model biological processes: Topics by Science.gov

Sample records for model biological processes

The (Mathematical) Modeling Process in Biosciences.

PubMed

Torres, Nestor V; Santos, Guido

2015-01-01

In this communication, we introduce a general framework and discussion on the role of models and the modeling process in the field of biosciences. The objective is to sum up the common procedures during the formalization and analysis of a biological problem from the perspective of Systems Biology, which approaches the study of biological systems as a whole. We begin by presenting the definitions of (biological) system and model. Particular attention is given to the meaning of mathematical model within the context of biology. Then, we present the process of modeling and analysis of biological systems. Three stages are described in detail: conceptualization of the biological system into a model, mathematical formalization of the previous conceptual model and optimization and system management derived from the analysis of the mathematical model. All along this work the main features and shortcomings of the process are analyzed and a set of rules that could help in the task of modeling any biological system are presented. Special regard is given to the formative requirements and the interdisciplinary nature of this approach. We conclude with some general considerations on the challenges that modeling is posing to current biology.
The (Mathematical) Modeling Process in Biosciences

PubMed Central

Torres, Nestor V.; Santos, Guido

2015-01-01

In this communication, we introduce a general framework and discussion on the role of models and the modeling process in the field of biosciences. The objective is to sum up the common procedures during the formalization and analysis of a biological problem from the perspective of Systems Biology, which approaches the study of biological systems as a whole. We begin by presenting the definitions of (biological) system and model. Particular attention is given to the meaning of mathematical model within the context of biology. Then, we present the process of modeling and analysis of biological systems. Three stages are described in detail: conceptualization of the biological system into a model, mathematical formalization of the previous conceptual model and optimization and system management derived from the analysis of the mathematical model. All along this work the main features and shortcomings of the process are analyzed and a set of rules that could help in the task of modeling any biological system are presented. Special regard is given to the formative requirements and the interdisciplinary nature of this approach. We conclude with some general considerations on the challenges that modeling is posing to current biology. PMID:26734063
Coupling biology and oceanography in models.

PubMed

Fennel, W; Neumann, T

2001-08-01

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

PubMed Central

2011-01-01

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future. PMID:22141422
Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

PubMed

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

2015-08-21

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

PubMed

Djordjevic, Ivan B

2015-08-24

Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually coupled.
Markov Chain-Like Quantum Biological Modeling of Mutations, Aging, and Evolution

PubMed Central

Djordjevic, Ivan B.

2015-01-01

Recent evidence suggests that quantum mechanics is relevant in photosynthesis, magnetoreception, enzymatic catalytic reactions, olfactory reception, photoreception, genetics, electron-transfer in proteins, and evolution; to mention few. In our recent paper published in Life, we have derived the operator-sum representation of a biological channel based on codon basekets, and determined the quantum channel model suitable for study of the quantum biological channel capacity. However, this model is essentially memoryless and it is not able to properly model the propagation of mutation errors in time, the process of aging, and evolution of genetic information through generations. To solve for these problems, we propose novel quantum mechanical models to accurately describe the process of creation spontaneous, induced, and adaptive mutations and their propagation in time. Different biological channel models with memory, proposed in this paper, include: (i) Markovian classical model, (ii) Markovian-like quantum model, and (iii) hybrid quantum-classical model. We then apply these models in a study of aging and evolution of quantum biological channel capacity through generations. We also discuss key differences of these models with respect to a multilevel symmetric channel-based Markovian model and a Kimura model-based Markovian process. These models are quite general and applicable to many open problems in biology, not only biological channel capacity, which is the main focus of the paper. We will show that the famous quantum Master equation approach, commonly used to describe different biological processes, is just the first-order approximation of the proposed quantum Markov chain-like model, when the observation interval tends to zero. One of the important implications of this model is that the aging phenotype becomes determined by different underlying transition probabilities in both programmed and random (damage) Markov chain-like models of aging, which are mutually coupled. PMID:26305258
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.
Modeling biological gradient formation: combining partial differential equations and Petri nets.

PubMed

Bertens, Laura M F; Kleijn, Jetty; Hille, Sander C; Heiner, Monika; Koutny, Maciej; Verbeek, Fons J

2016-01-01

Both Petri nets and differential equations are important modeling tools for biological processes. In this paper we demonstrate how these two modeling techniques can be combined to describe biological gradient formation. Parameters derived from partial differential equation describing the process of gradient formation are incorporated in an abstract Petri net model. The quantitative aspects of the resulting model are validated through a case study of gradient formation in the fruit fly.
Quantum biological channel modeling and capacity calculation.

PubMed

Djordjevic, Ivan B

2012-12-10

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

ERIC Educational Resources Information Center

Nowak, Kathrin Helena; Nehring, Andreas; Tiemann, Rüdiger; Upmeier zu Belzen, Annette

2013-01-01

The aim of the study was to describe, categorise and analyse students' (aged 14-16) processes of scientific inquiry in biology and chemistry education. Therefore, a theoretical structure for scientific inquiry for both biology and chemistry, the VerE model, was developed. This model consists of nine epistemological acts, which combine processes of…
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.
Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403
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
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.
Mathematical and Computational Modeling in Complex Biological Systems

PubMed Central

Li, Wenyang; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. PMID:28386558
Mathematical and Computational Modeling in Complex Biological Systems.

PubMed

Ji, Zhiwei; Yan, Ke; Li, Wenyang; Hu, Haigen; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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.

Biomolecular Modeling in a Process Dynamics and Control Course

ERIC Educational Resources Information Center

Gray, Jeffrey J.

2006-01-01

I present modifications to the traditional course entitled, "Process dynamics and control," which I renamed "Modeling, dynamics, and control of chemical and biological processes." Additions include the central dogma of biology, pharmacokinetic systems, population balances, control of gene transcription, and large-scale…
CHALLENGES OF PROCESSING BIOLOGICAL DATA FOR INCORPORATION INTO A LAKE EUTROPHICATION MODEL

EPA Science Inventory

A eutrophication model is in development as part of the Lake Michigan Mass Balance Project (LMMBP). Successful development and calibration of this model required the processing and incorporation of extensive biological data. Data were drawn from multiple sources, including nutrie...
A psychological model of mental disorder.

PubMed

Kinderman, Peter

2005-01-01

A coherent conceptualization of the role of psychological factors is of great importance in understanding mental disorder. Academic articles and professional reports alluding to psychological models of the etiology of mental disorder are becoming increasingly common, and there is evidence of a marked policy shift toward the provision of psychological therapies and interventions. This article discusses the relationship between biological, social, and psychological factors in the causation and treatment of mental disorder. It argues that simple biological reductionism is not scientifically justified, and also that the specific role of psychological processes within the biopsychosocial model requires further elaboration. The biopsychosocial model is usually interpreted as implying that biological, psychological, and social factors are co-equal partners in the etiology of mental disorder. The psychological model of mental disorder presented here suggests that disruption or dysfunction in psychological processes is a final common pathway in the development of mental disorder. These processes include, but are not limited to, cognitive processes. The model proposes that biological and social factors, together with a person's individual experiences, lead to mental disorder through their conjoint effects on those psychological processes. Implications for research, interventions, and policy are discussed.
Modeling biochemical transformation processes and information processing with Narrator.

PubMed

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

2007-03-27

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

PubMed Central

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

2007-01-01

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

PubMed

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

2004-07-01

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

NASA Astrophysics Data System (ADS)

Kindfield, Ann C. H.

Central to secondary and college-level biology instruction is the development of student understanding of a number of subcellular processes. Yet some of the most crucial are consistently cited as the most difficult components of biology to learn. Among these is meiosis. In this article I report on the meiosis models utilized by five individuals at each of three levels of expertise in genetics as each reasoned about this process in an individual interview setting. Detailed characterization of individual meiosis models and comparison among models revealed a set of biologically correct features common to all individuals' models as well as a variety of model flaws (i.e., meiosis misunderstandings) which are categorized according to type and level of expertise. These results are suggestive of both sources of various misunderstandings and factors that might contribute to the construction of a sound understanding of meiosis. Each of these is addressed in relation to their respective implications for instruction.
Invited review liquid crystal models of biological materials and silk spinning.

PubMed

Rey, Alejandro D; Herrera-Valencia, Edtson E

2012-06-01

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

ERIC Educational Resources Information Center

Kindfield, A. C. H.

1994-01-01

Reports on the meiosis models utilized by five individuals at each of three levels of expertise in genetics as each reasoned about this process in an individual interview setting. Results revealed a set of biologically correct features common to all individuals' models as well as a variety of model flaws (i.e., meiosis misunderstandings) which are…
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
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.
Activated sludge model (ASM) based modelling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities.

PubMed

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

2010-08-01

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

ERIC Educational Resources Information Center

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

2010-01-01

Mathematical manipulative models have had a long history of influence in biological research and in secondary school education, but they are frequently neglected in undergraduate biology education. By linking mathematical manipulative models in a four-step process--1) use of physical manipulatives, 2) interactive exploration of computer…
An effective hierarchical model for the biomolecular covalent bond: an approach integrating artificial chemistry and an actual terrestrial life system.

PubMed

Oohashi, Tsutomu; Ueno, Osamu; Maekawa, Tadao; Kawai, Norie; Nishina, Emi; Honda, Manabu

2009-01-01

Under the AChem paradigm and the programmed self-decomposition (PSD) model, we propose a hierarchical model for the biomolecular covalent bond (HBCB model). This model assumes that terrestrial organisms arrange their biomolecules in a hierarchical structure according to the energy strength of their covalent bonds. It also assumes that they have evolutionarily selected the PSD mechanism of turning biological polymers (BPs) into biological monomers (BMs) as an efficient biomolecular recycling strategy We have examined the validity and effectiveness of the HBCB model by coordinating two complementary approaches: biological experiments using existent terrestrial life, and simulation experiments using an AChem system. Biological experiments have shown that terrestrial life possesses a PSD mechanism as an endergonic, genetically regulated process and that hydrolysis, which decomposes a BP into BMs, is one of the main processes of such a mechanism. In simulation experiments, we compared different virtual self-decomposition processes. The virtual species in which the self-decomposition process mainly involved covalent bond cleavage from a BP to BMs showed evolutionary superiority over other species in which the self-decomposition process involved cleavage from BP to classes lower than BM. These converging findings strongly support the existence of PSD and the validity and effectiveness of the HBCB model.
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.
Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

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

1999-08-01

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

NASA Astrophysics Data System (ADS)

Rivoire, Olivier

2016-03-01

Biological organisms adapt to changes by processing informations from different sources, most notably from their ancestors and from their environment. We review an approach to quantify these informations by analyzing mathematical models of evolutionary dynamics and show how explicit results are obtained for a solvable subclass of these models. In several limits, the results coincide with those obtained in studies of information processing for communication, gambling or thermodynamics. In the most general case, however, information processing by biological populations shows unique features that motivate the analysis of specific models.
Functional model of biological neural networks.

PubMed

Lo, James Ting-Ho

2010-12-01

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

Review of stochastic hybrid systems with applications in biological systems modeling and analysis.

PubMed

Li, Xiangfang; Omotere, Oluwaseyi; Qian, Lijun; Dougherty, Edward R

2017-12-01

Stochastic hybrid systems (SHS) have attracted a lot of research interests in recent years. In this paper, we review some of the recent applications of SHS to biological systems modeling and analysis. Due to the nature of molecular interactions, many biological processes can be conveniently described as a mixture of continuous and discrete phenomena employing SHS models. With the advancement of SHS theory, it is expected that insights can be obtained about biological processes such as drug effects on gene regulation. Furthermore, combining with advanced experimental methods, in silico simulations using SHS modeling techniques can be carried out for massive and rapid verification or falsification of biological hypotheses. The hope is to substitute costly and time-consuming in vitro or in vivo experiments or provide guidance for those experiments and generate better hypotheses.
Biology meets physics: Reductionism and multi-scale modeling of morphogenesis.

PubMed

Green, Sara; Batterman, Robert

2017-02-01

A common reductionist assumption is that macro-scale behaviors can be described "bottom-up" if only sufficient details about lower-scale processes are available. The view that an "ideal" or "fundamental" physics would be sufficient to explain all macro-scale phenomena has been met with criticism from philosophers of biology. Specifically, scholars have pointed to the impossibility of deducing biological explanations from physical ones, and to the irreducible nature of distinctively biological processes such as gene regulation and evolution. This paper takes a step back in asking whether bottom-up modeling is feasible even when modeling simple physical systems across scales. By comparing examples of multi-scale modeling in physics and biology, we argue that the "tyranny of scales" problem presents a challenge to reductive explanations in both physics and biology. The problem refers to the scale-dependency of physical and biological behaviors that forces researchers to combine different models relying on different scale-specific mathematical strategies and boundary conditions. Analyzing the ways in which different models are combined in multi-scale modeling also has implications for the relation between physics and biology. Contrary to the assumption that physical science approaches provide reductive explanations in biology, we exemplify how inputs from physics often reveal the importance of macro-scale models and explanations. We illustrate this through an examination of the role of biomechanical modeling in developmental biology. In such contexts, the relation between models at different scales and from different disciplines is neither reductive nor completely autonomous, but interdependent. Copyright © 2016 Elsevier Ltd. All rights reserved.
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.
Evolutionary biology through the lens of budding yeast comparative genomics.

PubMed

Marsit, Souhir; Leducq, Jean-Baptiste; Durand, Éléonore; Marchant, Axelle; Filteau, Marie; Landry, Christian R

2017-10-01

The budding yeast Saccharomyces cerevisiae is a highly advanced model system for studying genetics, cell biology and systems biology. Over the past decade, the application of high-throughput sequencing technologies to this species has contributed to this yeast also becoming an important model for evolutionary genomics. Indeed, comparative genomic analyses of laboratory, wild and domesticated yeast populations are providing unprecedented detail about many of the processes that govern evolution, including long-term processes, such as reproductive isolation and speciation, and short-term processes, such as adaptation to natural and domestication-related environments.
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
Digital Learning Material for Model Building in Molecular Biology

ERIC Educational Resources Information Center

Aegerter-Wilmsen, Tinri; Janssen, Fred; Hartog, Rob; Bisseling, Ton

2005-01-01

Building models to describe processes forms an essential part of molecular biology research. However, in molecular biology curricula little attention is generally being paid to the development of this skill. In order to provide students the opportunity to improve their model building skills, we decided to develop a number of digital cases about…
Cancer systems biology: signal processing for cancer research

PubMed Central

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

2011-01-01

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

NASA Astrophysics Data System (ADS)

Garvin-Doxas, K.; Klymkowsky, M.

