. PMID:22391451

[Food safety of GMOs].

PubMed

Joudrier, P

2009-01-01

In this presentation, we review the complexity of the different biological events which occur during life cell cycles. Indeed transgenesis is not an unknown event for cells. In the second part of this article, the complex and complete evaluation process destined to assure the food safety of GMOs, before they are released on the market, is describd. Some ansers to questions frequently asked about the GMOs are given. It is concludedthat GMOs are probably more safe than their conventional non-GM counterpart.

On the search for design principles in biological systems.

PubMed

Poyatos, Juan F

2012-01-01

The search for basic concepts and underlying principles was at the core of the systems approach to science and technology. This approach was somehow abandoned in mainstream biology after its initial proposal, due to the rise and success of molecular biology. This situation has changed. The accumulated knowledge of decades of molecular studies in combination with new technological advances, while further highlighting the intricacies of natural systems, is also bringing back the quest-for-principles research program. Here, I present two lessons that I derived from my own quest: the importance of studying biological information processing to identify common principles in seemingly unrelated contexts and the adequacy of using known design principles at one level of biological organization as a valuable tool to help recognizing principles at an alternative one. These and additional lessons should contribute to the ultimate goal of establishing principles able to integrate the many scales of biological complexity.

Biological implications of lab-on-a-chip devices fabricated using multi-jet modelling and stereolithography processes

NASA Astrophysics Data System (ADS)

Zhu, Feng; Macdonald, Niall; Skommer, Joanna; Wlodkowic, Donald

2015-06-01

Current microfabrication methods are often restricted to two-dimensional (2D) or two and a half dimensional (2.5D) structures. Those fabrication issues can be potentially addressed by emerging additive manufacturing technologies. Despite rapid growth of additive manufacturing technologies in tissue engineering, microfluidics has seen relatively little developments with regards to adopting 3D printing for rapid fabrication of complex chip-based devices. This has been due to two major factors: lack of sufficient resolution of current rapid-prototyping methods (usually >100 μm ) and optical transparency of polymers to allow in vitro imaging of specimens. We postulate that adopting innovative fabrication processes can provide effective solutions for prototyping and manufacturing of chip-based devices with high-aspect ratios (i.e. above ration of 20:1). This work provides a comprehensive investigation of commercially available additive manufacturing technologies as an alternative for rapid prototyping of complex monolithic Lab-on-a-Chip devices for biological applications. We explored both multi-jet modelling (MJM) and several stereolithography (SLA) processes with five different 3D printing resins. Compared with other rapid prototyping technologies such as PDMS soft lithography and infrared laser micromachining, we demonstrated that selected SLA technologies had superior resolution and feature quality. We also for the first time optimised the post-processing protocols and demonstrated polymer features under scanning electronic microscope (SEM). Finally we demonstrate that selected SLA polymers have optical properties enabling high-resolution biological imaging. A caution should be, however, exercised as more work is needed to develop fully bio-compatible and non-toxic polymer chemistries.

Tandem catalysis: a new approach to polymers.

PubMed

Robert, Carine; Thomas, Christophe M

2013-12-21

The creation of polymers by tandem catalysis represents an exciting frontier in materials science. Tandem catalysis is one of the strategies used by Nature for building macromolecules. Living organisms generally synthesize macromolecules by in vivo enzyme-catalyzed chain growth polymerization reactions using activated monomers that have been formed within cells during complex metabolic processes. However, these biological processes rely on highly complex biocatalysts, thus limiting their industrial applications. In order to obtain polymers by tandem catalysis, homogeneous and enzyme catalysts have played a leading role in the last two decades. In the following feature article, we will describe selected published efforts to achieve these research goals.

Recent advances in the chemistry of Rh carbenoids: multicomponent reactions of diazocarbonyl compounds

NASA Astrophysics Data System (ADS)

Medvedev, J. J.; Nikolaev, V. A.

2015-07-01

Multicomponent reactions of diazo compounds catalyzed by RhII complexes become a powerful tool for organic synthesis. They enable three- or four-step processes to be carried out as one-pot procedures (actually as one step) with high stereoselectivity to give complex organic molecules, including biologically active compounds. This review addresses recent results in the chemistry of Rh-catalyzed multicomponent reactions of diazocarbonyl compounds with the intermediate formation of N-, O- and C=O-ylides. The diastereo- and enantioselectivity of these reactions and the possibility of using various co-catalysts to increase the efficiency of the processes under consideration are discussed. The bibliography includes 120 references.

Tree physiology research in a changing world.

PubMed

Kaufmann, Merrill R.; Linder, Sune

1996-01-01

Changes in issues and advances in methodology have contributed to substantial progress in tree physiology research during the last several decades. Current research focuses on process interactions in complex systems and the integration of processes across multiple spatial and temporal scales. An increasingly important challenge for future research is assuring sustainability of production systems and forested ecosystems in the face of increased demands for natural resources and human disturbance of forests. Meeting this challenge requires significant shifts in research approach, including the study of limitations of productivity that may accompany achievement of system sustainability, and a focus on the biological capabilities of complex land bases altered by human activity.

Equivalent formulations of “the equation of life”

NASA Astrophysics Data System (ADS)

Ao, Ping

2014-07-01

Motivated by progress in theoretical biology a recent proposal on a general and quantitative dynamical framework for nonequilibrium processes and dynamics of complex systems is briefly reviewed. It is nothing but the evolutionary process discovered by Charles Darwin and Alfred Wallace. Such general and structured dynamics may be tentatively named “the equation of life”. Three equivalent formulations are discussed, and it is also pointed out that such a quantitative dynamical framework leads naturally to the powerful Boltzmann-Gibbs distribution and the second law in physics. In this way, the equation of life provides a logically consistent foundation for thermodynamics. This view clarifies a particular outstanding problem and further suggests a unifying principle for physics and biology.

Risk Management in Biologics Technology Transfer.

PubMed

Toso, Robert; Tsang, Jonathan; Xie, Jasmina; Hohwald, Stephen; Bain, David; Willison-Parry, Derek

Technology transfer of biological products is a complex process that is important for product commercialization. To achieve a successful technology transfer, the risks that arise from changes throughout the project must be managed. Iterative risk analysis and mitigation tools can be used to both evaluate and reduce risk. The technology transfer stage gate model is used as an example tool to help manage risks derived from both designed process change and unplanned changes that arise due to unforeseen circumstances. The strategy of risk assessment for a change can be tailored to the type of change. In addition, a cross-functional team and centralized documentation helps maximize risk management efficiency to achieve a successful technology transfer. © PDA, Inc. 2016.

Interdependency of formation and localisation of the Min complex controls symmetric plastid division.

PubMed

Maple, Jodi; Møller, Simon G

2007-10-01

Plastid division represents a fundamental biological process essential for plant development; however, the molecular basis of symmetric plastid division is unclear. AtMinE1 plays a pivotal role in selection of the plastid division site in concert with AtMinD1. AtMinE1 localises to discrete foci in chloroplasts and interacts with AtMinD1, which shows a similar localisation pattern. Here, we investigate the importance of Min protein complex formation during the chloroplast division process. Dissection of the assembly of the Min protein complex and determination of the interdependency of complex assembly and localisation in planta allow us to present a model of the molecular basis of selection of the division site in plastids. Moreover, functional analysis of AtMinE1 in bacteria demonstrates the level of functional conservation and divergence of the plastidic MinE proteins.

Modeling the Effects of Light and Sucrose on In Vitro Propagated Plants: A Multiscale System Analysis Using Artificial Intelligence Technology

PubMed Central

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.

2014-01-01

Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m−2 s−1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829

Modeling the effects of light and sucrose on in vitro propagated plants: a multiscale system analysis using artificial intelligence technology.

PubMed

Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P

2014-01-01

Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122-130 µmol m(-2) s(-1). Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work.

Model annotation for synthetic biology: automating model to nucleotide sequence conversion

PubMed Central

Misirli, Goksel; Hallinan, Jennifer S.; Yu, Tommy; Lawson, James R.; Wimalaratne, Sarala M.; Cooling, Michael T.; Wipat, Anil

2011-01-01

Motivation: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biology projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behaviour. The larger model then acts as a blueprint for physical implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their physical manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. Results: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biology Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. Availability: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biology/downloads.html. Contact: anil.wipat@ncl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21296753

Structural Analysis of N- and O-glycans Using ZIC-HILIC/Dialysis Coupled to NMR Detection

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

Qu, Yi; Feng, Ju; Deng, Shuang

2014-11-19

Protein glycosylation, an important and complex post-translational modification (PTM), is involved in various biological processes including the receptor-ligand and cell-cell interaction, and plays a crucial role in many biological functions. However, little is known about the glycan structures of important biological complex samples, and the conventional glycan enrichment strategy (i.e., size-exclusion column [SEC] separation,) prior to nuclear magnetic resonance (NMR) detection is time-consuming and tedious. In this study, we employed SEC, Zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC), and ZIC-HILIC coupled with dialysis strategies to enrich the glycopeptides from the pronase E digests of RNase B, followed by NMR analysis ofmore » the glycoconjugate. Our results suggest that the ZIC-HILIC enrichment coupled with dialysis is the most efficient, which was thus applied to the analysis of biological complex sample, the pronase E digest of the secreted proteins from the fungi Aspergillus niger. The NMR spectra revealed that the secreted proteins from A. niger contain both N-linked glycans with a high-mannose core and O-linked glycans bearing mannose and glucose with 1->3 and 1->6 linkages. In all, our study provides compelling evidence that ZIC-HILIC separation coupled to dialysis is superior to the commonly used SEC separation to prepare glycopeptides for the downstream NMR analysis, which could greatly facilitate the future NMR-based glycoproteomics research.« less

Bipartite graphs in systems biology and medicine: a survey of methods and applications.

PubMed

Pavlopoulos, Georgios A; Kontou, Panagiota I; Pavlopoulou, Athanasia; Bouyioukos, Costas; Markou, Evripides; Bagos, Pantelis G

2018-04-01

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

An algorithm for automated layout of process description maps drawn in SBGN.

PubMed

Genc, Begum; Dogrusoz, Ugur

2016-01-01

Evolving technology has increased the focus on genomics. The combination of today's advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

An algorithm for automated layout of process description maps drawn in SBGN

PubMed Central

Genc, Begum; Dogrusoz, Ugur

2016-01-01

Motivation: Evolving technology has increased the focus on genomics. The combination of today’s advanced techniques with decades of molecular biology research has yielded huge amounts of pathway data. A standard, named the Systems Biology Graphical Notation (SBGN), was recently introduced to allow scientists to represent biological pathways in an unambiguous, easy-to-understand and efficient manner. Although there are a number of automated layout algorithms for various types of biological networks, currently none specialize on process description (PD) maps as defined by SBGN. Results: We propose a new automated layout algorithm for PD maps drawn in SBGN. Our algorithm is based on a force-directed automated layout algorithm called Compound Spring Embedder (CoSE). On top of the existing force scheme, additional heuristics employing new types of forces and movement rules are defined to address SBGN-specific rules. Our algorithm is the only automatic layout algorithm that properly addresses all SBGN rules for drawing PD maps, including placement of substrates and products of process nodes on opposite sides, compact tiling of members of molecular complexes and extensively making use of nested structures (compound nodes) to properly draw cellular locations and molecular complex structures. As demonstrated experimentally, the algorithm results in significant improvements over use of a generic layout algorithm such as CoSE in addressing SBGN rules on top of commonly accepted graph drawing criteria. Availability and implementation: An implementation of our algorithm in Java is available within ChiLay library (https://github.com/iVis-at-Bilkent/chilay). Contact: ugur@cs.bilkent.edu.tr or dogrusoz@cbio.mskcc.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26363029

A Learning Framework for Winner-Take-All Networks with Stochastic Synapses.

PubMed

Mostafa, Hesham; Cauwenberghs, Gert

2018-06-01

Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

Genome-Wide Detection and Analysis of Multifunctional Genes

PubMed Central

Pritykin, Yuri; Ghersi, Dario; Singh, Mona

2015-01-01

Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms—H. sapiens, D. melanogaster, and S. cerevisiae—and show that, as compared to other annotated genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be more central in protein interaction networks, tend to be more evolutionarily conserved, and are more likely to be essential. We also find that multifunctional genes are significantly more likely to be involved in human disorders. These same features also hold when multifunctionality is defined with respect to molecular functions instead of biological processes. Our analysis uncovers key features about multifunctional genes, and is a step towards a better genome-wide understanding of gene multifunctionality. PMID:26436655

Beyond disease susceptibility-Leveraging genome-wide association studies for new insights into complex disease biology.

PubMed

Lee, J C

2017-12-01

Genetic studies in complex diseases have been highly successful, but have also been largely one-dimensional: predominantly focusing on the genetic contribution to disease susceptibility. While this is undoubtedly important-indeed it is a pre-requisite for understanding the mechanisms underlying disease development-there are many other important aspects of disease biology that have received comparatively little attention. In this review, I will discuss how existing genetic data can be leveraged to provide new insights into other aspects of disease biology, why such insights could change the way we think about complex disease, and how this could provide opportunities for better therapies and/or facilitate personalised medicine. To do this, I will use the example of Crohn's disease-a chronic form of inflammatory bowel disease that has been one of the main success stories in complex disease genetics. Indeed, thanks to genetic studies, we now have a much more detailed understanding of the processes involved in Crohn's disease development, but still know relatively little about what determines the subsequent disease course (prognosis) and why this differs so considerably between individuals. I will discuss how we came to realise that genetic variation plays an important role in determining disease prognosis and how this has changed the way we think about Crohn's disease genetics. This will illustrate how phenotypic data can be used to leverage new insights from genetic data and will provide a broadly applicable framework that could yield new insights into the biology of multiple diseases. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Octamer-binding protein 4 affects the cell biology and phenotypic transition of lung cancer cells involving β-catenin/E-cadherin complex degradation.

PubMed

Chen, Zhong-Shu; Ling, Dong-Jin; Zhang, Yang-De; Feng, Jian-Xiong; Zhang, Xue-Yu; Shi, Tian-Sheng

2015-03-01

Clinical studies have reported evidence for the involvement of octamer‑binding protein 4 (Oct4) in the tumorigenicity and progression of lung cancer; however, the role of Oct4 in lung cancer cell biology in vitro and its mechanism of action remain to be elucidated. Mortality among lung cancer patients is more frequently due to metastasis rather than their primary tumors. Epithelial‑mesenchymal transition (EMT) is a prominent biological event for the induction of epithelial cancer metastasis. The aim of the present study was to investigate whether Oct4 had the capacity to induce lung cancer cell metastasis via the promoting the EMT in vitro. Moreover, the effect of Oct4 on the β‑catenin/E‑cadherin complex, associated with EMT, was examined using immunofluorescence and immunoprecipitation assays as well as western blot analysis. The results demonstrated that Oct4 enhanced cell invasion and adhesion accompanied by the downregulation of epithelial marker cytokeratin, and upregulation of the mesenchymal markers vimentin and N‑cadherin. Furthermore, Oct4 induced EMT of lung cancer cells by promoting β‑catenin/E‑cadherin complex degradation and regulating nuclear localization of β‑catenin. In conclusion, the present study indicated that Oct4 affected the cell biology of lung cancer cells in vitro through promoting lung cancer cell metastasis via EMT; in addition, the results suggested that the association and degradation of the β‑catenin/E‑cadherin complex was regulated by Oct4 during the process of EMT.

Programmable chemical controllers made from DNA.

PubMed

Chen, Yuan-Jyue; Dalchau, Neil; Srinivas, Niranjan; Phillips, Andrew; Cardelli, Luca; Soloveichik, David; Seelig, Georg

2013-10-01

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

Using the Tools and Resources of the RCSB Protein Data Bank.

PubMed

Costanzo, Luigi Di; Ghosh, Sutapa; Zardecki, Christine; Burley, Stephen K

2016-09-07

The Protein Data Bank (PDB) archive is the worldwide repository of experimentally determined three-dimensional structures of large biological molecules found in all three kingdoms of life. Atomic-level structures of these proteins, nucleic acids, and complex assemblies thereof are central to research and education in molecular, cellular, and organismal biology, biochemistry, biophysics, materials science, bioengineering, ecology, and medicine. Several types of information are associated with each PDB archival entry, including atomic coordinates, primary experimental data, polymer sequence(s), and summary metadata. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) serves as the U.S. data center for the PDB, distributing archival data and supporting both simple and complex queries that return results. These data can be freely downloaded, analyzed, and visualized using RCSB PDB tools and resources to gain a deeper understanding of fundamental biological processes, molecular evolution, human health and disease, and drug discovery. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Network biology discovers pathogen contact points in host protein-protein interactomes.

PubMed

Ahmed, Hadia; Howton, T C; Sun, Yali; Weinberger, Natascha; Belkhadir, Youssef; Mukhtar, M Shahid

2018-06-13

In all organisms, major biological processes are controlled by complex protein-protein interactions networks (interactomes), yet their structural complexity presents major analytical challenges. Here, we integrate a compendium of over 4300 phenotypes with Arabidopsis interactome (AI-1 MAIN ). We show that nodes with high connectivity and betweenness are enriched and depleted in conditional and essential phenotypes, respectively. Such nodes are located in the innermost layers of AI-1 MAIN and are preferential targets of pathogen effectors. We extend these network-centric analyses to Cell Surface Interactome (CSI LRR ) and predict its 35 most influential nodes. To determine their biological relevance, we show that these proteins physically interact with pathogen effectors and modulate plant immunity. Overall, our findings contrast with centrality-lethality rule, discover fast information spreading nodes, and highlight the structural properties of pathogen targets in two different interactomes. Finally, this theoretical framework could possibly be applicable to other inter-species interactomes to reveal pathogen contact points.

Programmable chemical controllers made from DNA

NASA Astrophysics Data System (ADS)

Chen, Yuan-Jyue; Dalchau, Neil; Srinivas, Niranjan; Phillips, Andrew; Cardelli, Luca; Soloveichik, David; Seelig, Georg

2013-10-01

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language' and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents.

New strategy for protein interactions and application to structure-based drug design

NASA Astrophysics Data System (ADS)

Zou, Xiaoqin

One of the greatest challenges in computational biophysics is to predict interactions between biological molecules, which play critical roles in biological processes and rational design of therapeutic drugs. Biomolecular interactions involve delicate interplay between multiple interactions, including electrostatic interactions, van der Waals interactions, solvent effect, and conformational entropic effect. Accurate determination of these complex and subtle interactions is challenging. Moreover, a biological molecule such as a protein usually consists of thousands of atoms, and thus occupies a huge conformational space. The large degrees of freedom pose further challenges for accurate prediction of biomolecular interactions. Here, I will present our development of physics-based theory and computational modeling on protein interactions with other molecules. The major strategy is to extract microscopic energetics from the information embedded in the experimentally-determined structures of protein complexes. I will also present applications of the methods to structure-based therapeutic design. Supported by NSF CAREER Award DBI-0953839, NIH R01GM109980, and the American Heart Association (Midwest Affiliate) [13GRNT16990076].