2008-12-01

While researching the Biology Concept Inventory (BCI) we found that a wide class of student difficulties in genetics and molecular biology can be traced to deep-seated misconceptions about random processes and molecular interactions. Students believe that random processes are inefficient, while biological systems are very efficient, and 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 (natural selection in genetics, or density gradients in molecular biology) with the process only taking place when the driver is present. The concept of underlying random processes that are taking place all the time giving rise to emergent behaviour is almost totally absent. Even students who have advanced or college physics, and can discuss diffusion correctly in that context, cannot make the transfer to biological processes. Furthermore, their understanding of molecular interactions is purely geometric, with a lock-and-key model (rather than an energy minimization model) that does not allow for the survival of slight variations of the "correct" molecule. Together with the dominant misconception about random processes, this results in a strong conceptual barrier in understanding evolutionary processes, and can frustrate the success of education programs.
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.
Mathematical manipulative models: in defense of "beanbag biology".

PubMed

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

2010-01-01

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

PubMed Central

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

2015-01-01

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

PubMed

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

2015-01-01

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

PubMed

Johnson, Colin G; Goldman, Jacki P; Gullick, William J

2004-11-01

The aim of this paper is to give an overview of computer modelling and simulation in cellular biology, in particular as applied to complex biochemical processes within the cell. This is illustrated by the use of the techniques of object-oriented modelling, where the computer is used to construct abstractions of objects in the domain being modelled, and these objects then interact within the computer to simulate the system and allow emergent properties to be observed. The paper also discusses the role of computer simulation in understanding complexity in biological systems, and the kinds of information which can be obtained about biology via simulation.
Linking Adverse Outcome Pathways to Dynamic Energy Budgets: A Conceptual Model

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

Murphy, Cheryl; Nisbet, Roger; Antczak, Philipp

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

NASA Astrophysics Data System (ADS)

Shah, Samir; Levine, Martin D.

1994-06-01

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

PubMed Central

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-01-01

Background Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. Results We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. Conclusion By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions. PMID:18570683
Systematic reconstruction of TRANSPATH data into cell system markup language.

PubMed

Nagasaki, Masao; Saito, Ayumu; Li, Chen; Jeong, Euna; Miyano, Satoru

2008-06-23

Many biological repositories store information based on experimental study of the biological processes within a cell, such as protein-protein interactions, metabolic pathways, signal transduction pathways, or regulations of transcription factors and miRNA. Unfortunately, it is difficult to directly use such information when generating simulation-based models. Thus, modeling rules for encoding biological knowledge into system-dynamics-oriented standardized formats would be very useful for fully understanding cellular dynamics at the system level. We selected the TRANSPATH database, a manually curated high-quality pathway database, which provides a plentiful source of cellular events in humans, mice, and rats, collected from over 31,500 publications. In this work, we have developed 16 modeling rules based on hybrid functional Petri net with extension (HFPNe), which is suitable for graphical representing and simulating biological processes. In the modeling rules, each Petri net element is incorporated with Cell System Ontology to enable semantic interoperability of models. As a formal ontology for biological pathway modeling with dynamics, CSO also defines biological terminology and corresponding icons. By combining HFPNe with the CSO features, it is possible to make TRANSPATH data to simulation-based and semantically valid models. The results are encoded into a biological pathway format, Cell System Markup Language (CSML), which eases the exchange and integration of biological data and models. By using the 16 modeling rules, 97% of the reactions in TRANSPATH are converted into simulation-based models represented in CSML. This reconstruction demonstrates that it is possible to use our rules to generate quantitative models from static pathway descriptions.
[Numerical simulation and operation optimization of biological filter].

PubMed

Zou, Zong-Sen; Shi, Han-Chang; Chen, Xiang-Qiang; Xie, Xiao-Qing

2014-12-01

BioWin software and two sensitivity analysis methods were used to simulate the Denitrification Biological Filter (DNBF) + Biological Aerated Filter (BAF) process in Yuandang Wastewater Treatment Plant. Based on the BioWin model of DNBF + BAF process, the operation data of September 2013 were used for sensitivity analysis and model calibration, and the operation data of October 2013 were used for model validation. The results indicated that the calibrated model could accurately simulate practical DNBF + BAF processes, and the most sensitive parameters were the parameters related to biofilm, OHOs and aeration. After the validation and calibration of model, it was used for process optimization with simulating operation results under different conditions. The results showed that, the best operation condition for discharge standard B was: reflux ratio = 50%, ceasing methanol addition, influent C/N = 4.43; while the best operation condition for discharge standard A was: reflux ratio = 50%, influent COD = 155 mg x L(-1) after methanol addition, influent C/N = 5.10.
Biological and related chemical research concerning subseabed disposal of high level nuclear waste

NASA Astrophysics Data System (ADS)

Mullin, M. M.; Gomez, L. S.

1981-10-01

This report contains: recommendations (research on radionuclide movement processes, research on radionuclide transport processes, administration and policy); abstracts of plenary talks (Large-Scale Distributions of Deep-Sea Benthic Organisms, Transfer Processes Between Water Column and Benthos, Particle Reworking and Biogeochemistry of Sediments, and radioecological Aspects of Deep-Sea Waste Disposal of Radionuclides. Summaries of subgroup discussions (geochemistry and microbiology, benthic biology, pelagic biology, radioecology); and appendices (model of physical biological transfers, and participants and institutional affiliations) are also presented.
Modeling nitrous oxide production during biological nitrogen removal via nitrification and denitrification: extensions to the general ASM models.

PubMed

Ni, Bing-Jie; Ruscalleda, Maël; Pellicer-Nàcher, Carles; Smets, Barth F

2011-09-15

Nitrous oxide (N(2)O) can be formed during biological nitrogen (N) removal processes. In this work, a mathematical model is developed that describes N(2)O production and consumption during activated sludge nitrification and denitrification. The well-known ASM process models are extended to capture N(2)O dynamics during both nitrification and denitrification in biological N removal. Six additional processes and three additional reactants, all involved in known biochemical reactions, have been added. The validity and applicability of the model is demonstrated by comparing simulations with experimental data on N(2)O production from four different mixed culture nitrification and denitrification reactor study reports. Modeling results confirm that hydroxylamine oxidation by ammonium oxidizers (AOB) occurs 10 times slower when NO(2)(-) participates as final electron acceptor compared to the oxic pathway. Among the four denitrification steps, the last one (N(2)O reduction to N(2)) seems to be inhibited first when O(2) is present. Overall, N(2)O production can account for 0.1-25% of the consumed N in different nitrification and denitrification systems, which can be well simulated by the proposed model. In conclusion, we provide a modeling structure, which adequately captures N(2)O dynamics in autotrophic nitrification and heterotrophic denitrification driven biological N removal processes and which can form the basis for ongoing refinements.

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.
Integrating interactive computational modeling in biology curricula.

PubMed

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

2015-03-01

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

Treesearch

Wen J. Wang; Hong S. He; Frank R. Thompson; Martin A. Spetich; Jacob S. Fraser

2018-01-01

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are notwell represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts.We investigate how species biological...
Findings

MedlinePlus

... Issue All Issues Explore Findings by Topic Cell Biology Cellular Structures, Functions, Processes, Imaging, Stress Response Chemistry ... Glycobiology, Synthesis, Natural Products, Chemical Reactions Computers in Biology Bioinformatics, Modeling, Systems Biology, Data Visualization Diseases Cancer, ...
Building a Model of Employee Training through Holistic Analysis of Biological, Psychological, and Sociocultural Factors

ERIC Educational Resources Information Center

Schenck, Andrew

2015-01-01

While theories of adult learning and motivation are often framed as being either biological, psychological, or sociocultural, they represent a more complex, integral process. To gain a more holistic perspective of this process, a study was designed to concurrently investigate relationships between a biological factor (age), psychological factors…
A Gambler's Model of Natural Selection.

ERIC Educational Resources Information Center

Nolan, Michael J.; Ostrovsky, David S.

1996-01-01

Presents an activity that highlights the mechanism and power of natural selection. Allows students to think in terms of modeling a biological process and instills an appreciation for a mathematical approach to biological problems. (JRH)
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.
Change Is Good: Variations in Common Biological Mechanisms in the Epsilonproteobacterial Genera Campylobacter and Helicobacter

PubMed Central

Gilbreath, Jeremy J.; Cody, William L.; Merrell, D. Scott; Hendrixson, David R.

2011-01-01

Summary: Microbial evolution and subsequent species diversification enable bacterial organisms to perform common biological processes by a variety of means. The epsilonproteobacteria are a diverse class of prokaryotes that thrive in diverse habitats. Many of these environmental niches are labeled as extreme, whereas other niches include various sites within human, animal, and insect hosts. Some epsilonproteobacteria, such as Campylobacter jejuni and Helicobacter pylori, are common pathogens of humans that inhabit specific regions of the gastrointestinal tract. As such, the biological processes of pathogenic Campylobacter and Helicobacter spp. are often modeled after those of common enteric pathogens such as Salmonella spp. and Escherichia coli. While many exquisite biological mechanisms involving biochemical processes, genetic regulatory pathways, and pathogenesis of disease have been elucidated from studies of Salmonella spp. and E. coli, these paradigms often do not apply to the same processes in the epsilonproteobacteria. Instead, these bacteria often display extensive variation in common biological mechanisms relative to those of other prototypical bacteria. In this review, five biological processes of commonly studied model bacterial species are compared to those of the epsilonproteobacteria C. jejuni and H. pylori. Distinct differences in the processes of flagellar biosynthesis, DNA uptake and recombination, iron homeostasis, interaction with epithelial cells, and protein glycosylation are highlighted. Collectively, these studies support a broader view of the vast repertoire of biological mechanisms employed by bacteria and suggest that future studies of the epsilonproteobacteria will continue to provide novel and interesting information regarding prokaryotic cellular biology. PMID:21372321
Change is good: variations in common biological mechanisms in the epsilonproteobacterial genera Campylobacter and Helicobacter.

PubMed

Gilbreath, Jeremy J; Cody, William L; Merrell, D Scott; Hendrixson, David R

2011-03-01

Microbial evolution and subsequent species diversification enable bacterial organisms to perform common biological processes by a variety of means. The epsilonproteobacteria are a diverse class of prokaryotes that thrive in diverse habitats. Many of these environmental niches are labeled as extreme, whereas other niches include various sites within human, animal, and insect hosts. Some epsilonproteobacteria, such as Campylobacter jejuni and Helicobacter pylori, are common pathogens of humans that inhabit specific regions of the gastrointestinal tract. As such, the biological processes of pathogenic Campylobacter and Helicobacter spp. are often modeled after those of common enteric pathogens such as Salmonella spp. and Escherichia coli. While many exquisite biological mechanisms involving biochemical processes, genetic regulatory pathways, and pathogenesis of disease have been elucidated from studies of Salmonella spp. and E. coli, these paradigms often do not apply to the same processes in the epsilonproteobacteria. Instead, these bacteria often display extensive variation in common biological mechanisms relative to those of other prototypical bacteria. In this review, five biological processes of commonly studied model bacterial species are compared to those of the epsilonproteobacteria C. jejuni and H. pylori. Distinct differences in the processes of flagellar biosynthesis, DNA uptake and recombination, iron homeostasis, interaction with epithelial cells, and protein glycosylation are highlighted. Collectively, these studies support a broader view of the vast repertoire of biological mechanisms employed by bacteria and suggest that future studies of the epsilonproteobacteria will continue to provide novel and interesting information regarding prokaryotic cellular biology.
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.
A biologically inspired model of bat echolocation in a cluttered environment with inputs designed from field Recordings

NASA Astrophysics Data System (ADS)

Loncich, Kristen Teczar

Bat echolocation strategies and neural processing of acoustic information, with a focus on cluttered environments, is investigated in this study. How a bat processes the dense field of echoes received while navigating and foraging in the dark is not well understood. While several models have been developed to describe the mechanisms behind bat echolocation, most are based in mathematics rather than biology, and focus on either peripheral or neural processing---not exploring how these two levels of processing are vitally connected. Current echolocation models also do not use habitat specific acoustic input, or account for field observations of echolocation strategies. Here, a new approach to echolocation modeling is described capturing the full picture of echolocation from signal generation to a neural picture of the acoustic scene. A biologically inspired echolocation model is developed using field research measurements of the interpulse interval timing used by a frequency modulating (FM) bat in the wild, with a whole method approach to modeling echolocation including habitat specific acoustic inputs, a biologically accurate peripheral model of sound processing by the outer, middle, and inner ear, and finally a neural model incorporating established auditory pathways and neuron types with echolocation adaptations. Field recordings analyzed underscore bat sonar design differences observed in the laboratory and wild, and suggest a correlation between interpulse interval groupings and increased clutter. The scenario model provides habitat and behavior specific echoes and is a useful tool for both modeling and behavioral studies, and the peripheral and neural model show that spike-time information and echolocation specific neuron types can produce target localization in the midbrain.
Quantifying electron transfer reactions in biological systems: what interactions play the major role?

NASA Astrophysics Data System (ADS)

Sjulstok, Emil; Olsen, Jógvan Magnus Haugaard; Solov'Yov, Ilia A.

2015-12-01

Various biological processes involve the conversion of energy into forms that are usable for chemical transformations and are quantum mechanical in nature. Such processes involve light absorption, excited electronic states formation, excitation energy transfer, electrons and protons tunnelling which for example occur in photosynthesis, cellular respiration, DNA repair, and possibly magnetic field sensing. Quantum biology uses computation to model biological interactions in light of quantum mechanical effects and has primarily developed over the past decade as a result of convergence between quantum physics and biology. In this paper we consider electron transfer in biological processes, from a theoretical view-point; namely in terms of quantum mechanical and semi-classical models. We systematically characterize the interactions between the moving electron and its biological environment to deduce the driving force for the electron transfer reaction and to establish those interactions that play the major role in propelling the electron. The suggested approach is seen as a general recipe to treat electron transfer events in biological systems computationally, and we utilize it to describe specifically the electron transfer reactions in Arabidopsis thaliana cryptochrome-a signaling photoreceptor protein that became attractive recently due to its possible function as a biological magnetoreceptor.
Dynamic Biological Functioning Important for Simulating and Stabilizing Ocean Biogeochemistry

NASA Astrophysics Data System (ADS)

Buchanan, P. J.; Matear, R. J.; Chase, Z.; Phipps, S. J.; Bindoff, N. L.