Programmable chemical controllers made from DNA

PubMed Central

Chen, Yuan-Jyue; Dalchau, Neil; Srinivas, Niranjan; Phillips, Andrew; Cardelli, Luca; Soloveichik, David; Seelig, Georg

2014-01-01

Biological organisms use complex molecular networks to navigate their environment and regulate their internal state. The development of synthetic systems with similar capabilities could lead to applications such as smart therapeutics or fabrication methods based on self-organization. To achieve this, molecular control circuits need to be engineered to perform integrated sensing, computation and actuation. Here we report a DNA-based technology for implementing the computational core of such controllers. We use the formalism of chemical reaction networks as a 'programming language', and our DNA architecture can, in principle, implement any behaviour that can be mathematically expressed as such. Unlike logic circuits, our formulation naturally allows complex signal processing of intrinsically analogue biological and chemical inputs. Controller components can be derived from biologically synthesized (plasmid) DNA, which reduces errors associated with chemically synthesized DNA. We implement several building-block reaction types and then combine them into a network that realizes, at the molecular level, an algorithm used in distributed control systems for achieving consensus between multiple agents. PMID:24077029

KRAS Mouse Models

PubMed Central

O’Hagan, Rónán C.; Heyer, Joerg

2011-01-01

KRAS is a potent oncogene and is mutated in about 30% of all human cancers. However, the biological context of KRAS-dependent oncogenesis is poorly understood. Genetically engineered mouse models of cancer provide invaluable tools to study the oncogenic process, and insights from KRAS-driven models have significantly increased our understanding of the genetic, cellular, and tissue contexts in which KRAS is competent for oncogenesis. Moreover, variation among tumors arising in mouse models can provide insight into the mechanisms underlying response or resistance to therapy in KRAS-dependent cancers. Hence, it is essential that models of KRAS-driven cancers accurately reflect the genetics of human tumors and recapitulate the complex tumor-stromal intercommunication that is manifest in human cancers. Here, we highlight the progress made in modeling KRAS-dependent cancers and the impact that these models have had on our understanding of cancer biology. In particular, the development of models that recapitulate the complex biology of human cancers enables translational insights into mechanisms of therapeutic intervention in KRAS-dependent cancers. PMID:21779503

Systems Biology and Biomechanical Model of Heart Failure

PubMed Central

Louridas, George E; Lourida, Katerina G

2012-01-01

Heart failure is seen as a complex disease caused by a combination of a mechanical disorder, cardiac remodeling and neurohormonal activation. To define heart failure the systems biology approach integrates genes and molecules, interprets the relationship of the molecular networks with modular functional units, and explains the interaction between mechanical dysfunction and cardiac remodeling. The biomechanical model of heart failure explains satisfactorily the progression of myocardial dysfunction and the development of clinical phenotypes. The earliest mechanical changes and stresses applied in myocardial cells and/or myocardial loss or dysfunction activate left ventricular cavity remodeling and other neurohormonal regulatory mechanisms such as early release of natriuretic peptides followed by SAS and RAAS mobilization. Eventually the neurohormonal activation and the left ventricular remodeling process are leading to clinical deterioration of heart failure towards a multi-organic damage. It is hypothesized that approaching heart failure with the methodology of systems biology we promote the elucidation of its complex pathophysiology and most probably we can invent new therapeutic strategies. PMID:22935019

At a glance: cellular biology for engineers.

PubMed

Khoshmanesh, K; Kouzani, A Z; Nahavandi, S; Baratchi, S; Kanwar, J R

2008-10-01

Engineering contributions have played an important role in the rise and evolution of cellular biology. Engineering technologies have helped biologists to explore the living organisms at cellular and molecular levels, and have created new opportunities to tackle the unsolved biological problems. There is now a growing demand to further expand the role of engineering in cellular biology research. For an engineer to play an effective role in cellular biology, the first essential step is to understand the cells and their components. However, the stumbling block of this step is to comprehend the information given in the cellular biology literature because it best suits the readers with a biological background. This paper aims to overcome this bottleneck by describing the human cell components as micro-plants that form cells as micro-bio-factories. This concept can accelerate the engineers' comprehension of the subject. In this paper, first the structure and function of different cell components are described. In addition, the engineering attempts to mimic various cell components through numerical modelling or physical implementation are highlighted. Next, the interaction of different cell components that facilitate complicated chemical processes, such as energy generation and protein synthesis, are described. These complex interactions are translated into simple flow diagrams, generally used by engineers to represent multi-component processes.

Biologically inspired collision avoidance system for unmanned vehicles

NASA Astrophysics Data System (ADS)

Ortiz, Fernando E.; Graham, Brett; Spagnoli, Kyle; Kelmelis, Eric J.

2009-05-01

In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

Exploring (novel) gene expression during retinoid-induced maturation and cell death of acute promyelocytic leukemia.

PubMed

Benoit, G R; Tong, J H; Balajthy, Z; Lanotte, M

2001-01-01

During recent years, reports have shown that biological responses of acute promyelocytic leukemia (APL) cells to retinoids are more complex than initially envisioned. PML-RARalpha chimeric protein disturbs various biological processes such as cell proliferation, differentiation, and apoptosis. The distinct biological programs that regulate these processes stem from specific transcriptional activation of distinct (but overlapping) sets of genes. These programs are sometimes mutually exclusive and depend on whether the signals are delivered by RAR or RXR agonists. Furthermore, evidence that retinoid nuclear signaling by retinoid, on its own, is not enough to trigger these cellular responses is rapidly accumulating. Indeed, work with NB4 cells show that the fate of APL cells treated by retinoid depends on complex signaling cross-talk. Elucidation of the sequence of events and cascades of transcriptional regulation necessary for APL cell maturation will be an additional tool with which to further improve therapy by retinoids. In this task, the classical techniques used to analyze gene expression have proved time consuming, and their yield has been limited. Global analyses of the APL cell transcriptome are needed. We review the technical approaches currently available (differential display, complementary DNA microarrays), to identify novel genes involved in the determination of cell fate.

Indirect electroreduction as pretreatment to enhance biodegradability of metronidazole.

PubMed

Saidi, I; Soutrel, I; Floner, D; Fourcade, F; Bellakhal, N; Amrane, A; Geneste, F

2014-08-15

The removal of metronidazole, a biorecalcitrant antibiotic, by coupling an electrochemical reduction with a biological treatment was examined. Electroreduction was performed in a home-made flow cell at -1.2V/SCE on graphite felt. After only one pass through the cell, analysis of the electrolyzed solution showed a total degradation of metronidazole. The biodegradability estimated from the BOD5/COD ratio increased from 0.07 to 0.2, namely below the value usually considered as the limit of biodegradability (0.4). In order to improve these results, indirect electrolysis of metronidazole was performed with a titanium complex known to reduce selectively nitro compounds into amine. The catalytic activity of the titanium complex towards electroreduction of metronidazole was shown by cyclic voltammetry analyses. Indirect electrolysis led to an improvement of the biodegradability from 0.07 to 0.42. To confirm the interest of indirect electroreduction to improve the electrochemical pretreatment, biological treatment was then carried out on activated sludge after direct and indirect electrolyses; different parameters were followed during the culture such as pH, TOC and metronidazole concentration. Both electrochemical processes led to a more efficient biodegradation of metronidazole compared with the single biological treatment, leading to an overall mineralization yield for the coupling process of 85%. Copyright © 2014 Elsevier B.V. All rights reserved.

System and process for pulsed multiple reaction monitoring

DOEpatents

Belov, Mikhail E

2013-05-17

A new pulsed multiple reaction monitoring process and system are disclosed that uses a pulsed ion injection mode for use in conjunction with triple-quadrupole instruments. The pulsed injection mode approach reduces background ion noise at the detector, increases amplitude of the ion signal, and includes a unity duty cycle that provides a significant sensitivity increase for reliable quantitation of proteins/peptides present at attomole levels in highly complex biological mixtures.

Ligand diffusion in proteins via enhanced sampling in molecular dynamics.

PubMed

Rydzewski, J; Nowak, W

2017-12-01

Computational simulations in biophysics describe the dynamics and functions of biological macromolecules at the atomic level. Among motions particularly important for life are the transport processes in heterogeneous media. The process of ligand diffusion inside proteins is an example of a complex rare event that can be modeled using molecular dynamics simulations. The study of physical interactions between a ligand and its biological target is of paramount importance for the design of novel drugs and enzymes. Unfortunately, the process of ligand diffusion is difficult to study experimentally. The need for identifying the ligand egress pathways and understanding how ligands migrate through protein tunnels has spurred the development of several methodological approaches to this problem. The complex topology of protein channels and the transient nature of the ligand passage pose difficulties in the modeling of the ligand entry/escape pathways by canonical molecular dynamics simulations. In this review, we report a methodology involving a reconstruction of the ligand diffusion reaction coordinates and the free-energy profiles along these reaction coordinates using enhanced sampling of conformational space. We illustrate the above methods on several ligand-protein systems, including cytochromes and G-protein-coupled receptors. The methods are general and may be adopted to other transport processes in living matter. Copyright © 2017 Elsevier B.V. All rights reserved.

Chiral Polychlorinated Biphenyl Transport, Metabolism and Distribution - A Review

PubMed Central

Lehmler, Hans-Joachim; Harrad, Stuart J.; Hühnerfuss, Heinrich; Kania-Korwel, Izabela; Lee, Cindy M.; Lu, Zhe; Wong, Charles S.

2009-01-01

Chirality can be exploited to gain insight into enantioselective fate processes that may otherwise remain undetected because only biological, but not physical and chemical transport and transformation processes in an achiral environment will change enantiomer compositions. This review provides an in-depth overview of the application of chirality to the study of chiral polychlorinated biphenyls (PCBs), an important group of legacy pollutants. Like other chiral compounds, individual PCB enantiomers may interact enantioselectively (or enantiospecifically) with chiral macromolecules, such as cytochrome P-450 enzymes or ryanodine receptors, leading to differences in their toxicological effects and the enantioselective formation of chiral biotransformation products. Species and congener-specific enantiomer enrichment has been demonstrated in environmental compartments, wildlife and mammals, including humans, typically due to a complex combination of biotransformation processes and uptake via the diet by passive diffusion. Changes in the enantiomer composition of chiral PCBs in the environment have been used to understand complex aerobic and anaerobic microbial transformation pathways, to delineate and quantify PCB sources and transport in the environment, to gain insight into the biotransformation of PCBs in aquatic food webs, and to investigate the enantioselective disposition of PCBs and their methylsulfonyl PCBs metabolites in rodents. Overall, changes in chiral signatures are powerful, but currently underutilized tools for studies of environmental and biological processes of PCBs. PMID:20384371

Engineering of routes to heparin and related polysaccharides.

PubMed

Bhaskar, Ujjwal; Sterner, Eric; Hickey, Anne Marie; Onishi, Akihiro; Zhang, Fuming; Dordick, Jonathan S; Linhardt, Robert J

2012-01-01

Anticoagulant heparin has been shown to possess important biological functions that vary according to its fine structure. Variability within heparin's structure occurs owing to its biosynthesis and animal tissue-based recovery and adds another dimension to its complex polymeric structure. The structural variations in chain length and sulfation patterns mediate its interaction with many heparin-binding proteins, thereby eliciting complex biological responses. The advent of novel chemical and enzymatic approaches for polysaccharide synthesis coupled with high throughput combinatorial approaches for drug discovery have facilitated an increased effort to understand heparin's structure-activity relationships. An improved understanding would offer potential for new therapeutic development through the engineering of polysaccharides. Such a bioengineering approach requires the amalgamation of several different disciplines, including carbohydrate synthesis, applied enzymology, metabolic engineering, and process biochemistry.

Understanding the nanoparticle-protein corona complexes using computational and experimental methods.

PubMed

Kharazian, B; Hadipour, N L; Ejtehadi, M R

2016-06-01

Nanoparticles (NP) have capability to adsorb proteins from biological fluids and form protein layer, which is called protein corona. As the cell sees corona coated NPs, the protein corona can dictate biological response to NPs. The composition of protein corona is varied by physicochemical properties of NPs including size, shape, surface chemistry. Processing of protein adsorption is dynamic phenomena; to that end, a protein may desorb or leave a surface vacancy that is rapidly filled by another protein and cause changes in the corona composition mainly by the Vroman effect. In this review, we discuss the interaction between NP and proteins and the available techniques for identification of NP-bound proteins. Also we review current developed computational methods for understanding the NP-protein complex interactions. Copyright © 2016. Published by Elsevier Ltd.

The Physics of Life and Quantum Complex Matter: A Case of Cross-Fertilization

PubMed Central

Poccia, Nicola; Bianconi, Antonio

2011-01-01

Progress in the science of complexity, from the Big Bang to the coming of humankind, from chemistry and biology to geosciences and medicine, and from materials engineering to energy sciences, is leading to a shift of paradigm in the physical sciences. The focus is on the understanding of the non-equilibrium process in fine tuned systems. Quantum complex materials such as high temperature superconductors and living matter are both non-equilibrium and fine tuned systems. These topics have been subbjects of scientific discussion in the Rome Symposium on the “Quantum Physics of Living Matter”. PMID:26791661

A multi-criteria decisionmaking approach to management indicator species selection for the Monongahela National Forest, West Virginia.

Treesearch

Kurtis R. Moseley; W.Mark Ford; John W. Edwards; Michael P. Strager

2010-01-01

The management indicator species concept is useful for land managers charged with monitoring and conserving complex biological diversity over large landscapes with limited available resources. We used the analytical hierarchy process (AHP) to determine the best management indicator species (MIS) for three...

NCI-CPTAC DREAM Proteogenomics Challenge (Registration Now Open) | Office of Cancer Clinical Proteomics Research

Cancer.gov

Proteogenomics, integration of proteomics, genomics, and transcriptomics, is an emerging approach that promises to advance basic, translational and clinical research.  By combining genomic and proteomic information, leading scientists are gaining new insights due to a more complete and unified understanding of complex biological processes.

Visualizing estrogen receptor-a-expressing neurons using a new ERa-ZsGreen reporter mouse line

USDA-ARS?s Scientific Manuscript database

A variety of biological functions of estrogens, including regulation of energy metabolism, are mediated by neurons expressingestrogen receptor-a (ERa) in the brain. However, complex intracellular processes in these ERa-expressing neurons are difficult to unravel, due to the lack of strategy to visua...

Molecular Thermodynamics for Cell Biology as Taught with Boxes

ERIC Educational Resources Information Center

Mayorga, Luis S.; Lopez, Maria Jose; Becker, Wayne M.

2012-01-01

Thermodynamic principles are basic to an understanding of the complex fluxes of energy and information required to keep cells alive. These microscopic machines are nonequilibrium systems at the micron scale that are maintained in pseudo-steady-state conditions by very sophisticated processes. Therefore, several nonstandard concepts need to be…

Introducing the Hero Complex and the Mythic Iconic Pathway of Problem Gambling

ERIC Educational Resources Information Center

Nixon, Gary; Solowoniuk, Jason

2009-01-01

Early research into the motivations behind problem gambling reflected separate paradigms of thought splitting our understanding of the gambler into divergent categories. However, over the past 25 years, problem gambling is now best understood to arise from biological, environmental, social, and psychological processes, and is now encapsulated…

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

Holm, Christian; Gompper, Gerhard; Dill, Ken A.

This special issue highlights new developments in theory and coarse-graining in biological and synthetic macromolecules and membranes. Such approaches give unique insights into the principles and design of the structures, dynamics, and assembly processes of these complex fluids and soft materials, where the length and time scales are often prohibitively long for fully atomistic modeling.

A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening

EPA Science Inventory

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive i...

Tethered bilayer lipid membranes (tBLMs): interest and applications for biological membrane investigations.

PubMed

Rebaud, Samuel; Maniti, Ofelia; Girard-Egrot, Agnès P

2014-12-01

Biological membranes play a central role in the biology of the cell. They are not only the hydrophobic barrier allowing separation between two water soluble compartments but also a supra-molecular entity that has vital structural functions. Notably, they are involved in many exchange processes between the outside and inside cellular spaces. Accounting for the complexity of cell membranes, reliable models are needed to acquire current knowledge of the molecular processes occurring in membranes. To simplify the investigation of lipid/protein interactions, the use of biomimetic membranes is an approach that allows manipulation of the lipid composition of specific domains and/or the protein composition, and the evaluation of the reciprocal effects. Since the middle of the 80's, lipid bilayer membranes have been constantly developed as models of biological membranes with the ultimate goal to reincorporate membrane proteins for their functional investigation. In this review, after a brief description of the planar lipid bilayers as biomimetic membrane models, we will focus on the construction of the tethered Bilayer Lipid Membranes, the most promising model for efficient membrane protein reconstitution and investigation of molecular processes occurring in cell membranes. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Use of micro computed-tomography and 3D printing for reverse engineering of mouse embryo nasal capsule

NASA Astrophysics Data System (ADS)

Tesařová, M.; Zikmund, T.; Kaucká, M.; Adameyko, I.; Jaroš, J.; Paloušek, D.; Škaroupka, D.; Kaiser, J.

2016-03-01

Imaging of increasingly complex cartilage in vertebrate embryos is one of the key tasks of developmental biology. This is especially important to study shape-organizing processes during initial skeletal formation and growth. Advanced imaging techniques that are reflecting biological needs give a powerful impulse to push the boundaries of biological visualization. Recently, techniques for contrasting tissues and organs have improved considerably, extending traditional 2D imaging approaches to 3D . X-ray micro computed tomography (μCT), which allows 3D imaging of biological objects including their internal structures with a resolution in the micrometer range, in combination with contrasting techniques seems to be the most suitable approach for non-destructive imaging of embryonic developing cartilage. Despite there are many software-based ways for visualization of 3D data sets, having a real solid model of the studied object might give novel opportunities to fully understand the shape-organizing processes in the developing body. In this feasibility study we demonstrated the full procedure of creating a real 3D object of mouse embryo nasal capsule, i.e. the staining, the μCT scanning combined by the advanced data processing and the 3D printing.

Flexible automated approach for quantitative liquid handling of complex biological samples.

PubMed

Palandra, Joe; Weller, David; Hudson, Gary; Li, Jeff; Osgood, Sarah; Hudson, Emily; Zhong, Min; Buchholz, Lisa; Cohen, Lucinda H

2007-11-01

A fully automated protein precipitation technique for biological sample preparation has been developed for the quantitation of drugs in various biological matrixes. All liquid handling during sample preparation was automated using a Hamilton MicroLab Star Robotic workstation, which included the preparation of standards and controls from a Watson laboratory information management system generated work list, shaking of 96-well plates, and vacuum application. Processing time is less than 30 s per sample or approximately 45 min per 96-well plate, which is then immediately ready for injection onto an LC-MS/MS system. An overview of the process workflow is discussed, including the software development. Validation data are also provided, including specific liquid class data as well as comparative data of automated vs manual preparation using both quality controls and actual sample data. The efficiencies gained from this automated approach are described.

Engineering artificial cells by combining HeLa-based cell-free expression and ultra-thin double emulsion template

PubMed Central

Ho, Kwun Yin; Murray, Victoria L.; Liu, Allen P.

2015-01-01

Generation of artificial cells provides the bridge needed to cover the gap between studying the complexity of biological processes in whole cells and studying these same processes in an in vitro reconstituted system. Artificial cells are defined as the encapsulation of biologically active material in a biological or synthetic membrane. Here, we describe a robust and general method to produce artificial cells for the purpose of mimicking one or more behaviors of a cell. A microfluidic double emulsion system is used to encapsulate a mammalian cell free expression system that is able to express membrane proteins into the bilayer or soluble proteins inside the vesicles. The development of a robust platform that allows the assembly of artificial cells is valuable in understanding subcellular functions and emergent behaviors in a more cell-like environment as well as for creating novel signaling pathways to achieve specific cellular behaviors. PMID:25997354

Antiviral Innate Immunity through the lens of Systems Biology

PubMed Central

Tripathi, Shashank; García-Sastre, Adolfo

2015-01-01

Cellular innate immunity poses the first hurdle against invading viruses in their attempt to establish infection. This antiviral response is manifested with the detection of viral components by the host cell, followed by transduction of antiviral signals, transcription and translation of antiviral effectors and leads to the establishment of an antiviral state. These events occur in a rather branched and interconnected sequence than a linear path. Traditionally, these processes were studied in the context of a single virus and a host component. However, with the advent of rapid and affordable OMICS technologies it has become feasible to address such questions on a global scale. In the discipline of Systems Biology’, extensive omics datasets are assimilated using computational tools and mathematical models to acquire deeper understanding of complex biological processes. In this review we have catalogued and discussed the application of Systems Biology approaches in dissecting the antiviral innate immune responses. PMID:26657882

Quantum Information Biology: From Theory of Open Quantum Systems to Adaptive Dynamics

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

This chapter reviews quantum(-like) information biology (QIB). Here biology is treated widely as even covering cognition and its derivatives: psychology and decision making, sociology, and behavioral economics and finances. QIB provides an integrative description of information processing by bio-systems at all scales of life: from proteins and cells to cognition, ecological and social systems. Mathematically QIB is based on the theory of adaptive quantum systems (which covers also open quantum systems). Ideologically QIB is based on the quantum-like (QL) paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all bio-scales. QIB re ects the two fundamental principles: a) adaptivity; and, b) openness (bio-systems are fundamentally open). In addition, quantum adaptive dynamics provides the most generally possible mathematical representation of these principles.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

PubMed

Podobnik, Boris; Jusup, Marko; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M; Stanley, H Eugene

2017-11-07

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Biological conservation law as an emerging functionality in dynamical neuronal networks

PubMed Central

Podobnik, Boris; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M.