2018-04-01

The biogeochemistry of the ocean exerts a strong influence on the climate by modulating atmospheric greenhouse gases. In turn, ocean biogeochemistry depends on numerous physical and biological processes that change over space and time. Accurately simulating these processes is fundamental for accurately simulating the ocean's role within the climate. However, our simulation of these processes is often simplistic, despite a growing understanding of underlying biological dynamics. Here we explore how new parameterizations of biological processes affect simulated biogeochemical properties in a global ocean model. We combine 6 different physical realizations with 6 different biogeochemical parameterizations (36 unique ocean states). The biogeochemical parameterizations, all previously published, aim to more accurately represent the response of ocean biology to changing physical conditions. We make three major findings. First, oxygen, carbon, alkalinity, and phosphate fields are more sensitive to changes in the ocean's physical state. Only nitrate is more sensitive to changes in biological processes, and we suggest that assessment protocols for ocean biogeochemical models formally include the marine nitrogen cycle to assess their performance. Second, we show that dynamic variations in the production, remineralization, and stoichiometry of organic matter in response to changing environmental conditions benefit the simulation of ocean biogeochemistry. Third, dynamic biological functioning reduces the sensitivity of biogeochemical properties to physical change. Carbon and nitrogen inventories were 50% and 20% less sensitive to physical changes, respectively, in simulations that incorporated dynamic biological functioning. These results highlight the importance of a dynamic biology for ocean properties and climate.
Role of Water in Proton-Hydroxide Conductance Across Model and Biological Membranes

DTIC Science & Technology

1989-09-30

of water in proton-hydroxide conductance across model and biological membranes 12. PERSONAL AUTHOR(S) Deamer, David W. 1 a. TYPE OF REPORT 13b. TIME...identify by block number) The goal of this research is to understand the mechanism of proton translocation in model and biological membranes. The...which conducts protons through hydrogen bonded water, thereby providing an important model for investigating such processes. The Fo subunit of
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
Atypical biological motion kinematics are represented by complementary lower-level and top-down processes during imitation learning.

PubMed

Hayes, Spencer J; Dutoy, Chris A; Elliott, Digby; Gowen, Emma; Bennett, Simon J

2016-01-01

Learning a novel movement requires a new set of kinematics to be represented by the sensorimotor system. This is often accomplished through imitation learning where lower-level sensorimotor processes are suggested to represent the biological motion kinematics associated with an observed movement. Top-down factors have the potential to influence this process based on the social context, attention and salience, and the goal of the movement. In order to further examine the potential interaction between lower-level and top-down processes in imitation learning, the aim of this study was to systematically control the mediating effects during an imitation of biological motion protocol. In this protocol, we used non-human agent models that displayed different novel atypical biological motion kinematics, as well as a control model that displayed constant velocity. Importantly the three models had the same movement amplitude and movement time. Also, the motion kinematics were displayed in the presence, or absence, of end-state-targets. Kinematic analyses showed atypical biological motion kinematics were imitated, and that this performance was different from the constant velocity control condition. Although the imitation of atypical biological motion kinematics was not modulated by the end-state-targets, movement time was more accurate in the absence, compared to the presence, of an end-state-target. The fact that end-state targets modulated movement time accuracy, but not biological motion kinematics, indicates imitation learning involves top-down attentional, and lower-level sensorimotor systems, which operate as complementary processes mediated by the environmental context. Copyright © 2015 Elsevier B.V. All rights reserved.
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…
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.

High performance hybrid functional Petri net simulations of biological pathway models on CUDA.

PubMed

Chalkidis, Georgios; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells.
Genome-scale biological models for industrial microbial systems.

PubMed

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.
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.
Towards physical principles of biological evolution

NASA Astrophysics Data System (ADS)

Katsnelson, Mikhail I.; Wolf, Yuri I.; Koonin, Eugene V.

2018-03-01

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in ‘emergent phenomena’. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice for adequate modeling of the biological level of complexity, and new developments within physics itself are likely to be required.
WE-DE-202-00: Connecting Radiation Physics with Computational Biology

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

NONE

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less
3D molecular models of whole HIV-1 virions generated with cellPACK

PubMed Central

Goodsell, David S.; Autin, Ludovic; Forli, Stefano; Sanner, Michel F.; Olson, Arthur J.

2014-01-01

As knowledge of individual biological processes grows, it becomes increasingly useful to frame new findings within their larger biological contexts in order to generate new systems-scale hypotheses. This report highlights two major iterations of a whole virus model of HIV-1, generated with the cellPACK software. cellPACK integrates structural and systems biology data with packing algorithms to assemble comprehensive 3D models of cell-scale structures in molecular detail. This report describes the biological data, modeling parameters and cellPACK methods used to specify and construct editable models for HIV-1. Anticipating that cellPACK interfaces under development will enable researchers from diverse backgrounds to critique and improve the biological models, we discuss how cellPACK can be used as a framework to unify different types of data across all scales of biology. PMID:25253262
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.
Connecting Biology and Mathematics: First Prepare the Teachers

PubMed Central

2010-01-01

Developing the connection between biology and mathematics is one of the most important ways to shift the paradigms of both established science disciplines. However, adding some mathematic content to biology or biology content to mathematics is not enough but must be accompanied by development of suitable pedagogical models. I propose a model of pedagogical mathematical biological content knowledge as a feasible starting point for connecting biology and mathematics in schools and universities. The process of connecting these disciplines should start as early as possible in the educational process, in order to produce prepared minds that will be able to combine both disciplines at graduate and postgraduate levels of study. Because teachers are a crucial factor in introducing innovations in education, the first step toward such a goal should be the education of prospective and practicing elementary and secondary school teachers. PMID:20810951
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
Process models as tools in forestry research and management

Treesearch

Kurt Johnsen; Lisa Samuelson; Robert Teskey; Steve McNulty; Tom Fox

2001-01-01

Forest process models are mathematical representations of biological systems that incorporate our understanding of physiological and ecological mechanisms into predictive algorithms. These models were originally designed and used for research purposes, but are being developed for use in practical forest management. Process models designed for research...
Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

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

2014-01-01

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

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

Phelps, Michael E

2012-12-14

The objectives of the project are the development of new Positron Emission Tomography (PET) imaging instrumentation, chemistry technology platforms and new molecular imaging probes to examine the transformations from normal cellular and biological processes to those of disease in pre-clinical animal models. These technology platforms and imaging probes provide the means to: 1. Study the biology of disease using pre-clinical mouse models and cells. 2. Develop molecular imaging probes for imaging assays of proteins in pre-clinical models. 3. Develop imaging assays in pre-clinical models to provide to other scientists the means to guide and improve the processes for discovering newmore » drugs. 4. Develop imaging assays in pre-clinical models for others to use in judging the impact of drugs on the biology of disease.« less
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.
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.
Disposal of Industrial and Domestic Wastes: Land and Sea Alternatives.

DTIC Science & Technology

1984-01-01

square kilometers. The rough classification of physical, chemical , and biological processes into near field versus far field and short term versus...contaminants by sedimentation is slowed. Chemical Precipitation and Dissolution During the few minutes of the initial dilution of a buoyant plume ...model. Time and space scales of physical, chemical , and biological processes often provide natural divisions in such modeling. Near -field and far-field
Colored petri net modeling of small interfering RNA-mediated messenger RNA degradation.

PubMed

Nickaeen, Niloofar; Moein, Shiva; Heidary, Zarifeh; Ghaisari, Jafar

2016-01-01

Mathematical modeling of biological systems is an attractive way for studying complex biological systems and their behaviors. Petri Nets, due to their ability to model systems with various levels of qualitative information, have been wildly used in modeling biological systems in which enough qualitative data may not be at disposal. These nets have been used to answer questions regarding the dynamics of different cell behaviors including the translation process. In one stage of the translation process, the RNA sequence may be degraded. In the process of degradation of RNA sequence, small-noncoding RNA molecules known as small interfering RNA (siRNA) match the target RNA sequence. As a result of this matching, the target RNA sequence is destroyed. In this context, the process of matching and destruction is modeled using Colored Petri Nets (CPNs). The model is constructed using CPNs which allow tokens to have a value or type on them. Thus, CPN is a suitable tool to model string structures in which each element of the string has a different type. Using CPNs, long RNA, and siRNA strings are modeled with a finite set of colors. The model is simulated via CPN Tools. A CPN model of the matching between RNA and siRNA strings is constructed in CPN Tools environment. In previous studies, a network of stoichiometric equations was modeled. However, in this particular study, we modeled the mechanism behind the silencing process. Modeling this kind of mechanisms provides us with a tool to examine the effects of different factors such as mutation or drugs on the process.
Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm.

PubMed

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

2012-08-29

Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time. There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature. Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions. Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.
WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

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

Stewart, R.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

McMahon, S.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less
WE-DE-202-01: Connecting Nanoscale Physics to Initial DNA Damage Through Track Structure Simulations

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

Schuemann, J.

Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying biological processes are too complex for a mechanistic approach. Can computer simulations be used to guide future biological research? We will debate the feasibility of explaining biology from a physicists’ perspective. Learning Objectives: Understand the potential applications and limitations of computational methods for dose-response modeling at the molecular, cellular and tissue levels Learn about mechanism of action underlying the induction, repair and biological processing of damage to DNA and other constituents Understand how effects and processes at one biological scale impact on biological processes and outcomes on other scales J. Schuemann, NCI/NIH grantsS. McMahon, Funding: European Commission FP7 (grant EC FP7 MC-IOF-623630)« less

Computing biological functions using BioΨ, a formal description of biological processes based on elementary bricks of actions

PubMed Central

Pérès, Sabine; Felicori, Liza; Rialle, Stéphanie; Jobard, Elodie; Molina, Franck

2010-01-01

Motivation: In the available databases, biological processes are described from molecular and cellular points of view, but these descriptions are represented with text annotations that make it difficult to handle them for computation. Consequently, there is an obvious need for formal descriptions of biological processes. Results: We present a formalism that uses the BioΨ concepts to model biological processes from molecular details to networks. This computational approach, based on elementary bricks of actions, allows us to calculate on biological functions (e.g. process comparison, mapping structure–function relationships, etc.). We illustrate its application with two examples: the functional comparison of proteases and the functional description of the glycolysis network. This computational approach is compatible with detailed biological knowledge and can be applied to different kinds of systems of simulation. Availability: www.sysdiag.cnrs.fr/publications/supplementary-materials/BioPsi_Manager/ Contact: sabine.peres@sysdiag.cnrs.fr; franck.molina@sysdiag.cnrs.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20448138
Thermal injury models for optical treatment of biological tissues: a comparative study.

PubMed

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

2010-01-01

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

PubMed

Tsurusaki, Takashi; Sakakibara, Hiroyuki; Aoshima, Yoshiki; Yamazaki, Shunsuke; Sakono, Masanobu; Shimoi, Kayoko

2013-05-01

In the past few decades, many types of functional factors have been identified in dietary foods; for example, flavonoids are major groups widely distributed in the plant kingdom. However, the absorption rates of the functional food factors are usually low, and many of these are difficult to be absorbed in the intact forms because of metabolization by biological processes during absorption. To gain adequate beneficial effects, it is therefore mandatory to know whether functional food factors are absorbed in sufficient quantity, and then reach target organs while maintaining beneficial effects. These are the reasons why the bioavailability of functional food factors has been well investigated using rodent models. Recently, many of the biological processes have been reported to follow diurnal rhythms recurring every 24 h. Therefore, absorption and metabolism of functional food factors influenced by the biological processes may vary with time of day. Consequently, the evaluation of the bioavailability of functional food factors using rodent models should take into consideration the timing of consumption. In this review, we provide a perspective overview of the diurnal rhythm of biological processes involved in the bioavailability of functional food factors, particularly flavonoids.
Evaluation of the energy efficiency of enzyme fermentation by mechanistic modeling.

PubMed

Albaek, Mads O; Gernaey, Krist V; Hansen, Morten S; Stocks, Stuart M

2012-04-01

Modeling biotechnological processes is key to obtaining increased productivity and efficiency. Particularly crucial to successful modeling of such systems is the coupling of the physical transport phenomena and the biological activity in one model. We have applied a model for the expression of cellulosic enzymes by the filamentous fungus Trichoderma reesei and found excellent agreement with experimental data. The most influential factor was demonstrated to be viscosity and its influence on mass transfer. Not surprisingly, the biological model is also shown to have high influence on the model prediction. At different rates of agitation and aeration as well as headspace pressure, we can predict the energy efficiency of oxygen transfer, a key process parameter for economical production of industrial enzymes. An inverse relationship between the productivity and energy efficiency of the process was found. This modeling approach can be used by manufacturers to evaluate the enzyme fermentation process for a range of different process conditions with regard to energy efficiency. Copyright © 2011 Wiley Periodicals, Inc.
Using stochastic models to incorporate spatial and temporal variability [Exercise 14

Treesearch

Carolyn Hull Sieg; Rudy M. King; Fred Van Dyke

2003-01-01

To this point, our analysis of population processes and viability in the western prairie fringed orchid has used only deterministic models. In this exercise, we conduct a similar analysis, using a stochastic model instead. This distinction is of great importance to population biology in general and to conservation biology in particular. In deterministic models,...
An Introduction to Biological Modeling Using Coin Flips to Predict the Outcome of a Diffusion Activity

ERIC Educational Resources Information Center

Butcher, Greg Q.; Rodriguez, Juan; Chirhart, Scott; Messina, Troy C.

2016-01-01

In order to increase students' awareness for and comfort with mathematical modeling of biological processes, and increase their understanding of diffusion, the following lab was developed for use in 100-level, majors/non-majors biology and neuroscience courses. The activity begins with generation of a data set that uses coin-flips to replicate…
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.
Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

PubMed

Endendijk, Joyce J; Groeneveld, Marleen G; Mesman, Judi

2018-05-01

This article reviews and integrates research on gender-related biological, cognitive, and social processes that take place in or between family members, resulting in a newly developed gendered family process (GFP) model. The GFP model serves as a guiding framework for research on gender in the family context, calling for the integration of biological, social, and cognitive factors. Biological factors in the model are prenatal, postnatal, and pubertal androgen levels of children and parents, and genetic effects on parent and child gendered behavior. Social factors are family sex composition (i.e., parent sex, sexual orientation, marriage status, sibling sex composition) and parental gender socialization, such as modeling, gender-differentiated parenting, and gender talk. Cognitive factors are implicit and explicit gender-role cognitions of parents and children. Our review and the GFP model confirm that gender is an important organizer of family processes, but also highlight that much is still unclear about the mechanisms underlying gender-related processes within the family context. Therefore, we stress the need for (1) longitudinal studies that take into account the complex bidirectional relationship between parent and child gendered behavior and cognitions, in which within-family comparisons (comparing behavior of parents toward a boy and a girl in the same family) are made instead of between-family comparisons (comparing parenting between all-boy families and all-girl families, or between mixed-gender families and same-gender families), (2) experimental studies on the influence of testosterone on human gender development, (3) studies examining the interplay between biology with gender socialization and gender-role cognitions in humans.
The NASA Space Radiobiology Risk Assessment Project

NASA Astrophysics Data System (ADS)

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

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

PubMed Central

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

2015-01-01

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

PubMed

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

2014-01-01

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

PubMed Central

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

2014-01-01

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

NASA Astrophysics Data System (ADS)

Fankell, Douglas P.