2017-01-01

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law—the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective. PMID:29078286

Computational structure analysis of biomacromolecule complexes by interface geometry.

PubMed

Mahdavi, Sedigheh; Salehzadeh-Yazdi, Ali; Mohades, Ali; Masoudi-Nejad, Ali

2013-12-01

The ability to analyze and compare protein-nucleic acid and protein-protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein-nucleic acid and protein-protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA-protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model based on solvent accessibility and the high validation parameters in comparison to the classical model. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cryo-EM of dynamic protein complexes in eukaryotic DNA replication.

PubMed

Sun, Jingchuan; Yuan, Zuanning; Bai, Lin; Li, Huilin

2017-01-01

DNA replication in Eukaryotes is a highly dynamic process that involves several dozens of proteins. Some of these proteins form stable complexes that are amenable to high-resolution structure determination by cryo-EM, thanks to the recent advent of the direct electron detector and powerful image analysis algorithm. But many of these proteins associate only transiently and flexibly, precluding traditional biochemical purification. We found that direct mixing of the component proteins followed by 2D and 3D image sorting can capture some very weakly interacting complexes. Even at 2D average level and at low resolution, EM images of these flexible complexes can provide important biological insights. It is often necessary to positively identify the feature-of-interest in a low resolution EM structure. We found that systematically fusing or inserting maltose binding protein (MBP) to selected proteins is highly effective in these situations. In this chapter, we describe the EM studies of several protein complexes involved in the eukaryotic DNA replication over the past decade or so. We suggest that some of the approaches used in these studies may be applicable to structural analysis of other biological systems. © 2016 The Protein Society.

Beyond the known functions of the CCR4-NOT complex in gene expression regulatory mechanisms: New structural insights to unravel CCR4-NOT mRNA processing machinery.

PubMed

Ukleja, Marta; Valpuesta, José María; Dziembowski, Andrzej; Cuellar, Jorge

2016-10-01

Large protein assemblies are usually the effectors of major cellular processes. The intricate cell homeostasis network is divided into numerous interconnected pathways, each controlled by a set of protein machines. One of these master regulators is the CCR4-NOT complex, which ultimately controls protein expression levels. This multisubunit complex assembles around a scaffold platform, which enables a wide variety of well-studied functions from mRNA synthesis to transcript decay, as well as other tasks still being identified. Solving the structure of the entire CCR4-NOT complex will help to define the distribution of its functions. The recently published three-dimensional reconstruction of the complex, in combination with the known crystal structures of some of the components, has begun to address this. Methodological improvements in structural biology, especially in cryoelectron microscopy, encourage further structural and protein-protein interaction studies, which will advance our comprehension of the gene expression machinery. © 2016 WILEY Periodicals, Inc.

A thorough experimental study of CH/π interactions in water: quantitative structure-stability relationships for carbohydrate/aromatic complexes.

PubMed

Jiménez-Moreno, Ester; Jiménez-Osés, Gonzalo; Gómez, Ana M; Santana, Andrés G; Corzana, Francisco; Bastida, Agatha; Jiménez-Barbero, Jesus; Asensio, Juan Luis

2015-11-13

CH/π interactions play a key role in a large variety of molecular recognition processes of biological relevance. However, their origins and structural determinants in water remain poorly understood. In order to improve our comprehension of these important interaction modes, we have performed a quantitative experimental analysis of a large data set comprising 117 chemically diverse carbohydrate/aromatic stacking complexes, prepared through a dynamic combinatorial approach recently developed by our group. The obtained free energies provide a detailed picture of the structure-stability relationships that govern the association process, opening the door to the rational design of improved carbohydrate-based ligands or carbohydrate receptors. Moreover, this experimental data set, supported by quantum mechanical calculations, has contributed to the understanding of the main driving forces that promote complex formation, underlining the key role played by coulombic and solvophobic forces on the stabilization of these complexes. This represents the most quantitative and extensive experimental study reported so far for CH/π complexes in water.

Dissecting the function of Cullin-RING ubiquitin ligase complex genes in planarian regeneration.

PubMed

Strand, Nicholas S; Allen, John M; Ghulam, Mahjoobah; Taylor, Matthew R; Munday, Roma K; Carrillo, Melissa; Movsesyan, Artem; Zayas, Ricardo M

2018-01-15

The ubiquitin system plays a role in nearly every aspect of eukaryotic cell biology. The enzymes responsible for transferring ubiquitin onto specific substrates are the E3 ubiquitin ligases, a large and diverse family of proteins, for which biological roles and target substrates remain largely undefined. Studies using model organisms indicate that ubiquitin signaling mediates key steps in developmental processes and tissue regeneration. Here, we used the freshwater planarian, Schmidtea mediterranea, to investigate the role of Cullin-RING ubiquitin ligase (CRL) complexes in stem cell regulation during regeneration. We identified six S. mediterranea cullin genes, and used RNAi to uncover roles for homologs of Cullin-1, -3 and -4 in planarian regeneration. The cullin-1 RNAi phenotype included defects in blastema formation, organ regeneration, lesions, and lysis. To further investigate the function of cullin-1-mediated cellular processes in planarians, we examined genes encoding the adaptor protein Skp1 and F-box substrate-recognition proteins that are predicted to partner with Cullin-1. RNAi against skp1 resulted in phenotypes similar to cullin-1 RNAi, and an RNAi screen of the F-box genes identified 19 genes that recapitulated aspects of cullin-1 RNAi, including ones that in mammals are involved in stem cell regulation and cancer biology. Our data provides evidence that CRLs play discrete roles in regenerative processes and provide a platform to investigate how CRLs regulate stem cells in vivo. Copyright © 2017 Elsevier Inc. All rights reserved.

Stimulus Sensitivity of a Spiking Neural Network Model

NASA Astrophysics Data System (ADS)

Chevallier, Julien

2018-02-01

Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.

Model-based design of experiments for cellular processes.

PubMed

Chakrabarty, Ankush; Buzzard, Gregery T; Rundell, Ann E

2013-01-01

Model-based design of experiments (MBDOE) assists in the planning of highly effective and efficient experiments. Although the foundations of this field are well-established, the application of these techniques to understand cellular processes is a fertile and rapidly advancing area as the community seeks to understand ever more complex cellular processes and systems. This review discusses the MBDOE paradigm along with applications and challenges within the context of cellular processes and systems. It also provides a brief tutorial on Fisher information matrix (FIM)-based and Bayesian experiment design methods along with an overview of existing software packages and computational advances that support MBDOE application and adoption within the Systems Biology community. As cell-based products and biologics progress into the commercial sector, it is anticipated that MBDOE will become an essential practice for design, quality control, and production. Copyright © 2013 Wiley Periodicals, Inc.

An analysis of the Petri net based model of the human body iron homeostasis process.

PubMed

Sackmann, Andrea; Formanowicz, Dorota; Formanowicz, Piotr; Koch, Ina; Blazewicz, Jacek

2007-02-01

In the paper a Petri net based model of the human body iron homeostasis is presented and analyzed. The body iron homeostasis is an important but not fully understood complex process. The modeling of the process presented in the paper is expressed in the language of Petri net theory. An application of this theory to the description of biological processes allows for very precise analysis of the resulting models. Here, such an analysis of the body iron homeostasis model from a mathematical point of view is given.

Antivirion Effects of Streptovaricin Complex Against Friend Virus

PubMed Central

Horoszewicz, Julius S.; Leong, Susan S.; Byrd, Daniel M.; Carter, William A.

1974-01-01

The in vitro antivirion activities of five different streptovaricin complex lots against the polycythemic strain of the Friend virus were evaluated. The assay system was based on the inhibition of the Friend virus-induced spleen foci. The virus inactivation process was shown to be susceptible to variation in temperature, pH, and time. The antivirion activity and the acute toxicity for mice, as well as the optical properties of these streptovaricin complexes, do not co-vary; this suggests that their biological activities are not associated with a single molecular structure. In addition, the antivirion activity of the five preparations of streptovaricin complex differs about 30-fold, indicating that this activity does not reside in a major component of the complex. PMID:15825311

Different Evolutionary Paths to Complexity for Small and Large Populations of Digital Organisms

PubMed Central

2016-01-01

A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large—but not intermediate-sized—populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations. PMID:27923053

Spectroscopic, cyclic voltammetric and biological studies of transition metal complexes with mixed nitrogen-sulphur (NS) donor macrocyclic ligand derived from thiosemicarbazide

NASA Astrophysics Data System (ADS)

Chandra, Sulekh; Gupta, Lokesh Kumar; Sangeetika

2005-11-01

The complexation of new mixed thia-aza-oxa macrocycle viz., 2,12-dithio-5,9,14,18-tetraoxo-7,16-dithia-1,3,4,10,11,13-hexaazacyclooctadecane containing thiosemicarba-zone unit with a series of transition metals Co(II), Ni(II) and Cu(II) has been investigated, by different spectroscopic techniques. The structural features of the ligand have been studied by EI-mass, 1H NMR and IR spectral techniques. Elemental analyses, magnetic moment susceptibility, molar conductance, IR, electronic, and EPR spectral studies characterized the complexes. Electronic absorption and IR spectra of the complexes indicate octahedral geometry for chloro, nitrato, thiocyanato or acetato complexes. The dimeric and neutral nature of the sulphato complexes are confirmed from magnetic susceptibility and low conductance values. Electronic spectra suggests square-planar geometry for all sulphato complexes. The redox behaviour was studied by cyclic voltammetry, show metal-centered reduction processes for all complexes. The complexes of copper show both oxidation and reduction process. The redox potentials depend on the conformation of central atom in the macrocyclic complexes. Newly synthesized macrocyclic ligand and its transition metal complexes show markedly growth inhibitory activity against pathogenic bacterias and plant pathogenic fungi under study. Most of the complexes have higher activity than that of the metal free ligand.

[Visual hygiene in LED lighting. Modern scientific imaginations].

PubMed

Deynego, V N; Kaptsov, V A

2014-01-01

There are considered a classic and modern paradigm of perception of light and its impact on human health. To consider the perception of light as a complex self-organizing synergistic system of compression of information in the process of its sequencing was supposed. This allowed to develop a complex of interrelated measures, which may become the basis for modern hygiene, and determine requirements for the led lamp with biologically adequate spectrum of the light, for which there were obtained patents in Russia, Europe and USA.

Bio-jETI: a service integration, design, and provisioning platform for orchestrated bioinformatics processes

PubMed Central

Margaria, Tiziana; Kubczak, Christian; Steffen, Bernhard

2008-01-01

Background With Bio-jETI, we introduce a service platform for interdisciplinary work on biological application domains and illustrate its use in a concrete application concerning statistical data processing in R and xcms for an LC/MS analysis of FAAH gene knockout. Methods Bio-jETI uses the jABC environment for service-oriented modeling and design as a graphical process modeling tool and the jETI service integration technology for remote tool execution. Conclusions As a service definition and provisioning platform, Bio-jETI has the potential to become a core technology in interdisciplinary service orchestration and technology transfer. Domain experts, like biologists not trained in computer science, directly define complex service orchestrations as process models and use efficient and complex bioinformatics tools in a simple and intuitive way. PMID:18460173

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

New Insights Into the Mechanisms and Biological Roles of D-Amino Acids in Complex Eco-Systems

PubMed Central

Aliashkevich, Alena; Alvarez, Laura; Cava, Felipe

2018-01-01

In the environment bacteria share their habitat with a great diversity of organisms, from microbes to humans, animals and plants. In these complex communities, the production of extracellular effectors is a common strategy to control the biodiversity by interfering with the growth and/or viability of nearby microbes. One of such effectors relies on the production and release of extracellular D-amino acids which regulate diverse cellular processes such as cell wall biogenesis, biofilm integrity, and spore germination. Non-canonical D-amino acids are mainly produced by broad spectrum racemases (Bsr). Bsr’s promiscuity allows it to generate high concentrations of D-amino acids in environments with variable compositions of L-amino acids. However, it was not clear until recent whether these molecules exhibit divergent functions. Here we review the distinctive biological roles of D-amino acids, their mechanisms of action and their modulatory properties of the biodiversity of complex eco-systems. PMID:29681896

A visual metaphor describing neural dynamics in schizophrenia.

PubMed

van Beveren, Nico J M; de Haan, Lieuwe

2008-07-09

In many scientific disciplines the use of a metaphor as an heuristic aid is not uncommon. A well known example in somatic medicine is the 'defense army metaphor' used to characterize the immune system. In fact, probably a large part of the everyday work of doctors consists of 'translating' scientific and clinical information (i.e. causes of disease, percentage of success versus risk of side-effects) into information tailored to the needs and capacities of the individual patient. The ability to do so in an effective way is at least partly what makes a clinician a good communicator. Schizophrenia is a severe psychiatric disorder which affects approximately 1% of the population. Over the last two decades a large amount of molecular-biological, imaging and genetic data have been accumulated regarding the biological underpinnings of schizophrenia. However, it remains difficult to understand how the characteristic symptoms of schizophrenia such as hallucinations and delusions are related to disturbances on the molecular-biological level. In general, psychiatry seems to lack a conceptual framework with sufficient explanatory power to link the mental- and molecular-biological domains. Here, we present an essay-like study in which we propose to use visualized concepts stemming from the theory on dynamical complex systems as a 'visual metaphor' to bridge the mental- and molecular-biological domains in schizophrenia. We first describe a computer model of neural information processing; we show how the information processing in this model can be visualized, using concepts from the theory on complex systems. We then describe two computer models which have been used to investigate the primary theory on schizophrenia, the neurodevelopmental model, and show how disturbed information processing in these two computer models can be presented in terms of the visual metaphor previously described. Finally, we describe the effects of dopamine neuromodulation, of which disturbances have been frequently described in schizophrenia, in terms of the same visualized metaphor. The conceptual framework and metaphor described offers a heuristic tool to understand the relationship between the mental- and molecular-biological domains in an intuitive way. The concepts we present may serve to facilitate communication between researchers, clinicians and patients.

Stem cells: The Next Therapeutic Frontier

PubMed Central

Humes, H. David

2005-01-01

Cell therapy is one of the most exciting fields in translational medicine. It stands at the intersection of a variety of rapidly developing scientific disciplines: stem cell biology, immunology, tissue engineering, molecular biology, biomaterials, transplantation biology, regenerative medicine, and clinical research. Cell-based therapy may develop into a new therapeutic platform to treat a vast array of clinical disorders. Blood transfusions and bone marrow transplantation are prime examples of the successful application of cell-based therapeutics; but recent advances in cellular and molecular biology have expanded the potential applications of this approach. Although recombinant genetic engineering to produce a variety of therapeutics such as human erythropoietin and insulin has proven successful, these treatments are unable to completely correct or reverse disease states, because most common disease processes are not due to the deficiency of a single protein but develop due to alterations in the complex interactions of a variety of cell components. In these complex situations, cell-based therapy may be a more successful strategy by providing a dynamic, interactive, and individualized therapeutic approach that responds to the pathophysiological condition of the patient. In this regard, cells may provide innovative methods for drug delivery of biologics, immunotherapy, and tissue regenerative or replacement engineering (1,2). The translation of this discipline to medical practice has tremendous potential, but in many applications technological issues need to be overcome. Since many cell-based indications are already being evaluated in the clinic, the field appears to be on the threshold of a number of successes. This review will focus on our group's use of human stem/progenitor cells in the treatment of acute and chronic renal failure as extensions to the current successful renal substitution processes of hemodialysis and hemofiltration. PMID:16555613

Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

PubMed

Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

2018-01-01

There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

Color mapping of one specific velocity of a biological fluid flows with complex geometry using optical coherence tomography

NASA Astrophysics Data System (ADS)

Potlov, A. Yu.; Frolov, S. V.; Proskurin, S. G.

2018-04-01

The method of Doppler color mapping of one specific (previously chosen) velocity in a turbulent flow inside biological tissues using optical coherence tomography is described. The key features of the presented method are: the raw data are separated into three parts, corresponding to the unmoving biological tissue, the positively and negatively directed biological fluid flows; the further independent signal processing procedure yields the structure image and two images of the chosen velocity, which are then normalised, encoded and joined. The described method can be used to obtain in real time the anatomical maps of the chosen velocities in normal and pathological states. The described method can be applied not only in optical coherence tomography, but also in endoscopic and Doppler ultrasonic medical imaging systems.

Directed evolution and synthetic biology applications to microbial systems.

PubMed

Bassalo, Marcelo C; Liu, Rongming; Gill, Ryan T

2016-06-01

Biotechnology applications require engineering complex multi-genic traits. The lack of knowledge on the genetic basis of complex phenotypes restricts our ability to rationally engineer them. However, complex phenotypes can be engineered at the systems level, utilizing directed evolution strategies that drive whole biological systems toward desired phenotypes without requiring prior knowledge of the genetic basis of the targeted trait. Recent developments in the synthetic biology field accelerates the directed evolution cycle, facilitating engineering of increasingly complex traits in biological systems. In this review, we summarize some of the most recent advances in directed evolution and synthetic biology that allows engineering of complex traits in microbial systems. Then, we discuss applications that can be achieved through engineering at the systems level. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins.

PubMed

Corominas-Faja, Bruna; Santangelo, Elvira; Cuyàs, Elisabet; Micol, Vicente; Joven, Jorge; Ariza, Xavier; Segura-Carretero, Antonio; García, Jordi; Menendez, Javier A

2014-09-01

Aging is associated with common conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer's disease. The type of multi-targeted pharmacological approach necessary to address a complex multifaceted disease such as aging might take advantage of pleiotropic natural polyphenols affecting a wide variety of biological processes. We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health-promoting extra virgin olive oil (EVOO), might constitute a new family of plant-produced gerosuppressant agents. This paper describes an analysis of the biological activity spectra (BAS) of OA and DOA using PASS (Prediction of Activity Spectra for Substances) software. PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound's structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities. Using Pharmaexpert, a tool that analyzes the PASS-predicted BAS of substances based on thousands of "mechanism-effect" and "effect-mechanism" relationships, we illuminate hypothesis-generating pharmacological effects, mechanisms of action, and targets that might underlie the anti-aging/anti-cancer activities of the gerosuppressant EVOO oleuropeins.

Methane-Oxidizing Enzymes: An Upstream Problem in Biological Gas-to-Liquids Conversion

PubMed Central

Lawton, Thomas J.; Rosenzweig, Amy C.

2017-01-01

Biological conversion of natural gas to liquids (Bio-GTL) represents an immense economic opportunity. In nature, aerobic methanotrophic bacteria and anaerobic archaea are able to selectively oxidize methane using methane monooxygenase (MMO) and methyl coenzyme M reductase (MCR) enzymes. Although significant progress has been made toward genetically manipulating these organisms for biotechnological applications, the enzymes themselves are slow, complex, and not recombinantly tractable in traditional industrial hosts. With turnover numbers of 0.16–13 s−1, these enzymes pose a considerable upstream problem in the biological production of fuels or chemicals from methane. Methane oxidation enzymes will need to be engineered to be faster to enable high volumetric productivities; however, efforts to do so and to engineer simpler enzymes have been minimally successful. Moreover, known methane-oxidizing enzymes have different expression levels, carbon and energy efficiencies, require auxiliary systems for biosynthesis and function, and vary considerably in terms of complexity and reductant requirements. The pros and cons of using each methane-oxidizing enzyme for Bio-GTL are considered in detail. The future for these enzymes is bright, but a renewed focus on studying them will be critical to the successful development of biological processes that utilize methane as a feedstock. PMID:27366961

Methane-Oxidizing Enzymes: An Upstream Problem in Biological Gas-to-Liquids Conversion.