This work provides modeling efforts and supplemental experimental work performed towards the ultimate goal of modeling heat transfer, mass transfer, and deformation occurring in biological tissue, in particular during arterial fusion and cutting. Developing accurate models of these processes accomplishes two goals. First, accurate models would enable engineers to design devices to be safer and less expensive. Second, the mechanisms behind tissue fusion and cutting are widely unknown; models with the ability to accurately predict physical phenomena occurring in the tissue will allow for insight into the underlying mechanisms of the processes. This work presents three aims and the efforts in achieving them, leading to an accurate model of tissue fusion and more broadly the thermo-poromechanics (TPM) occurring within biological tissue. Chapters 1 and 2 provide the motivation for developing accurate TPM models of biological tissue and an overview of previous modeling efforts. In Chapter 3, a coupled thermo-structural finite element (FE) model with the ability to predict arterial cutting is offered. From the work presented in Chapter 3, it became obvious a more detailed model was needed. Chapter 4 meets this need by presenting small strain TPM theory and its implementation in an FE code. The model is then used to simulate thermal tissue fusion. These simulations show the model's promise in predicting the water content and temperature of arterial wall tissue during the fusion process, but it is limited by its small deformation assumptions. Chapters 5-7 attempt to address this limitation by developing and implementing a large deformation TPM FE model. Chapters 5, 6, and 7 present a thermodynamically consistent, large deformation TPM FE model and its ability to simulate tissue fusion. Ultimately, this work provides several methods of simulating arterial tissue fusion and the thermo-poromechanics of biological tissue. It is the first work, to the author's knowledge, to simulate the fully coupled TPM of biological tissue and the first to present a fully coupled large deformation TPM FE model. In doing so, a stepping stone for more advanced modeling of biological tissue has been laid.
Systems modelling methodology for the analysis of apoptosis signal transduction and cell death decisions.

PubMed

Rehm, Markus; Prehn, Jochen H M

2013-06-01

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

PubMed Central

Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

2013-01-01

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy–industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies. PMID:20349528
Process and metaphors in the evolutionary paradigm

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

Ho, M.; Fox, S

1988-01-01

Presents thinking on the processes and interpretation of biological evolution, emphasizing the study of biological processes as they occur in living organisms and their communities, rather than through mechanical or statistical models. Contributors explore processes and metaphors in evolution, the origin of the genetic code, new genetic mechanisms and their implications for the formation of new species, panbiogeography, the active role of behavior in evolution, sociobiology, and more.
From Random Walks to Brownian Motion, from Diffusion to Entropy: Statistical Principles in Introductory Physics

NASA Astrophysics Data System (ADS)

Reeves, Mark

2014-03-01

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

The Feasibility of Systems Thinking in Biology Education

ERIC Educational Resources Information Center

Boersma, Kerst; Waarlo, Arend Jan; Klaassen, Kees

2011-01-01

Systems thinking in biology education is an up and coming research topic, as yet with contrasting feasibility claims. In biology education systems thinking can be understood as thinking backward and forward between concrete biological objects and processes and systems models representing systems theoretical characteristics. Some studies claim that…
A SYSTEMS BIOLOGY APPROACH TO DEVELOPMENTAL TOXICOLOGY

EPA Science Inventory

Abstract
Recent advances in developmental biology have yielded detailed models of gene regulatory networks (GRNs) involved in cell specification and other processes in embryonic differentiation. Such networks form the bedrock on which a systems biology approach to developme...
Biomimetic modelling.

PubMed Central

Vincent, Julian F V

2003-01-01

Biomimetics is seen as a path from biology to engineering. The only path from engineering to biology in current use is the application of engineering concepts and models to biological systems. However, there is another pathway: the verification of biological mechanisms by manufacture, leading to an iterative process between biology and engineering in which the new understanding that the engineering implementation of a biological system can bring is fed back into biology, allowing a more complete and certain understanding and the possibility of further revelations for application in engineering. This is a pathway as yet unformalized, and one that offers the possibility that engineers can also be scientists. PMID:14561351
Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

PubMed Central

2014-01-01

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility. PMID:25276860
Inter-species investigation of the mechano-regulation of bone healing: comparison of secondary bone healing in sheep and rat.

PubMed

Checa, Sara; Prendergast, Patrick J; Duda, Georg N

2011-04-29

Inter-species differences in regeneration exist in various levels. One aspect is the dynamics of bone regeneration and healing, e.g. small animals show a faster healing response when compared to large animals. Mechanical as well as biological factors are known to play a key role in the process. However, it remains so far unknown whether different animals follow at all comparable mechano-biological rules during tissue regeneration, and in particular during bone healing. In this study, we investigated whether differences observed in vivo in the dynamics of bone healing between rat and sheep are only due to differences in the animal size or whether these animals have a different mechano-biological response during the healing process. Histological sections from in vivo experiments were compared to in silico predictions of a mechano-biological computer model for the simulation of bone healing. Investigations showed that the healing processes in both animal models occur under significantly different levels of mechanical stimuli within the callus region, which could explain histological observations of early intramembranous ossification at the endosteal side. A species-specific adaptation of a mechano-biological model allowed a qualitative match of model predictions with histological observations. Specifically, when keeping cell activity processes at the same rate, the amount of tissue straining defining favorable mechanical conditions for the formation of bone had to be increased in the large animal model, with respect to the small animal, to achieve a qualitative agreement of model predictions with histological data. These findings illustrate that geometrical (size) differences alone cannot explain the distinctions seen in the histological appearance of secondary bone healing in sheep and rat. It can be stated that significant differences in the mechano-biological regulation of the healing process exist between these species. Future investigations should aim towards understanding whether these differences are due to differences in cell behavior, material properties of the newly formed tissues within the callus and/or differences in response to the mechanical environment. Copyright © 2011 Elsevier Ltd. All rights reserved.
Modeling stochasticity and robustness in gene regulatory networks.

PubMed

Garg, Abhishek; Mohanram, Kartik; Di Cara, Alessandro; De Micheli, Giovanni; Xenarios, Ioannis

2009-06-15

Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations. In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs. Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.
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.
Using Petri Net Tools to Study Properties and Dynamics of Biological Systems

PubMed Central

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

2005-01-01

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

PubMed Central

Somogyi, Endre; Glazier, James A.

2017-01-01

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

PubMed

Somogyi, Endre; Glazier, James A

2017-04-01

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

PubMed

Menshutkin, V V; Kazanskiĭ, A B; Levchenko, V F

2010-01-01

The history of rise and development of evolutionary methods in Saint Petersburg school of biological modelling is traced and analyzed. Some pioneering works in simulation of ecological and evolutionary processes, performed in St.-Petersburg school became an exemplary ones for many followers in Russia and abroad. The individual-based approach became the crucial point in the history of the school as an adequate instrument for construction of models of biological evolution. This approach is natural for simulation of the evolution of life-history parameters and adaptive processes in populations and communities. In some cases simulated evolutionary process was used for solving a reverse problem, i. e., for estimation of uncertain life-history parameters of population. Evolutionary computations is one more aspect of this approach application in great many fields. The problems and vistas of ecological and evolutionary modelling in general are discussed.
HOW CAN BIOLOGICALLY-BASED MODELING OF ARSENIC KINETICS AND DYNAMICS INFORM THE RISK ASSESSMENT PROCESS?

EPA Science Inventory

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

PubMed

Lorenz, Sönke; Diederichs, Elmar; Telgmann, Regina; Schütte, Christof

2007-06-01

In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics.
Network news: prime time for systems biology of the plant circadian clock.

PubMed

McClung, C Robertson; Gutiérrez, Rodrigo A

2010-12-01

Whole-transcriptome analyses have established that the plant circadian clock regulates virtually every plant biological process and most prominently hormonal and stress response pathways. Systems biology efforts have successfully modeled the plant central clock machinery and an iterative process of model refinement and experimental validation has contributed significantly to the current view of the central clock machinery. The challenge now is to connect this central clock to the output pathways for understanding how the plant circadian clock contributes to plant growth and fitness in a changing environment. Undoubtedly, systems approaches will be needed to integrate and model the vastly increased volume of experimental data in order to extract meaningful biological information. Thus, we have entered an era of systems modeling, experimental testing, and refinement. This approach, coupled with advances from the genetic and biochemical analyses of clock function, is accelerating our progress towards a comprehensive understanding of the plant circadian clock network. Copyright © 2010 Elsevier Ltd. All rights reserved.
Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Treesearch

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

2016-01-01

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

PubMed

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

2013-01-01

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

ERIC Educational Resources Information Center

Sitaraman, Ramakrishnan

2011-01-01

The study of model organisms is a powerful and proven experimental strategy for understanding biological processes. This paper describes an attempt to utilize advances in yeast molecular biology to enhance student understanding by presenting a more comprehensive view of several interconnected molecular processes in the overall functioning of an…
From biology to mathematical models and back: teaching modeling to biology students, and biology to math and engineering students.

PubMed

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

2010-01-01

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

EPA Science Inventory

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

PubMed Central

Yao, Lixia

2009-01-01

Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801

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

PubMed

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

2013-01-01

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

PubMed Central

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

2013-01-01

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

EPA Science Inventory

Predicting subsurface contaminant transport and transformation requires mathematical models based on a variety of physical, chemical, and biological processes. The mathematical model is an attempt to quantitatively describe observed processes in order to permit systematic forecas...
An instructional design process based on expert knowledge for teaching students how mechanisms are explained.

PubMed

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

2016-06-01

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

PubMed Central

Kendler, Kenneth S.

2009-01-01

How can we best develop explanatory models for psychiatric disorders? Because causal factors have an impact on psychiatric illness both at micro levels and macro levels, both within and outside of the individual, and involving processes best understood from biological, psychological, and sociocultural perspectives, traditional models of science that strive for single broadly applicable explanatory laws are ill suited for our field. Such models are based on the incorrect assumption that psychiatric illnesses can be understood from a single perspective. A more appropriate scientific model for psychiatry emphasizes the understanding of mechanisms, an approach that fits naturally with a multicausal framework and provides a realistic paradigm for scientific progress, that is, understanding mechanisms through decomposition and reassembly. Simple subunits of complicated mechanisms can be usefully studied in isolation. Reassembling these constituent parts into a functioning whole, which is straightforward for simple additive mechanisms, will be far more challenging in psychiatry where causal networks contain multiple nonlinear interactions and causal loops. Our field has long struggled with the interrelationship between biological and psychological explanatory perspectives. Building from the seminal work of the neuronal modeler and philosopher David Marr, the author suggests that biology will implement but not replace psychology within our explanatory systems. The iterative process of interactions between biology and psychology needed to achieve this implementation will deepen our understanding of both classes of processes. PMID:18483135
An instructional design process based on expert knowledge for teaching students how mechanisms are explained

PubMed Central

Anderson, Trevor R.; Pelaez, Nancy J.

2016-01-01

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

PubMed

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

2017-01-01

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

PubMed Central

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

2017-01-01

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

PubMed Central

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao

2016-01-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein–protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae. In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways in S. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. PMID:26476430
Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

PubMed

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

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.
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
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
Process-based modeling of silicate mineral weathering responses to increasing atmospheric CO2 and climate change

NASA Astrophysics Data System (ADS)

Banwart, Steven A.; Berg, Astrid; Beerling, David J.

2009-12-01

A mathematical model describes silicate mineral weathering processes in modern soils located in the boreal coniferous region of northern Europe. The process model results demonstrate a stabilizing biological feedback mechanism between atmospheric CO2 levels and silicate weathering rates as is generally postulated for atmospheric evolution. The process model feedback response agrees within a factor of 2 of that calculated by a weathering feedback function of the type generally employed in global geochemical carbon cycle models of the Earth's Phanerozoic CO2 history. Sensitivity analysis of parameter values in the process model provides insight into the key mechanisms that influence the strength of the biological feedback to weathering. First, the process model accounts for the alkalinity released by weathering, whereby its acceleration stabilizes pH at values that are higher than expected. Although the process model yields faster weathering with increasing temperature, because of activation energy effects on mineral dissolution kinetics at warmer temperature, the mineral dissolution rate laws utilized in the process model also result in lower dissolution rates at higher pH values. Hence, as dissolution rates increase under warmer conditions, more alkalinity is released by the weathering reaction, helping maintain higher pH values thus stabilizing the weathering rate. Second, the process model yields a relatively low sensitivity of soil pH to increasing plant productivity. This is due to more rapid decomposition of dissolved organic carbon (DOC) under warmer conditions. Because DOC fluxes strongly influence the soil water proton balance and pH, this increased decomposition rate dampens the feedback between productivity and weathering. The process model is most sensitive to parameters reflecting soil structure; depth, porosity, and water content. This suggests that the role of biota to influence these characteristics of the weathering profile is as important, if not more important, than the role of biota to influence mineral dissolution rates through changes in soil water chemistry. This process-modeling approach to quantify the biological weathering feedback to atmospheric CO2 demonstrates the potential for a far more mechanistic description of weathering feedback in simulations of the global geochemical carbon cycle.
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
Peer Review for EPA’s Biologically Based Dose-Response (BBDR) Model for Perchlorate

EPA Science Inventory

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

PubMed

Eddy, James A; Funk, Cory C; Price, Nathan D

2015-08-01

In the shared pursuit of elucidating detailed mechanisms of cell function, systems biology presents a natural complement to ongoing efforts in cell biology. Systems biology aims to characterize biological systems through integrated and quantitative modeling of cellular information. The process of model building and analysis provides value through synthesizing and cataloging information about cells and molecules, predicting mechanisms and identifying generalizable themes, generating hypotheses and guiding experimental design, and highlighting knowledge gaps and refining understanding. In turn, incorporating domain expertise and experimental data is crucial for building towards whole cell models. An iterative cycle of interaction between cell and systems biologists advances the goals of both fields and establishes a framework for mechanistic understanding of the genome-to-phenome relationship. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.
Parameter Estimation and Model Selection in Computational Biology

PubMed Central

Lillacci, Gabriele; Khammash, Mustafa

2010-01-01

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

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Hicks, Bruce B.

2016-03-01

Just as the exchange of heat, moisture and momentum between the Earth's surface and the atmosphere are critical components of meteorological and climate models, the surface-atmosphere exchange of many trace gases and aerosol particles is a vitally important process in air quality (AQ) models. Current state-of-the-art AQ models treat the emission and deposition of most gases and particles as separate model parameterizations, even though evidence has accumulated over time that the emission and deposition processes of many constituents are often two sides of the same coin, with the upward (emission) or downward (deposition) flux over a landscape depending on a range of environmental, seasonal and biological variables. In this note we argue that the time has come to integrate the treatment of these processes in AQ models to provide biological, physical and chemical consistency and improved predictions of trace gases and particles.
A systematic petri net approach for multiple-scale modeling and simulation of biochemical processes.

PubMed

Chen, Ming; Hu, Minjie; Hofestädt, Ralf

2011-06-01

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

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

PubMed Central

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

2015-01-01

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

PubMed Central

Navlakha, Saket; Bar-Joseph, Ziv

2011-01-01

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future. PMID:22068329
Modelling biological Cr(VI) reduction in aquifer microcosm column systems.