PubMed

Lawton, Thomas J; Rosenzweig, Amy C

2016-08-03

Biological conversion of natural gas to liquids (Bio-GTL) represents an immense economic opportunity. In nature, aerobic methanotrophic bacteria and anaerobic archaea are able to selectively oxidize methane using methane monooxygenase (MMO) and methyl coenzyme M reductase (MCR) enzymes. Although significant progress has been made toward genetically manipulating these organisms for biotechnological applications, the enzymes themselves are slow, complex, and not recombinantly tractable in traditional industrial hosts. With turnover numbers of 0.16-13 s(-1), these enzymes pose a considerable upstream problem in the biological production of fuels or chemicals from methane. Methane oxidation enzymes will need to be engineered to be faster to enable high volumetric productivities; however, efforts to do so and to engineer simpler enzymes have been minimally successful. Moreover, known methane-oxidizing enzymes have different expression levels, carbon and energy efficiencies, require auxiliary systems for biosynthesis and function, and vary considerably in terms of complexity and reductant requirements. The pros and cons of using each methane-oxidizing enzyme for Bio-GTL are considered in detail. The future for these enzymes is bright, but a renewed focus on studying them will be critical to the successful development of biological processes that utilize methane as a feedstock.

Biocharts: a visual formalism for complex biological systems

PubMed Central

Kugler, Hillel; Larjo, Antti; Harel, David

2010-01-01

We address one of the central issues in devising languages, methods and tools for the modelling and analysis of complex biological systems, that of linking high-level (e.g. intercellular) information with lower-level (e.g. intracellular) information. Adequate ways of dealing with this issue are crucial for understanding biological networks and pathways, which typically contain huge amounts of data that continue to grow as our knowledge and understanding of a system increases. Trying to comprehend such data using the standard methods currently in use is often virtually impossible. We propose a two-tier compound visual language, which we call Biocharts, that is geared towards building fully executable models of biological systems. One of the main goals of our approach is to enable biologists to actively participate in the computational modelling effort, in a natural way. The high-level part of our language is a version of statecharts, which have been shown to be extremely successful in software and systems engineering. The statecharts can be combined with any appropriately well-defined language (preferably a diagrammatic one) for specifying the low-level dynamics of the pathways and networks. We illustrate the language and our general modelling approach using the well-studied process of bacterial chemotaxis. PMID:20022895

Measurement Frontiers in Molecular Biology

NASA Astrophysics Data System (ADS)

Laderman, Stephen

2009-03-01

Developments of molecular measurements and manipulations have long enabled forefront research in evolution, genetics, biological development and its dysfunction, and the impact of external factors on the behavior of cells. Measurement remains at the heart of exciting and challenging basic and applied problems in molecular and cell biology. Methods to precisely determine the identity and abundance of particular molecules amongst a complex mixture of similar and dissimilar types require the successful design and integration of multiple steps involving biochemical manipulations, separations, physical probing, and data processing. Accordingly, today's most powerful methods for characterizing life at the molecular level depend on coordinated advances in applied physics, biochemistry, chemistry, computer science, and engineering. This is well illustrated by recent approaches to the measurement of DNA, RNA, proteins, and intact cells. Such successes underlie well founded visions of how molecular biology can further assist in answering compelling scientific questions and in enabling the development of remarkable advances in human health. These visions, in turn, are motivating the interdisciplinary creation of even more comprehensive measurements. As a further and closely related consequence, they are motivating innovations in the conceptual and practical approaches to organizing and visualizing large, complex sets of interrelated experimental results and distilling from those data compelling, informative conclusions.

The Emergence of Temporal Structures in Dynamical Systems

NASA Astrophysics Data System (ADS)

Mainzer, Klaus

2010-10-01

Dynamical systems in classical, relativistic and quantum physics are ruled by laws with time reversibility. Complex dynamical systems with time-irreversibility are known from thermodynamics, biological evolution, growth of organisms, brain research, aging of people, and historical processes in social sciences. Complex systems are systems that compromise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous emergence of distinctive temporal, spatial or functional structures. But, emergence is no mystery. In a general meaning, the emergence of macroscopic features results from the nonlinear interactions of the elements in a complex system. Mathematically, the emergence of irreversible structures is modelled by phase transitions in non-equilibrium dynamics of complex systems. These methods have been modified even for chemical, biological, economic and societal applications (e.g., econophysics). Emergence of irreversible structures can also be simulated by computational systems. The question arises how the emergence of irreversible structures is compatible with the reversibility of fundamental physical laws. It is argued that, according to quantum cosmology, cosmic evolution leads from symmetry to complexity of irreversible structures by symmetry breaking and phase transitions. Thus, arrows of time and aging processes are not only subjective experiences or even contradictions to natural laws, but they can be explained by quantum cosmology and the nonlinear dynamics of complex systems. Human experiences and religious concepts of arrows of time are considered in a modern scientific framework. Platonic ideas of eternity are at least understandable with respect to mathematical invariance and symmetry of physical laws. Heraclit’s world of change and dynamics can be mapped onto our daily real-life experiences of arrows of time.

The Organic Complexation of Iron in the Marine Environment: A Review

PubMed Central

Gledhill, Martha; Buck, Kristen N.

2012-01-01

Iron (Fe) is an essential micronutrient for marine organisms, and it is now well established that low Fe availability controls phytoplankton productivity, community structure, and ecosystem functioning in vast regions of the global ocean. The biogeochemical cycle of Fe involves complex interactions between lithogenic inputs (atmospheric, continental, or hydrothermal), dissolution, precipitation, scavenging, biological uptake, remineralization, and sedimentation processes. Each of these aspects of Fe biogeochemical cycling is likely influenced by organic Fe-binding ligands, which complex more than 99% of dissolved Fe. In this review we consider recent advances in our knowledge of Fe complexation in the marine environment and their implications for the biogeochemistry of Fe in the ocean. We also highlight the importance of constraining the dissolved Fe concentration value used in interpreting voltammetric titration data for the determination of Fe speciation. Within the published Fe speciation data, there appear to be important temporal and spatial variations in Fe-binding ligand concentrations and their conditional stability constants in the marine environment. Excess ligand concentrations, particularly in the truly soluble size fraction, seem to be consistently higher in the upper water column, and especially in Fe-limited, but productive, waters. Evidence is accumulating for an association of Fe with both small, well-defined ligands, such as siderophores, as well as with larger, macromolecular complexes like humic substances, exopolymeric substances, and transparent exopolymers. The diverse size spectrum and chemical nature of Fe ligand complexes corresponds to a change in kinetic inertness which will have a consequent impact on biological availability. However, much work is still to be done in coupling voltammetry, mass spectrometry techniques, and process studies to better characterize the nature and cycling of Fe-binding ligands in the marine environment. PMID:22403574

Innovation: an emerging focus from cells to societies.

PubMed

Hochberg, Michael E; Marquet, Pablo A; Boyd, Robert; Wagner, Andreas

2017-12-05

Innovations are generally unexpected, often spectacular changes in phenotypes and ecological functions. The contributions to this theme issue are the latest conceptual, theoretical and experimental developments, addressing how ecology, environment, ontogeny and evolution are central to understanding the complexity of the processes underlying innovations. Here, we set the stage by introducing and defining key terms relating to innovation and discuss their relevance to biological, cultural and technological change. Discovering how the generation and transmission of novel biological information, environmental interactions and selective evolutionary processes contribute to innovation as an ecosystem will shed light on how the dominant features across life come to be, generalize to social, cultural and technological evolution, and have applications in the health sciences and sustainability.This article is part of the theme issue 'Process and pattern in innovations from cells to societies'. © 2017 The Author(s).

Innovation: an emerging focus from cells to societies

PubMed Central

Boyd, Robert

2017-01-01

Innovations are generally unexpected, often spectacular changes in phenotypes and ecological functions. The contributions to this theme issue are the latest conceptual, theoretical and experimental developments, addressing how ecology, environment, ontogeny and evolution are central to understanding the complexity of the processes underlying innovations. Here, we set the stage by introducing and defining key terms relating to innovation and discuss their relevance to biological, cultural and technological change. Discovering how the generation and transmission of novel biological information, environmental interactions and selective evolutionary processes contribute to innovation as an ecosystem will shed light on how the dominant features across life come to be, generalize to social, cultural and technological evolution, and have applications in the health sciences and sustainability. This article is part of the theme issue ‘Process and pattern in innovations from cells to societies’. PMID:29061887

[Medical certification in workers involved in logging and wood-processing].

PubMed

Romankow, Jacek

2007-01-01

Activities involved in forestry and woodworking industry are associated with workers being exposed to numerous environmental and technology-related factors that are detrimental to their health. Such hazards include working in changeable climatic conditions, in the vicinity of heavy equipment, exposure to noise, chainsaw vibrations, enforced body positioning, hard physical work, the effect of exhaust gases, potential effects of biological factors, including epizootic diseases. Wood processing involves performing mechanical activities employing tools and machines, as well as processes utilizing various chemical substances. Forestry and woodworking industry workers may deal both with timber and with wood products. In medical certification, the following issues are of significance: work in the vicinity of rotational elements, noise, effects of chemicals or biological factors, including carcinogenic substances. For this reason, the procedures involved in medical examinations of such workers are complex.

Modeling and simulation of high dimensional stochastic multiscale PDE systems at the exascale

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

Zabaras, Nicolas J.

2016-11-08

Predictive Modeling of multiscale and Multiphysics systems requires accurate data driven characterization of the input uncertainties, and understanding of how they propagate across scales and alter the final solution. This project develops a rigorous mathematical framework and scalable uncertainty quantification algorithms to efficiently construct realistic low dimensional input models, and surrogate low complexity systems for the analysis, design, and control of physical systems represented by multiscale stochastic PDEs. The work can be applied to many areas including physical and biological processes, from climate modeling to systems biology.