PubMed

Molokwane, Pulane E; Chirwa, Evans M N

2013-01-01

Several chrome processing facilities in South Africa release hexavalent chromium (Cr(VI)) into groundwater resources. Pump-and-treat remediation processes have been implemented at some of the sites but have not been successful in reducing contamination levels. The current study is aimed at developing an environmentally friendly, cost-effective and self-sustained biological method to curb the spread of chromium at the contaminated sites. An indigenous Cr(VI)-reducing mixed culture of bacteria was demonstrated to reduce high levels of Cr(VI) in laboratory samples. The effect of Cr(VI) on the removal rate was evaluated at concentrations up to 400 mg/L. Following the detailed evaluation of fundamental processes for biological Cr(VI) reduction, a predictive model for Cr(VI) breakthrough through aquifer microcosm reactors was developed. The reaction rate in batch followed non-competitive rate kinetics with a Cr(VI) inhibition threshold concentration of approximately 99 mg/L. This study evaluates the application of the kinetic parameters determined in the batch reactors to the continuous flow process. The model developed from advection-reaction rate kinetics in a porous media fitted best the effluent Cr(VI) concentration. The model was also used to elucidate the logistic nature of biomass growth in the reactor systems.
Reconciling transport models across scales: The role of volume exclusion

NASA Astrophysics Data System (ADS)

Taylor, P. R.; Yates, C. A.; Simpson, M. J.; Baker, R. E.

2015-10-01

Diffusive transport is a universal phenomenon, throughout both biological and physical sciences, and models of diffusion are routinely used to interrogate diffusion-driven processes. However, most models neglect to take into account the role of volume exclusion, which can significantly alter diffusive transport, particularly within biological systems where the diffusing particles might occupy a significant fraction of the available space. In this work we use a random walk approach to provide a means to reconcile models that incorporate crowding effects on different spatial scales. Our work demonstrates that coarse-grained models incorporating simplified descriptions of excluded volume can be used in many circumstances, but that care must be taken in pushing the coarse-graining process too far.
Adapting viral safety assurance strategies to continuous processing of biological products.

PubMed

Johnson, Sarah A; Brown, Matthew R; Lute, Scott C; Brorson, Kurt A

2017-01-01

There has been a recent drive in commercial large-scale production of biotechnology products to convert current batch mode processing to continuous processing manufacturing. There have been reports of model systems capable of adapting and linking upstream and downstream technologies into a continuous manufacturing pipeline. However, in many of these proposed continuous processing model systems, viral safety has not been comprehensively addressed. Viral safety and detection is a highly important and often expensive regulatory requirement for any new biological product. To ensure success in the adaption of continuous processing to large-scale production, there is a need to consider the development of approaches that allow for seamless incorporation of viral testing and clearance/inactivation methods. In this review, we outline potential strategies to apply current viral testing and clearance/inactivation technologies to continuous processing, as well as modifications of existing unit operations to ensure the successful integration of viral clearance into the continuous processing of biological products. Biotechnol. Bioeng. 2017;114: 21-32. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
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.
Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation.

PubMed

Godec, Jernej; Tan, Yan; Liberzon, Arthur; Tamayo, Pablo; Bhattacharya, Sanchita; Butte, Atul J; Mesirov, Jill P; Haining, W Nicholas

2016-01-19

Gene-expression profiling has become a mainstay in immunology, but subtle changes in gene networks related to biological processes are hard to discern when comparing various datasets. For instance, conservation of the transcriptional response to sepsis in mouse models and human disease remains controversial. To improve transcriptional analysis in immunology, we created ImmuneSigDB: a manually annotated compendium of ∼5,000 gene-sets from diverse cell states, experimental manipulations, and genetic perturbations in immunology. Analysis using ImmuneSigDB identified signatures induced in activated myeloid cells and differentiating lymphocytes that were highly conserved between humans and mice. Sepsis triggered conserved patterns of gene expression in humans and mouse models. However, we also identified species-specific biological processes in the sepsis transcriptional response: although both species upregulated phagocytosis-related genes, a mitosis signature was specific to humans. ImmuneSigDB enables granular analysis of transcriptomic data to improve biological understanding of immune processes of the human and mouse immune systems. Copyright © 2016 Elsevier Inc. All rights reserved.
CFD simulation of fluid dynamic and biokinetic processes within activated sludge reactors under intermittent aeration regime.

PubMed

Sánchez, F; Rey, H; Viedma, A; Nicolás-Pérez, F; Kaiser, A S; Martínez, M

2018-08-01

Due to the aeration system, biological reactors are the most energy-consuming facilities of convectional WWTPs. Many biological reactors work under intermittent aeration regime; the optimization of the aeration process (air diffuser layout, air flow rate per diffuser, aeration length …) is necessary to ensure an efficient performance; satisfying the effluent requirements with the minimum energy consumption. This work develops a CFD modelling of an activated sludge reactor (ASR) which works under intermittent aeration regime. The model considers the fluid dynamic and biological processes within the ASR. The biological simulation, which is transient, takes into account the intermittent aeration regime. The CFD modelling is employed for the selection of the aeration system of an ASR. Two different aeration configurations are simulated. The model evaluates the aeration power consumption necessary to satisfy the effluent requirements. An improvement of 2.8% in terms of energy consumption is achieved by modifying the air diffuser layout. An analysis of the influence of the air flow rate per diffuser on the ASR performance is carried out. The results show a reduction of 14.5% in the energy consumption of the aeration system when the air flow rate per diffuser is reduced. The model provides an insight into the aeration inefficiencies produced within ASRs. Copyright © 2018 Elsevier Ltd. All rights reserved.
Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

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

PubMed

Assar, Rodrigo; Montecino, Martín A; Maass, Alejandro; Sherman, David J

2014-07-01

In order to describe the dynamic behavior of a complex biological system, it is useful to combine models integrating processes at different levels and with temporal dependencies. Such combinations are necessary for modeling acclimatization, a phenomenon where changes in environmental conditions can induce drastic changes in the behavior of a biological system. In this article we formalize the use of hybrid systems as a tool to model this kind of biological behavior. A modeling scheme called strong switches is proposed. It allows one to take into account both minor adjustments to the coefficients of a continuous model, and, more interestingly, large-scale changes to the structure of the model. We illustrate the proposed methodology with two applications: acclimatization in wine fermentation kinetics, and acclimatization of osteo-adipo differentiation system linking stimulus signals to bone mass. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
From Biology to Mathematical Models and Back: Teaching Modeling to Biology Students, and Biology to Math and Engineering Students

PubMed Central

McManus, Jeffrey M.; Shaw, Kendrick M.

2010-01-01

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

PubMed

McCaig, Chris; Begon, Mike; Norman, Rachel; Shankland, Carron

2011-03-01

Changing scale, for example, the ability to move seamlessly from an individual-based model to a population-based model, is an important problem in many fields. In this paper, we introduce process algebra as a novel solution to this problem in the context of models of infectious disease spread. Process algebra allows us to describe a system in terms of the stochastic behaviour of individuals, and is a technique from computer science. We review the use of process algebra in biological systems, and the variety of quantitative and qualitative analysis techniques available. The analysis illustrated here solves the changing scale problem: from the individual behaviour we can rigorously derive equations to describe the mean behaviour of the system at the level of the population. The biological problem investigated is the transmission of infection, and how this relates to individual interactions.

Modeling for Visual Feature Extraction Using Spiking Neural Networks

NASA Astrophysics Data System (ADS)

Kimura, Ichiro; Kuroe, Yasuaki; Kotera, Hiromichi; Murata, Tomoya

This paper develops models for “visual feature extraction” in biological systems by using “spiking neural network (SNN)”. The SNN is promising for developing the models because the information is encoded and processed by spike trains similar to biological neural networks. Two architectures of SNN are proposed for modeling the directionally selective and the motion parallax cell in neuro-sensory systems and they are trained so as to possess actual biological responses of each cell. To validate the developed models, their representation ability is investigated and their visual feature extraction mechanisms are discussed from the neurophysiological viewpoint. It is expected that this study can be the first step to developing a sensor system similar to the biological systems and also a complementary approach to investigating the function of the brain.
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.
Electrophoretic separator for purifying biologicals, part 1

NASA Technical Reports Server (NTRS)

Mccreight, L. R.

1978-01-01

A program to develop an engineering model of an electrophoretic separator for purifying biologicals is summarized. An extensive mathematical modeling study and numerous ground based tests were included. Focus was placed on developing an actual electrophoretic separator of the continuous flow type, configured and suitable for flight testing as a space processing applications rocket payload.
Effects of species biological traits and environmental heterogeneity on simulated tree species distribution shifts under climate change.

PubMed

Wang, Wen J; He, Hong S; Thompson, Frank R; Spetich, Martin A; Fraser, Jacob S

2018-09-01

Demographic processes (fecundity, dispersal, colonization, growth, and mortality) and their interactions with environmental changes are not well represented in current climate-distribution models (e.g., niche and biophysical process models) and constitute a large uncertainty in projections of future tree species distribution shifts. We investigate how species biological traits and environmental heterogeneity affect species distribution shifts. We used a species-specific, spatially explicit forest dynamic model LANDIS PRO, which incorporates site-scale tree species demography and competition, landscape-scale dispersal and disturbances, and regional-scale abiotic controls, to simulate the distribution shifts of four representative tree species with distinct biological traits in the central hardwood forest region of United States. Our results suggested that biological traits (e.g., dispersal capacity, maturation age) were important for determining tree species distribution shifts. Environmental heterogeneity, on average, reduced shift rates by 8% compared to perfect environmental conditions. The average distribution shift rates ranged from 24 to 200myear -1 under climate change scenarios, implying that many tree species may not able to keep up with climate change because of limited dispersal capacity, long generation time, and environmental heterogeneity. We suggest that climate-distribution models should include species demographic processes (e.g., fecundity, dispersal, colonization), biological traits (e.g., dispersal capacity, maturation age), and environmental heterogeneity (e.g., habitat fragmentation) to improve future predictions of species distribution shifts in response to changing climates. Copyright © 2018 Elsevier B.V. All rights reserved.
Mathematical modelling in developmental biology.

PubMed

Vasieva, Olga; Rasolonjanahary, Manan'Iarivo; Vasiev, Bakhtier

2013-06-01

In recent decades, molecular and cellular biology has benefited from numerous fascinating developments in experimental technique, generating an overwhelming amount of data on various biological objects and processes. This, in turn, has led biologists to look for appropriate tools to facilitate systematic analysis of data. Thus, the need for mathematical techniques, which can be used to aid the classification and understanding of this ever-growing body of experimental data, is more profound now than ever before. Mathematical modelling is becoming increasingly integrated into biological studies in general and into developmental biology particularly. This review outlines some achievements of mathematics as applied to developmental biology and demonstrates the mathematical formulation of basic principles driving morphogenesis. We begin by describing a mathematical formalism used to analyse the formation and scaling of morphogen gradients. Then we address a problem of interplay between the dynamics of morphogen gradients and movement of cells, referring to mathematical models of gastrulation in the chick embryo. In the last section, we give an overview of various mathematical models used in the study of the developmental cycle of Dictyostelium discoideum, which is probably the best example of successful mathematical modelling in developmental biology.
Ordinary differential equations with applications in molecular biology.

PubMed

Ilea, M; Turnea, M; Rotariu, M

2012-01-01

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

PubMed

Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.
Reinvigorating Introductory Biology: A Theme-based, Investigative Approach To Teaching Biology Majors.

ERIC Educational Resources Information Center

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

1997-01-01

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

USDA-ARS?s Scientific Manuscript database

It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (P...
The short-lived African turquoise killifish: an emerging experimental model for ageing

PubMed Central

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

2016-01-01

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

PubMed Central

2014-01-01

Background Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of “omics” data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting. Methods We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis. Results Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques. Conclusions We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability. PMID:24965703
Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection

NASA Astrophysics Data System (ADS)

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

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

PubMed Central

Andersen, Barbara L.; Kiecolt-Glaser, Janice K.; Glaser, Ronald

2009-01-01

Approximately 1 million Americans are diagnosed with cancer each year and must cope with the disease and treatments. Many studies have documented the deteriorations in quality of life that occur. These data suggest that the adjustment process is burdensome and lengthy. There is ample evidence showing that adults experiencing other long-term stressors experience not only high rates of adjustment difficulties (e.g., syndromal depression) but important biologic effects, such as persistent downregulation of elements of the immune system, and adverse health outcomes, such as higher rates of respiratory tract infections. Thus, deteriorations in quality of life with cancer are underscored if they have implications for biological processes, such as the immune system, relating to disease progression and spread. Considering these and other data, a biobehavioral model of adjustment to the stresses of cancer is offered, and mechanisms by which psychological and behavioral responses may influence biological processes and, perhaps, health outcomes are proposed. Finally, strategies for testing the model via experiments testing psychological interventions are offered. PMID:8024167
The use of 'Omics technology to rationally improve industrial mammalian cell line performance.

PubMed

Lewis, Amanda M; Abu-Absi, Nicholas R; Borys, Michael C; Li, Zheng Jian

2016-01-01

Biologics represent an increasingly important class of therapeutics, with 7 of the 10 top selling drugs from 2013 being in this class. Furthermore, health authority approval of biologics in the immuno-oncology space is expected to transform treatment of patients with debilitating and deadly diseases. The growing importance of biologics in the healthcare field has also resulted in the recent approvals of several biosimilars. These recent developments, combined with pressure to provide treatments at lower costs to payers, are resulting in increasing need for the industry to quickly and efficiently develop high yielding, robust processes for the manufacture of biologics with the ability to control quality attributes within narrow distributions. Achieving this level of manufacturing efficiency and the ability to design processes capable of regulating growth, death and other cellular pathways through manipulation of media, feeding strategies, and other process parameters will undoubtedly be facilitated through systems biology tools generated in academic and public research communities. Here we discuss the intersection of systems biology, 'Omics technologies, and mammalian bioprocess sciences. Specifically, we address how these methods in conjunction with traditional monitoring techniques represent a unique opportunity to better characterize and understand host cell culture state, shift from an empirical to rational approach to process development and optimization of bioreactor cultivation processes. We summarize the following six key areas: (i) research applied to parental, non-recombinant cell lines; (ii) systems level datasets generated with recombinant cell lines; (iii) datasets linking phenotypic traits to relevant biomarkers; (iv) data depositories and bioinformatics tools; (v) in silico model development, and (vi) examples where these approaches have been used to rationally improve cellular processes. We critically assess relevant and state of the art research being conducted in academic, government and industrial laboratories. Furthermore, we apply our expertise in bioprocess to define a potential model for integration of these systems biology approaches into biologics development. © 2015 Wiley Periodicals, Inc.
Kinetic Theory and Simulation of Single-Channel Water Transport

NASA Astrophysics Data System (ADS)

Tajkhorshid, Emad; Zhu, Fangqiang; Schulten, Klaus

Water translocation between various compartments of a system is a fundamental process in biology of all living cells and in a wide variety of technological problems. The process is of interest in different fields of physiology, physical chemistry, and physics, and many scientists have tried to describe the process through physical models. Owing to advances in computer simulation of molecular processes at an atomic level, water transport has been studied in a variety of molecular systems ranging from biological water channels to artificial nanotubes. While simulations have successfully described various kinetic aspects of water transport, offering a simple, unified model to describe trans-channel translocation of water turned out to be a nontrivial task.
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.
An overview of bioinformatics methods for modeling biological pathways in yeast.

PubMed

Hou, Jie; Acharya, Lipi; Zhu, Dongxiao; Cheng, Jianlin

2016-03-01

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.
A statistical approach to quasi-extinction forecasting.

PubMed

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

2007-12-01

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

PubMed

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

2017-01-04

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

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

PubMed Central

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

2017-01-01

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

PubMed

Pei, Zhi-Jun; Gao, Guan-Xin; Hao, Bo; Qiao, Qing-Li; Ai, Hui-Jian

2016-04-01

Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases. Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue. Some retinal models have been presented, ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena. However, Most of these focus on stimulus image compression, edge detection and reconstruction, but do not generate spike trains corresponding to visual image. In this study, based on state-of-the-art retinal physiological mechanism, including effective visual information extraction, static nonlinear rectification of biological systems and neurons Poisson coding, a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward, which integrates both anatomic connections and functional computations of retina. Using MATLAB software, spike trains corresponding to stimulus image were numerically computed by four steps: linear spatiotemporal filtering, static nonlinear rectification, radial sampling and then Poisson spike generation. The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina, which is helpful in developing artificial retina for the retinally blind.
Physical models of biological information and adaptation.

PubMed

Stuart, C I

1985-04-07

The bio-informational equivalence asserts that biological processes reduce to processes of information transfer. In this paper, that equivalence is treated as a metaphor with deeply anthropomorphic content of a sort that resists constitutive-analytical definition, including formulation within mathematical theories of information. It is argued that continuance of the metaphor, as a quasi-theoretical perspective in biology, must entail a methodological dislocation between biological and physical science. It is proposed that a general class of functions, drawn from classical physics, can serve to eliminate the anthropomorphism. Further considerations indicate that the concept of biological adaptation is central to the general applicability of the informational idea in biology; a non-anthropomorphic treatment of adaptive phenomena is suggested in terms of variational principles.
Systems cell biology

PubMed Central

Mast, Fred D.; Ratushny, Alexander V.

2014-01-01

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology. PMID:25225336
Automatic Screening for Perturbations in Boolean Networks.

PubMed

Schwab, Julian D; Kestler, Hans A

2018-01-01

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.
Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models.

PubMed

Le Muzic, M; Mindek, P; Sorger, J; Autin, L; Goodsell, D; Viola, I

2016-06-01

In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine-tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer-based approach for visibility specification was valuable and effective for both, scientific and educational purposes.
Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models

PubMed Central

Le Muzic, M.; Mindek, P.; Sorger, J.; Autin, L.; Goodsell, D.; Viola, I.

2017-01-01

In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine-tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer-based approach for visibility specification was valuable and effective for both, scientific and educational purposes. PMID:28344374
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
Understanding Undergraduates’ Problem-Solving Processes †

PubMed Central

Nehm, Ross H.

2010-01-01

Fostering effective problem-solving skills is one of the most longstanding and widely agreed upon goals of biology education. Nevertheless, undergraduate biology educators have yet to leverage many major findings about problem-solving processes from the educational and cognitive science research literatures. This article highlights key facets of problem-solving processes and introduces methodologies that may be used to reveal how undergraduate students perceive and represent biological problems. Overall, successful problem-solving entails a keen sensitivity to problem contexts, disciplined internal representation or modeling of the problem, and the principled management and deployment of cognitive resources. Context recognition tasks, problem representation practice, and cognitive resource management receive remarkably little emphasis in the biology curriculum, despite their central roles in problem-solving success. PMID:23653710
An online model composition tool for system biology models

PubMed Central

2013-01-01

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

PubMed Central

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

2013-01-01

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

PubMed

Bettinger, Jesse S; Eastman, Timothy E

2017-12-01

Recent advances in modern physics and biology reveal several scenarios in which top-down effects (Ellis, 2016) and anticipatory systems (Rosen, 1980) indicate processes at work enabling active modeling and inference such that anticipated effects project onto potential causes. We extrapolate a broad landscape of anticipatory systems in the natural sciences extending to computational neuroscience of perception in the capacity of Bayesian inferential models of predictive processing. This line of reasoning also comes with philosophical foundations, which we develop in terms of counterfactual reasoning and possibility space, Whitehead's process thought, and correlations with Eastern wisdom traditions. Copyright © 2017 Elsevier Ltd. All rights reserved.
Biomorphodynamics: Physical-biological feedbacks that shape landscapes

USGS Publications Warehouse

Murray, A.B.; Knaapen, M.A.F.; Tal, M.; Kirwan, M.L.

2008-01-01

Plants and animals affect morphological evolution in many environments. The term "ecogeomorphology" describes studies that address such effects. In this opinion article we use the term "biomorphodynamics" to characterize a subset of ecogeomorphologic studies: those that investigate not only the effects of organisms on physical processes and morphology but also how the biological processes depend on morphology and physical forcing. The two-way coupling precipitates feedbacks, leading to interesting modes of behavior, much like the coupling between flow/sediment transport and morphology leads to rich morphodynamic behaviors. Select examples illustrate how even the basic aspects of some systems cannot be understood without considering biomorphodynamic coupling. Prominent examples include the dynamic interactions between vegetation and flow/sediment transport that can determine river channel patterns and the multifaceted biomorphodynamic feedbacks shaping tidal marshes and channel networks. These examples suggest that the effects of morphology and physical processes on biology tend to operate over the timescale of the evolution of the morphological pattern. Thus, in field studies, which represent a snapshot in the pattern evolution, these effects are often not as obvious as the effects of biology on physical processes. However, numerical modeling indicates that the influences on biology from physical processes can play a key role in shaping landscapes and that even local and temporary vegetation disturbances can steer large-scale, long-term landscape evolution. The prevalence of biomorphodynamic research is burgeoning in recent years, driven by societal need and a confluence of complex systems-inspired modeling approaches in ecology and geomorphology. To make fundamental progress in understanding the dynamics of many landscapes, our community needs to increasingly learn to look for two-way, biomorphodynamic feedbacks and to collect new types of data to support the modeling of such emergent interactions. Copyright 2008 by the American Geophysical Union.
A comparison of form processing involved in the perception of biological and nonbiological movements

PubMed Central

Thurman, Steven M.; Lu, Hongjing

2016-01-01

Although there is evidence for specialization in the human brain for processing biological motion per se, few studies have directly examined the specialization of form processing in biological motion perception. The current study was designed to systematically compare form processing in perception of biological (human walkers) to nonbiological (rotating squares) stimuli. Dynamic form-based stimuli were constructed with conflicting form cues (position and orientation), such that the objects were perceived to be moving ambiguously in two directions at once. In Experiment 1, we used the classification image technique to examine how local form cues are integrated across space and time in a bottom-up manner. By comparing with a Bayesian observer model that embodies generic principles of form analysis (e.g., template matching) and integrates form information according to cue reliability, we found that human observers employ domain-general processes to recognize both human actions and nonbiological object movements. Experiments 2 and 3 found differential top-down effects of spatial context on perception of biological and nonbiological forms. When a background does not involve social information, observers are biased to perceive foreground object movements in the direction opposite to surrounding motion. However, when a background involves social cues, such as a crowd of similar objects, perception is biased toward the same direction as the crowd for biological walking stimuli, but not for rotating nonbiological stimuli. The model provided an accurate account of top-down modulations by adjusting the prior probabilities associated with the internal templates, demonstrating the power and flexibility of the Bayesian approach for visual form perception. PMID:26746875
Implementing a Flipped Classroom: A Case Study of Biology Teaching in a Greek High School

ERIC Educational Resources Information Center

Gariou-Papalexiou, Angeliki; Papadakis, Spyros; Manousou, Evangelia; Georgiadu, Irene

2017-01-01

The purpose of this study was to investigate the application of the model of the "flipped classroom" as a complementary method to school distance education in junior high school Biology. The "flipped classroom" model attempts a different way of organizing the educational process according to which the traditional methods of…
PREDICTIVE MODELING OF LIGHT-INDUCED MORTALITY OF ENTEROCOCCI FAECALIS IN RECREATIONAL WATERS

EPA Science Inventory

One approach to predictive modeling of biological contamination of recreational waters involves the application of process-based approaches that consider microbial sources, hydrodynamic transport, and microbial fate. This presentation focuses on one important fate process, light-...
Primary Literature as a Basis for a High-School Biology Curriculum.

ERIC Educational Resources Information Center

Yarden, Anat; Brill, Gilat; Falk, Hedda

2001-01-01

Adopts primary literature as a means of developing scientific literacy among high-school biology majors. Reports on the development and implementation of a primary literature-based curriculum in developmental biology. Discusses the process of adapting original research articles to the high-school level, as well as a conversational model developed…
The short-lived African turquoise killifish: an emerging experimental model for ageing.

PubMed

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

2016-02-01

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

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

An overview of topic modeling and its current applications in bioinformatics.

PubMed

Liu, Lin; Tang, Lin; Dong, Wen; Yao, Shaowen; Zhou, Wei

2016-01-01

With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers' ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.
Systems cell biology.

PubMed

Mast, Fred D; Ratushny, Alexander V; Aitchison, John D

2014-09-15

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology. © 2014 Mast et al.
Modeling and Advanced Control for Sustainable Process Systems

EPA Science Inventory

This book chapter introduces a novel process systems engineering framework that integrates process control with sustainability assessment tools for the simultaneous evaluation and optimization of process operations. The implemented control strategy consists of a biologically-insp...
Conway's "Game of Life" and the Epigenetic Principle.

PubMed

Caballero, Lorena; Hodge, Bob; Hernandez, Sergio

2016-01-01

Cellular automatons and computer simulation games are widely used as heuristic devices in biology, to explore implications and consequences of specific theories. Conway's Game of Life has been widely used for this purpose. This game was designed to explore the evolution of ecological communities. We apply it to other biological processes, including symbiopoiesis. We show that Conway's organization of rules reflects the epigenetic principle, that genetic action and developmental processes are inseparable dimensions of a single biological system, analogous to the integration processes in symbiopoiesis. We look for similarities and differences between two epigenetic models, by Turing and Edelman, as they are realized in Game of Life objects. We show the value of computer simulations to experiment with and propose generalizations of broader scope with novel testable predictions. We use the game to explore issues in symbiopoiesis and evo-devo, where we explore a fractal hypothesis: that self-similarity exists at different levels (cells, organisms, ecological communities) as a result of homologous interactions of two as processes modeled in the Game of Life.
Modelling the Impact of Soil Management on Soil Functions

NASA Astrophysics Data System (ADS)

Vogel, H. J.; Weller, U.; Rabot, E.; Stößel, B.; Lang, B.; Wiesmeier, M.; Urbanski, L.; Wollschläger, U.

2017-12-01

Due to an increasing soil loss and an increasing demand for food and energy there is an enormous pressure on soils as the central resource for agricultural production. Besides the importance of soils for biomass production there are other essential soil functions, i.e. filter and buffer for water, carbon sequestration, provision and recycling of nutrients, and habitat for biological activity. All these functions have a direct feed back to biogeochemical cycles and climate. To render agricultural production efficient and sustainable we need to develop model tools that are capable to predict quantitatively the impact of a multitude of management measures on these soil functions. These functions are considered as emergent properties produced by soils as complex systems. The major challenge is to handle the multitude of physical, chemical and biological processes interacting in a non-linear manner. A large number of validated models for specific soil processes are available. However, it is not possible to simulate soil functions by coupling all the relevant processes at the detailed (i.e. molecular) level where they are well understood. A new systems perspective is required to evaluate the ensemble of soil functions and their sensitivity to external forcing. Another challenge is that soils are spatially heterogeneous systems by nature. Soil processes are highly dependent on the local soil properties and, hence, any model to predict soil functions needs to account for the site-specific conditions. For upscaling towards regional scales the spatial distribution of functional soil types need to be taken into account. We propose a new systemic model approach based on a thorough analysis of the interactions between physical, chemical and biological processes considering their site-specific characteristics. It is demonstrated for the example of soil compaction and the recovery of soil structure, water capacity and carbon stocks as a result of plant growth and biological activity. Coupling of the observed nonlinear interactions allows for modeling the stability and resilience of soil systems in terms of their essential functions.
Payao: a community platform for SBML pathway model curation

PubMed Central

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

2010-01-01

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

ERIC Educational Resources Information Center

Ma, Min; Van Oystaeyen, Fred

2016-01-01

The authors' aim was to arrive at a measurable model of the creative process by putting creativity in the context of a learning process. The authors aimed to provide a rather detailed description of how creative thinking fits in a general description of the learning process without trying to go into an analysis of a biological description of the…
Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406
Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

PubMed Central

de Graaf, Albert A.; Freidig, Andreas P.; De Roos, Baukje; Jamshidi, Neema; Heinemann, Matthias; Rullmann, Johan A.C.; Hall, Kevin D.; Adiels, Martin; van Ommen, Ben

2009-01-01

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales. PMID:19956660
Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM).

PubMed

Wu, Xiaohui; Yang, Yang; Wu, Gaoming; Mao, Juan; Zhou, Tao

2016-01-01

Applications of activated sludge models (ASM) in simulating industrial biological wastewater treatment plants (WWTPs) are still difficult due to refractory and complex components in influents as well as diversity in activated sludges. In this study, an ASM3 modeling study was conducted to simulate and optimize a practical coking wastewater treatment plant (CWTP). First, respirometric characterizations of the coking wastewater and CWTP biomasses were conducted to determine the specific kinetic and stoichiometric model parameters for the consecutive aeration-anoxic-aeration (O-A/O) biological process. All ASM3 parameters have been further estimated and calibrated, through cross validation by the model dynamic simulation procedure. Consequently, an ASM3 model was successfully established to accurately simulate the CWTP performances in removing COD and NH4-N. An optimized CWTP operation condition could be proposed reducing the operation cost from 6.2 to 5.5 €/m(3) wastewater. This study is expected to provide a useful reference for mathematic simulations of practical industrial WWTPs. Copyright © 2015 Elsevier Ltd. All rights reserved.
Application of a 2-step process for the biological treatment of sulfidic spent caustics.