Pattern formation and collective effects in populations of magnetic microswimmers

NASA Astrophysics Data System (ADS)

Vach, Peter J.; Walker, Debora; Fischer, Peer; Fratzl, Peter; Faivre, Damien

2017-03-01

Self-propelled particles are one prototype of synthetic active matter used to understand complex biological processes, such as the coordination of movement in bacterial colonies or schools of fishes. Collective patterns such as clusters were observed for such systems, reproducing features of biological organization. However, one limitation of this model is that the synthetic assemblies are made of identical individuals. Here we introduce an active system based on magnetic particles at colloidal scales. We use identical but also randomly-shaped magnetic micropropellers and show that they exhibit dynamic and reversible pattern formation.

Epidemic processes in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Castellano, Claudio; Van Mieghem, Piet; Vespignani, Alessandro

2015-07-01

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

Developmental engineering: a new paradigm for the design and manufacturing of cell-based products. Part II: from genes to networks: tissue engineering from the viewpoint of systems biology and network science.

PubMed

Lenas, Petros; Moos, Malcolm; Luyten, Frank P

2009-12-01

The field of tissue engineering is moving toward a new concept of "in vitro biomimetics of in vivo tissue development." In Part I of this series, we proposed a theoretical framework integrating the concepts of developmental biology with those of process design to provide the rules for the design of biomimetic processes. We named this methodology "developmental engineering" to emphasize that it is not the tissue but the process of in vitro tissue development that has to be engineered. To formulate the process design rules in a rigorous way that will allow a computational design, we should refer to mathematical methods to model the biological process taking place in vitro. Tissue functions cannot be attributed to individual molecules but rather to complex interactions between the numerous components of a cell and interactions between cells in a tissue that form a network. For tissue engineering to advance to the level of a technologically driven discipline amenable to well-established principles of process engineering, a scientifically rigorous formulation is needed of the general design rules so that the behavior of networks of genes, proteins, or cells that govern the unfolding of developmental processes could be related to the design parameters. Now that sufficient experimental data exist to construct plausible mathematical models of many biological control circuits, explicit hypotheses can be evaluated using computational approaches to facilitate process design. Recent progress in systems biology has shown that the empirical concepts of developmental biology that we used in Part I to extract the rules of biomimetic process design can be expressed in rigorous mathematical terms. This allows the accurate characterization of manufacturing processes in tissue engineering as well as the properties of the artificial tissues themselves. In addition, network science has recently shown that the behavior of biological networks strongly depends on their topology and has developed the necessary concepts and methods to describe it, allowing therefore a deeper understanding of the behavior of networks during biomimetic processes. These advances thus open the door to a transition for tissue engineering from a substantially empirical endeavor to a technology-based discipline comparable to other branches of engineering.

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

Complex systems dynamics in aging: new evidence, continuing questions.

PubMed

Cohen, Alan A

2016-02-01

There have long been suggestions that aging is tightly linked to the complex dynamics of the physiological systems that maintain homeostasis, and in particular to dysregulation of regulatory networks of molecules. This review synthesizes recent work that is starting to provide evidence for the importance of such complex systems dynamics in aging. There is now clear evidence that physiological dysregulation--the gradual breakdown in the capacity of complex regulatory networks to maintain homeostasis--is an emergent property of these regulatory networks, and that it plays an important role in aging. It can be measured simply using small numbers of biomarkers. Additionally, there are indications of the importance during aging of emergent physiological processes, functional processes that cannot be easily understood through clear metabolic pathways, but can nonetheless be precisely quantified and studied. The overall role of such complex systems dynamics in aging remains an important open question, and to understand it future studies will need to distinguish and integrate related aspects of aging research, including multi-factorial theories of aging, systems biology, bioinformatics, network approaches, robustness, and loss of complexity.

Mammalian synthetic biology: emerging medical applications

PubMed Central

Kis, Zoltán; Pereira, Hugo Sant'Ana; Homma, Takayuki; Pedrigi, Ryan M.; Krams, Rob

2015-01-01

In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON–OFF switches, categorized into transcriptional, post-transcriptional, translational and post-translational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes. PMID:25808341

Light-energy conversion in engineered microorganisms.

PubMed

Johnson, Ethan T; Schmidt-Dannert, Claudia

2008-12-01

Increasing interest in renewable resources by the energy and chemical industries has spurred new technologies both to capture solar energy and to develop biologically derived chemical feedstocks and fuels. Advances in molecular biology and metabolic engineering have provided new insights and techniques for increasing biomass and biohydrogen production, and recent efforts in synthetic biology have demonstrated that complex regulatory and metabolic networks can be designed and engineered in microorganisms. Here, we explore how light-driven processes may be incorporated into nonphotosynthetic microbes to boost metabolic capacity for the production of industrial and fine chemicals. Progress towards the introduction of light-driven proton pumping or anoxygenic photosynthesis into Escherichia coli to increase the efficiency of metabolically-engineered biosynthetic pathways is highlighted.

Systems biology approach to bioremediation

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

Chakraborty, Romy; Wu, Cindy H.; Hazen, Terry C.

2012-06-01

Bioremediation has historically been approached as a ‘black box’ in terms of our fundamental understanding. Thus it succeeds and fails, seldom without a complete understanding of why. Systems biology is an integrated research approach to study complex biological systems, by investigating interactions and networks at the molecular, cellular, community, and ecosystem level. The knowledge of these interactions within individual components is fundamental to understanding the dynamics of the ecosystem under investigation. Finally, understanding and modeling functional microbial community structure and stress responses in environments at all levels have tremendous implications for our fundamental understanding of hydrobiogeochemical processes and the potentialmore » for making bioremediation breakthroughs and illuminating the ‘black box’.« less

Using a biased qubit to probe complex systems

NASA Astrophysics Data System (ADS)

Pollock, Felix A.; Checińska, Agata; Pascazio, Saverio; Modi, Kavan

2016-09-01

Complex mesoscopic systems play increasingly important roles in modern science, from understanding biological functions at the molecular level to designing solid-state information processing devices. The operation of these systems typically depends on their energetic structure, yet probing their energy landscape can be extremely challenging; they have many degrees of freedom, which may be hard to isolate and measure independently. Here, we show that a qubit (a two-level quantum system) with a biased energy splitting can directly probe the spectral properties of a complex system, without knowledge of how they couple. Our work is based on the completely positive and trace-preserving map formalism, which treats any unknown dynamics as a "black-box" process. This black box contains information about the system with which the probe interacts, which we access by measuring the survival probability of the initial state of the probe as function of the energy splitting and the process time. Fourier transforming the results yields the energy spectrum of the complex system. Without making assumptions about the strength or form of its coupling, our probe could determine aspects of a complex molecule's energy landscape as well as, in many cases, test for coherent superposition of its energy eigenstates.

Synthetic biology: advancing the design of diverse genetic systems

PubMed Central

Wang, Yen-Hsiang; Wei, Kathy Y.; Smolke, Christina D.

2013-01-01

A main objective of synthetic biology is to make the process of designing genetically-encoded biological systems more systematic, predictable, robust, scalable, and efficient. The examples of genetic systems in the field vary widely in terms of operating hosts, compositional approaches, and network complexity, ranging from a simple genetic switch to search-and-destroy systems. While significant advances in synthesis capabilities support the potential for the implementation of pathway- and genome-scale programs, several design challenges currently restrict the scale of systems that can be reasonably designed and implemented. Synthetic biology offers much promise in developing systems to address challenges faced in manufacturing, the environment and sustainability, and health and medicine, but the realization of this potential is currently limited by the diversity of available parts and effective design frameworks. As researchers make progress in bridging this design gap, advances in the field hint at ever more diverse applications for biological systems. PMID:23413816

Light microscopy applications in systems biology: opportunities and challenges

PubMed Central

2013-01-01

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology. PMID:23578051

Designing and testing a classroom curriculum to teach preschoolers about the biology of physical activity: The respiration system as an underlying biological causal mechanism

NASA Astrophysics Data System (ADS)

Ewing, Tracy S.

The present study examined young children's understanding of respiration and oxygen as a source of vital energy underlying physical activity. Specifically, the purpose of the study was to explore whether a coherent biological theory, characterized by an understanding that bodily parts (heart and lungs) and processes (oxygen in respiration) as part of a biological system, can be taught as a foundational concept to reason about physical activity. The effects of a biology-based intervention curriculum designed to teach preschool children about bodily functions as a part of the respiratory system, the role of oxygen as a vital substance and how physical activity acts an energy source were examined. Participants were recruited from three private preschool classrooms (two treatment; 1 control) in Southern California and included a total of 48 four-year-old children (30 treatment; 18 control). Findings from this study suggested that young children could be taught relevant biological concepts about the role of oxygen in respiratory processes. Children who received biology-based intervention curriculum made significant gains in their understanding of the biology of respiration, identification of physical and sedentary activities. In addition these children demonstrated that coherence of conceptual knowledge was correlated with improved accuracy at activity identification and reasoning about the inner workings of the body contributing to endurance. Findings from this study provided evidence to support the benefits of providing age appropriate but complex coherent biological instruction to children in early childhood settings.

Spectroscopic techniques to study the immune response in human saliva

NASA Astrophysics Data System (ADS)

Nepomnyashchaya, E.; Savchenko, E.; Velichko, E.; Bogomaz, T.; Aksenov, E.

2018-01-01

Studies of the immune response dynamics by means of spectroscopic techniques, i.e., laser correlation spectroscopy and fluorescence spectroscopy, are described. The laser correlation spectroscopy is aimed at measuring sizes of particles in biological fluids. The fluorescence spectroscopy allows studying of the conformational and other structural changings in immune complex. We have developed a new scheme of a laser correlation spectrometer and an original signal processing algorithm. We have suggested a new fluorescence detection scheme based on a prism and an integrating pin diode. The developed system based on the spectroscopic techniques allows studies of complex process in human saliva and opens some prospects for an individual treatment of immune diseases.