PubMed

de Graaff, Marco; Klok, Johannes B M; Bijmans, Martijn F M; Muyzer, Gerard; Janssen, Albert J H

2012-03-01

This research demonstrates the feasibility and advantages of a 2-step process for the biological treatment of sulfidic spent caustics under halo-alkaline conditions (i.e. pH 9.5; Na(+) = 0.8 M). Experiments with synthetically prepared solutions were performed in a continuously fed system consisting of two gas-lift reactors in series operated at aerobic conditions at 35 °C. The detoxification of sulfide to thiosulfate in the first step allowed the successful biological treatment of total-S loading rates up to 33 mmol L(-1) day(-1). In the second, biological step, the remaining sulfide and thiosulfate was completely converted to sulfate by haloalkaliphilic sulfide oxidizing bacteria. Mathematical modeling of the 2-step process shows that under the prevailing conditions an optimal reactor configuration consists of 40% 'abiotic' and 60% 'biological' volume, whilst the total reactor volume is 22% smaller than for the 1-step process. Copyright © 2011 Elsevier Ltd. All rights reserved.
Evaluating and improving count-based population inference: A case study from 31 years of monitoring Sandhill Cranes

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.

2017-01-01

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

ERIC Educational Resources Information Center

Couper, Lisa; Johannes, Kristen; Powers, Jackie; Silberglitt, Matt; Davenport, Jodi

2016-01-01

Understanding key concepts in molecular biology requires reasoning about molecular processes that are not directly observable and, as such, presents a challenge to students and teachers. We ask whether novel interactive physical models and activities can help students understand key processes in viral replication. Our 3D tangible models are…
The quest for a new modelling framework in mathematical biology. Comment on "On the interplay between mathematics and biology: Hallmarks towards a new systems biology" by N. Bellomo et al.

NASA Astrophysics Data System (ADS)

Eftimie, Raluca

2015-03-01

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

PubMed

Krupke, Debra M; Begley, Dale A; Sundberg, John P; Richardson, Joel E; Neuhauser, Steven B; Bult, Carol J

2017-11-01

Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology. The Mouse Tumor Biology (MTB) database is an expertly curated, comprehensive compendium of mouse models of human cancer. Through the enforcement of nomenclature and related annotation standards, MTB supports aggregation of data about a cancer model from diverse sources and assessment of how genetic background of a mouse strain influences the biological properties of a specific tumor type and model utility. Cancer Res; 77(21); e67-70. ©2017 AACR . ©2017 American Association for Cancer Research.
NATURALIST'S APPLICATION OF A PROMISING TURBULENCE MODEL

EPA Science Inventory

Turbulence has infinite applications to the biological sciences, affecting distributions, transport, feeding, mating, and other biological processes. The topic is like the universe for which five successive magnefications are required to finally focus on a topic that can be grasp...
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.
Effect of sub-pore scale morphology of biological deposits on porous media flow properties

NASA Astrophysics Data System (ADS)

Ghezzehei, T. A.

2012-12-01

Biological deposits often influence fluid flow by altering the pore space morphology and related hydrologic properties such as porosity, water retention characteristics, and permeability. In most coupled-processes models changes in porosity are inferred from biological process models using mass-balance. The corresponding evolution of permeability is estimated using (semi-) empirical porosity-permeability functions such as the Kozeny-Carman equation or power-law functions. These equations typically do not account for the heterogeneous spatial distribution and morphological irregularities of the deposits. As a result, predictions of permeability evolution are generally unsatisfactory. In this presentation, we demonstrate the significance of pore-scale deposit distribution on porosity-permeability relations using high resolution simulations of fluid flow through a single pore interspersed with deposits of varying morphologies. Based on these simulations, we present a modification to the Kozeny-Carman model that accounts for the shape of the deposits. Limited comparison with published experimental data suggests the plausibility of the proposed conceptual model.

The system-resonance approach in modeling genetic structures.

PubMed

Petoukhov, Sergey V

2016-01-01

The founder of the theory of resonance in structural chemistry Linus Pauling established the importance of resonance patterns in organization of living systems. Any living organism is a great chorus of coordinated oscillatory processes. From the formal point of view, biological organism is an oscillatory system with a great number of degrees of freedom. Such systems are studied in the theory of oscillations using matrix mathematics of their resonance characteristics. This study is devoted to a new approach for modeling genetically inherited structures and processes in living organisms using mathematical tools of the theory of resonances. This approach reveals hidden relationships in a number of genetic phenomena and gives rise to a new class of bio-mathematical models, which contribute to a convergence of biology with physics and informatics. In addition some relationships of molecular-genetic ensembles with mathematics of noise-immunity coding of information in modern communications technology are shown. Perspectives of applications of the phenomena of vibrational mechanics for modeling in biology are discussed. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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.
Rich in life but poor in data: the known knowns and known unknowns of modelling how soil biology drives soil structure

NASA Astrophysics Data System (ADS)

Hallett, Paul; Ogden, Mike

2015-04-01

Soil biology has a fascinating capacity to manipulate pore structure by altering or overcoming hydrological and mechanical properties of soil. Many have postulated, quite rightly, that this capacity of soil biology to 'engineer' its habitat drives its diversity, improves competitiveness and increases resilience to external stresses. A large body of observational research has quantified pore structure evolution accompanied by the growth of organisms in soil. Specific compounds that are exuded by organisms or the biological structures they create have been isolated and found to correlate well with observed changes to pore structure or soil stability. This presentation will provide an overview of basic mechanical and hydrological properties of soil that are affected by biology, and consider missing data that are essential to model how they impact soil structure evolution. Major knowledge gaps that prevent progress will be identified and suggestions will be made of how research in this area should progress. We call for more research to gain a process based understanding of structure formation by biology, to complement observational studies of soil structure before and after imposed biological activity. Significant advancement has already been made in modelling soil stabilisation by plant roots, by combining data on root biomechanics, root-soil interactions and soil mechanical properties. Approaches for this work were developed from earlier materials science and geotechnical engineering research, and the same ethos should be adopted to model the impacts of other biological compounds. Fungal hyphae likely reinforce soils in a similar way to plant roots, with successful biomechanical measurements of these micron diameter structures achieved with micromechanical test frames. Extending root reinforcement models to fungi would not be a straightforward exercise, however, as interparticle bonding and changes to pore water caused by fungal exudates could have a major impact on structure formation and stability. Biological exudates from fungi, bacteria or roots have been found to decrease surface tension and increase viscosity of pore water, with observed impacts to soil strength and water retention. Modelling approaches developed in granular mechanics and geotechnical engineering could be built upon to incorporate biological transformations of hydrological and mechanical properties of soil. With new testing approaches, adapted from materials science, pore scale hydromechanical impacts from biological exudates can be quantified. The research can be complemented with model organisms with differences in biological structures (e.g. root hair mutants), exudation or other properties. Coupled with technological advances that provide 4D imaging of soil structure at relatively rapid capture rates, the potential opportunities to disentangle and model how biology drives soil structure evolution and stability are vast. By quantifying basic soil hydrological and mechanical processes that are driven by soil biology, unknown unknowns may also emerge, providing new insight into how soils function.
Decoupling the influence of biological and physical processes on the dissolved oxygen in the Chesapeake Bay

NASA Astrophysics Data System (ADS)

Du, Jiabi; Shen, Jian

2015-01-01

is instructive and essential to decouple the effects of biological and physical processes on the dissolved oxygen condition, in order to understand their contribution to the interannual variability of hypoxia in Chesapeake Bay since the 1980s. A conceptual bottom DO budget model is applied, using the vertical exchange time scale (VET) to quantify the physical condition and net oxygen consumption rate to quantify biological activities. By combining observed DO data and modeled VET values along the main stem of the Chesapeake Bay, the monthly net bottom DO consumption rate was estimated for 1985-2012. The DO budget model results show that the interannual variations of physical conditions accounts for 88.8% of the interannual variations of observed DO. The high similarity between the VET spatial pattern and the observed DO suggests that physical processes play a key role in regulating the DO condition. Model results also show that long-term VET has a slight increase in summer, but no statistically significant trend is found. Correlations among southerly wind strength, North Atlantic Oscillation index, and VET demonstrate that the physical condition in the Chesapeake Bay is highly controlled by the large-scale climate variation. The relationship is most significant during the summer, when the southerly wind dominates throughout the Chesapeake Bay. The seasonal pattern of the averaged net bottom DO consumption rate (B'20) along the main stem coincides with that of the chlorophyll-a concentration. A significant correlation between nutrient loading and B'20 suggests that the biological processes in April-May are most sensitive to the nutrient loading.
Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

PubMed Central

Zur, Hadas; Tuller, Tamir

2016-01-01

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field. PMID:27591251
Mathematical Manipulative Models: In Defense of “Beanbag Biology”

PubMed Central

Gaff, Holly; Weisstein, Anton E.

2010-01-01

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

PubMed

Endler, Lukas; Rodriguez, Nicolas; Juty, Nick; Chelliah, Vijayalakshmi; Laibe, Camille; Li, Chen; Le Novère, Nicolas

2009-08-06

A key component of any synthetic biology effort is the use of quantitative models. These models and their corresponding simulations allow optimization of a system design, as well as guiding their subsequent analysis. Once a domain mostly reserved for experts, dynamical modelling of gene regulatory and reaction networks has been an area of growth over the last decade. There has been a concomitant increase in the number of software tools and standards, thereby facilitating model exchange and reuse. We give here an overview of the model creation and analysis processes as well as some software tools in common use. Using markup language to encode the model and associated annotation, we describe the mining of components, their integration in relational models, formularization and parametrization. Evaluation of simulation results and validation of the model close the systems biology 'loop'.
[Construction of automatic elucidation platform for mechanism of traditional Chinese medicine].

PubMed

Zhang, Bai-xia; Luo, Si-jun; Yan, Jing; Gu, Hao; Luo, Ji; Zhang, Yan-ling; Tao, Ou; Wang, Yun

2015-10-01

Aim at the two problems in the field of traditional Chinese medicine (TCM) mechanism elucidation, one is the lack of detailed biological processes information, next is the low efficient in constructing network models, we constructed an auxiliary elucidation system for the TCM mechanism and realize the automatic establishment of biological network model. This study used the Entity Grammar Systems (EGS) as the theoretical framework, integrated the data of formulae, herbs, chemical components, targets of component, biological reactions, signaling pathways and disease related proteins, established the formal models, wrote the reasoning engine, constructed the auxiliary elucidation system for the TCM mechanism elucidation. The platform provides an automatic modeling method for biological network model of TCM mechanism. It would be benefit to perform the in-depth research on TCM theory of natures and combination and provides the scientific references for R&D of TCM.
Prototype Biology-Based Radiation Risk Module Project

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

PubMed Central

Kar, Parimal; Feig, Michael

2017-01-01

Computer simulations are indispensable tools for studying the structure and dynamics of biological macromolecules. Biochemical processes occur on different scales of length and time. Atomistic simulations cannot cover the relevant spatiotemporal scales at which the cellular processes occur. To address this challenge, coarse-grained (CG) modeling of the biological systems are employed. Over the last few years, many CG models for proteins continue to be developed. However, many of them are not transferable with respect to different systems and different environments. In this review, we discuss those CG protein models that are transferable and that retain chemical specificity. We restrict ourselves to CG models of soluble proteins only. We also briefly review recent progress made in the multi-scale hybrid all-atom/coarse-grained simulations of proteins. PMID:25443957
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.
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.
Live Imaging of Centriole Dynamics by Fluorescently Tagged Proteins in Starfish Oocyte Meiosis.

PubMed

Borrego-Pinto, Joana; Somogyi, Kálmán; Lénárt, Péter

2016-01-01

High throughput DNA sequencing, the decreasing costs of DNA synthesis, and universal techniques for genetic manipulation have made it much easier and quicker to establish molecular tools for any organism than it has been 5 years ago. This opens a great opportunity for reviving "nonconventional" model organisms, which are particularly suited to study a specific biological process and many of which have already been established before the era of molecular biology. By taking advantage of transcriptomics, in particular, these systems can now be easily turned into full fetched models for molecular cell biology.As an example, here we describe how we established molecular tools in the starfish Patiria miniata, which has been a popular model for cell and developmental biology due to the synchronous and rapid development, transparency, and easy handling of oocytes, eggs, and embryos. Here, we detail how we used a de novo assembled transcriptome to produce molecular markers and established conditions for live imaging to investigate the molecular mechanisms underlying centriole elimination-a poorly understood process essential for sexual reproduction of animal species.
Towards a whole-cell modeling approach for synthetic biology

NASA Astrophysics Data System (ADS)

Purcell, Oliver; Jain, Bonny; Karr, Jonathan R.; Covert, Markus W.; Lu, Timothy K.

2013-06-01

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.
A Computational Approach to Increase Time Scales in Brownian Dynamics–Based Reaction-Diffusion Modeling

PubMed Central

Frazier, Zachary

2012-01-01

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

PubMed

Harrison, Jonathan U; Baker, Ruth E

2018-06-25

Imaging data has become an essential tool to explore key biological questions at various scales, for example the motile behaviour of bacteria or the transport of mRNA, and it has the potential to transform our understanding of important transport mechanisms. Often these imaging studies require us to compare biological species or mutants, and to do this we need to quantitatively characterise their behaviour. Mathematical models offer a quantitative description of a system that enables us to perform this comparison, but to relate mechanistic mathematical models to imaging data, we need to estimate their parameters. In this work we study how collecting data at different temporal resolutions impacts our ability to infer parameters of biological transport models; performing exact inference for simple velocity jump process models in a Bayesian framework. The question of how best to choose the frequency with which data is collected is prominent in a host of studies because the majority of imaging technologies place constraints on the frequency with which images can be taken, and the discrete nature of observations can introduce errors into parameter estimates. In this work, we mitigate such errors by formulating the velocity jump process model within a hidden states framework. This allows us to obtain estimates of the reorientation rate and noise amplitude for noisy observations of a simple velocity jump process. We demonstrate the sensitivity of these estimates to temporal variations in the sampling resolution and extent of measurement noise. We use our methodology to provide experimental guidelines for researchers aiming to characterise motile behaviour that can be described by a velocity jump process. In particular, we consider how experimental constraints resulting in a trade-off between temporal sampling resolution and observation noise may affect parameter estimates. Finally, we demonstrate the robustness of our methodology to model misspecification, and then apply our inference framework to a dataset that was generated with the aim of understanding the localization of RNA-protein complexes.
An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health

PubMed Central

Mancy, Rebecca; Brock, Patrick M.; Kao, Rowland R.

2017-01-01

Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature. PMID:29021983
An Integrated Framework for Process-Driven Model Construction in Disease Ecology and Animal Health.

PubMed

Mancy, Rebecca; Brock, Patrick M; Kao, Rowland R

2017-01-01

Process models that focus on explicitly representing biological mechanisms are increasingly important in disease ecology and animal health research. However, the large number of process modelling approaches makes it difficult to decide which is most appropriate for a given disease system and research question. Here, we discuss different motivations for using process models and present an integrated conceptual analysis that can be used to guide the construction of infectious disease process models and comparisons between them. Our presentation complements existing work by clarifying the major differences between modelling approaches and their relationship with the biological characteristics of the epidemiological system. We first discuss distinct motivations for using process models in epidemiological research, identifying the key steps in model design and use associated with each. We then present a conceptual framework for guiding model construction and comparison, organised according to key aspects of epidemiological systems. Specifically, we discuss the number and type of disease states, whether to focus on individual hosts (e.g., cows) or groups of hosts (e.g., herds or farms), how space or host connectivity affect disease transmission, whether demographic and epidemiological processes are periodic or can occur at any time, and the extent to which stochasticity is important. We use foot-and-mouth disease and bovine tuberculosis in cattle to illustrate our discussion and support explanations of cases in which different models are used to address similar problems. The framework should help those constructing models to structure their approach to modelling decisions and facilitate comparisons between models in the literature.
[Construction of individual-based ecological model for Scomber japonicas at its early growth stages in East China Sea].

PubMed

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

2012-06-01

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

PubMed

Nguyen, Tramy; Roehner, Nicholas; Zundel, Zach; Myers, Chris J

2016-06-17

Standards are important to synthetic biology because they enable exchange and reproducibility of genetic designs. This paper describes a procedure for converting between two standards: the Systems Biology Markup Language (SBML) and the Synthetic Biology Open Language (SBOL). SBML is a standard for behavioral models of biological systems at the molecular level. SBOL describes structural and basic qualitative behavioral aspects of a biological design. Converting SBML to SBOL enables a consistent connection between behavioral and structural information for a biological design. The conversion process described in this paper leverages Systems Biology Ontology (SBO) annotations to enable inference of a designs qualitative function.

Chasing Perfection: Should We Reduce Model Uncertainty in Carbon Cycle-Climate Feedbacks

NASA Astrophysics Data System (ADS)

Bonan, G. B.; Lombardozzi, D.; Wieder, W. R.; Lindsay, K. T.; Thomas, R. Q.

2015-12-01

Earth system model simulations of the terrestrial carbon (C) cycle show large multi-model spread in the carbon-concentration and carbon-climate feedback parameters. Large differences among models are also seen in their simulation of global vegetation and soil C stocks and other aspects of the C cycle, prompting concern about model uncertainty and our ability to faithfully represent fundamental aspects of the terrestrial C cycle in Earth system models. Benchmarking analyses that compare model simulations with common datasets have been proposed as a means to assess model fidelity with observations, and various model-data fusion techniques have been used to reduce model biases. While such efforts will reduce multi-model spread, they may not help reduce uncertainty (and increase confidence) in projections of the C cycle over the twenty-first century. Many ecological and biogeochemical processes represented in Earth system models are poorly understood at both the site scale and across large regions, where biotic and edaphic heterogeneity are important. Our experience with the Community Land Model (CLM) suggests that large uncertainty in the terrestrial C cycle and its feedback with climate change is an inherent property of biological systems. The challenge of representing life in Earth system models, with the rich diversity of lifeforms and complexity of biological systems, may necessitate a multitude of modeling approaches to capture the range of possible outcomes. Such models should encompass a range of plausible model structures. We distinguish between model parameter uncertainty and model structural uncertainty. Focusing on improved parameter estimates may, in fact, limit progress in assessing model structural uncertainty associated with realistically representing biological processes. Moreover, higher confidence may be achieved through better process representation, but this does not necessarily reduce uncertainty.
Influence of using challenging tasks in biology classrooms on students' cognitive knowledge structure: an empirical video study

NASA Astrophysics Data System (ADS)

Nawani, Jigna; Rixius, Julia; Neuhaus, Birgit J.

2016-08-01

Empirical analysis of secondary biology classrooms revealed that, on average, 68% of teaching time in Germany revolved around processing tasks. Quality of instruction can thus be assessed by analyzing the quality of tasks used in classroom discourse. This quasi-experimental study analyzed how teachers used tasks in 38 videotaped biology lessons pertaining to the topic 'blood and circulatory system'. Two fundamental characteristics used to analyze tasks include: (1) required cognitive level of processing (e.g. low level information processing: repetiition, summary, define, classify and high level information processing: interpret-analyze data, formulate hypothesis, etc.) and (2) complexity of task content (e.g. if tasks require use of factual, linking or concept level content). Additionally, students' cognitive knowledge structure about the topic 'blood and circulatory system' was measured using student-drawn concept maps (N = 970 students). Finally, linear multilevel models were created with high-level cognitive processing tasks and higher content complexity tasks as class-level predictors and students' prior knowledge, students' interest in biology, and students' interest in biology activities as control covariates. Results showed a positive influence of high-level cognitive processing tasks (β = 0.07; p < .01) on students' cognitive knowledge structure. However, there was no observed effect of higher content complexity tasks on students' cognitive knowledge structure. Presented findings encourage the use of high-level cognitive processing tasks in biology instruction.
Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

PubMed Central

2010-01-01

Background The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system. Results In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Conclusions Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics. PMID:21143785
Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system.

PubMed

Ghosh, Preetam; Ghosh, Samik; Basu, Kalyan; Das, Sajal K; Zhang, Chaoyang

2010-12-01

The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system. In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.
Computational Systems Biology in Cancer: Modeling Methods and Applications

PubMed Central

Materi, Wayne; Wishart, David S.

2007-01-01

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

USDA-ARS?s Scientific Manuscript database

Soil erosion directly affects the quality of the soil, its agricultural productivity and its biological diversity. Many mathematical models have been developed to estimate plot soil erosion at different temporal scales. At present, empirical soil loss equations and process-oriented models are consid...
Model-Data Fusion to Test Hypothesized Drivers of Lake Carbon Cycling Reveals Importance of Physical Controls

NASA Astrophysics Data System (ADS)

Hararuk, Oleksandra; Zwart, Jacob A.; Jones, Stuart E.; Prairie, Yves; Solomon, Christopher T.

2018-03-01

Formal integration of models and data to test hypotheses about the processes controlling carbon dynamics in lakes is rare, despite the importance of lakes in the carbon cycle. We built a suite of models (n = 102) representing different hypotheses about lake carbon processing, fit these models to data from a north-temperate lake using data assimilation, and identified which processes were essential for adequately describing the observations. The hypotheses that we tested concerned organic matter lability and its variability through time, temperature dependence of biological decay, photooxidation, microbial dynamics, and vertical transport of water via hypolimnetic entrainment and inflowing density currents. The data included epilimnetic and hypolimnetic CO2 and dissolved organic carbon, hydrologic fluxes, carbon loads, gross primary production, temperature, and light conditions at high frequency for one calibration and one validation year. The best models explained 76-81% and 64-67% of the variability in observed epilimnetic CO2 and dissolved organic carbon content in the validation data. Accurately describing C dynamics required accounting for hypolimnetic entrainment and inflowing density currents, in addition to accounting for biological transformations. In contrast, neither photooxidation nor variable organic matter lability improved model performance. The temperature dependence of biological decay (Q10) was estimated at 1.45, significantly lower than the commonly assumed Q10 of 2. By confronting multiple models of lake C dynamics with observations, we identified processes essential for describing C dynamics in a temperate lake at daily to annual scales, while also providing a methodological roadmap for using data assimilation to further improve understanding of lake C cycling.
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 finite element simulation of biological conversion processes in landfills.

PubMed

Robeck, M; Ricken, T; Widmann, R

2011-04-01

Landfills are the most common way of waste disposal worldwide. Biological processes convert the organic material into an environmentally harmful landfill gas, which has an impact on the greenhouse effect. After the depositing of waste has been stopped, current conversion processes continue and emissions last for several decades and even up to 100years and longer. A good prediction of these processes is of high importance for landfill operators as well as for authorities, but suitable models for a realistic description of landfill processes are rather poor. In order to take the strong coupled conversion processes into account, a constitutive three-dimensional model based on the multiphase Theory of Porous Media (TPM) has been developed at the University of Duisburg-Essen. The theoretical formulations are implemented in the finite element code FEAP. With the presented calculation concept we are able to simulate the coupled processes that occur in an actual landfill. The model's theoretical background and the results of the simulations as well as the meantime successfully performed simulation of a real landfill body will be shown in the following. Copyright © 2010 Elsevier Ltd. All rights reserved.
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.
Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

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

PubMed

Allen, Johnie J; Anderson, Craig A; Bushman, Brad J

2018-02-01

The General Aggression Model (GAM) is a comprehensive, integrative, framework for understanding aggression. It considers the role of social, cognitive, personality, developmental, and biological factors on aggression. Proximate processes of GAM detail how person and situation factors influence cognitions, feelings, and arousal, which in turn affect appraisal and decision processes, which in turn influence aggressive or nonaggressive behavioral outcomes. Each cycle of the proximate processes serves as a learning trial that affects the development and accessibility of aggressive knowledge structures. Distal processes of GAM detail how biological and persistent environmental factors can influence personality through changes in knowledge structures. GAM has been applied to understand aggression in many contexts including media violence effects, domestic violence, intergroup violence, temperature effects, pain effects, and the effects of global climate change. Copyright © 2017 Elsevier Ltd. All rights reserved.
Inter-institutional Development of a Poster-Based Cancer Biology Learning Tool

PubMed Central

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

2010-01-01

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

PubMed

Ferrer, J; Morenilla, J J; Bouzas, A; García-Usach, F

2004-01-01

Control and optimisation of plant processes has become a priority for WWTP managers. The calibration and verification of a mathematical model provides an important tool for the investigation of advanced control strategies that may assist in the design or optimization of WWTPs. This paper describes the calibration of the ASM2d model for two full scale biological nitrogen and phosphorus removal plants in order to characterize the biological process and to upgrade the plants' performance. Results from simulation showed a good correspondence with experimental data demonstrating that the model and the calibrated parameters were able to predict the behaviour of both WWTPs. Once the calibration and simulation process was finished, a study for each WWTP was done with the aim of improving its performance. Modifications focused on reactor configuration and operation strategies were proposed.
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).
Biological Engineering: A New Discipline for the Next Century.

ERIC Educational Resources Information Center

Tao, Bernard Y.

1993-01-01

Reviews the issues driving the need for a biological engineering discipline and summarizes current curricula at several universities. The Purdue Biochemical and Food Processing Engineering program is presented as a model for the implementation of curriculum objectives. (23 references) (Author/MCO)
SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool

PubMed Central

Zi, Zhike; Zheng, Yanan; Rundell, Ann E; Klipp, Edda

2008-01-01

Background It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models. Results This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface. Conclusion SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes. PMID:18706080
SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool.

PubMed

Zi, Zhike; Zheng, Yanan; Rundell, Ann E; Klipp, Edda

2008-08-15

It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models. This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface. SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.

Low Fidelity Imitation of Atypical Biological Kinematics in Autism Spectrum Disorders Is Modulated by Self-Generated Selective Attention.

PubMed

Hayes, Spencer J; Andrew, Matthew; Elliott, Digby; Gowen, Emma; Bennett, Simon J

2016-02-01

We examined whether adults with autism had difficulty imitating atypical biological kinematics. To reduce the impact that higher-order processes have on imitation we used a non-human agent model to control social attention, and removed end-state target goals in half of the trials to minimise goal-directed attention. Findings showed that only neurotypical adults imitated atypical biological kinematics. Adults with autism did, however, become significantly more accurate at imitating movement time. This confirmed they engaged in the task, and that sensorimotor adaptation was self-regulated. The attentional bias to movement time suggests the attenuation in imitating kinematics might be a compensatory strategy due to deficits in lower-level visuomotor processes associated with self-other mapping, or selective attention modulated the processes that represent biological kinematics.
Process Relationships for Evaluating the Role of Light-induced Inactivation of Enterococci at Selected Beaches and Nearby Tributaries of the Great Lakes

EPA Science Inventory

One approach to predictive modeling of biological contamination of recreational waters and drinking water sources involves applying process-based models that consider microbial sources, hydrodynamic transport, and microbial fate. Fecal indicator bacteria such as enterococci have ...
Cost modeling of biocontrol strains Pseudomonas chlororaphis and P. flurorescens for competitive exclusion of Salmonella enterica on tomatoes

USDA-ARS?s Scientific Manuscript database

Biological control of foodborne pathogens may complement postharvest intervention measures to enhance food safety of minimally processed produce. The purpose of this research was to develop cost model estimates for application of competitive exclusion process (CEM) using Pseudomonas chlororaphis and...
High-resolution modeling of a marine ecosystem using the FRESCO hydroecological model

NASA Astrophysics Data System (ADS)

Zalesny, V. B.; Tamsalu, R.

2009-02-01

The FRESCO (Finnish Russian Estonian Cooperation) mathematical model describing a marine hydroecosystem is presented. The methodology of the numerical solution is based on the method of multicomponent splitting into physical and biological processes, spatial coordinates, etc. The model is used for the reproduction of physical and biological processes proceeding in the Baltic Sea. Numerical experiments are performed with different spatial resolutions for four marine basins that are enclosed into one another: the Baltic Sea, the Gulf of Finland, the Tallinn-Helsinki water area, and Tallinn Bay. Physical processes are described by the equations of nonhydrostatic dynamics, including the k-ω parametrization of turbulence. Biological processes are described by the three-dimensional equations of an aquatic ecosystem with the use of a size-dependent parametrization of biochemical reactions. The main goal of this study is to illustrate the efficiency of the developed numerical technique and to demonstrate the importance of a high spatial resolution for water basins that have complex bottom topography, such as the Baltic Sea. Detailed information about the atmospheric forcing, bottom topography, and coastline is very important for the description of coastal dynamics and specific features of a marine ecosystem. Experiments show that the spatial inhomogeneity of hydroecosystem fields is caused by the combined effect of upwelling, turbulent mixing, surface-wave breaking, and temperature variations, which affect biochemical reactions.
Completing and Adapting Models of Biological Processes

NASA Technical Reports Server (NTRS)

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

2006-01-01

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

NASA Astrophysics Data System (ADS)

Ponce Dawson, Silvina

2003-03-01

In spite of their relatively small sizes, cells are incredibly complex objects in which various sorts of self-organizing processes occur. Cell division is an example of a process that nearly all cells undergo in which a concerted sequence of events takes place. What are the signals that tell the cell to move along this sequence? Clearly, this is a self-organized process. Microtubules (long polymers that are part of the cytoskeleton) and calcium signals play a major role during cell division. In this course we will focus on some features of microtubule dynamics and calcium signals that are amenable to modeling. In both of these biological systems, behaviors at a single molecule level are key determinants of the self-organized dynamics that is observed at larger scales. Thus, the modeling of these systems presents interesting challenges which require novel strategies. Their study may not only provide answers for biologically motivated questions, but is also a natural setting in which the transition between particle-like and mean-field models can be explored. In this course we will first give a brief biological introduction on the structure of eukaryotic cells, microtubule dynamics and intracellular calcium signals. We will then describe some of the models that have been presented in the literature and discuss the spatio-temporal dynamics that they predict, comparing them with observed behaviors in vitro or in vivo. We will end with a discussion on the virtues and limitations of the various modeling strategies described.
Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns

PubMed Central

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning. PMID:29209191
Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

PubMed

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.
Specifications of Standards in Systems and Synthetic Biology.

PubMed

Schreiber, Falk; Bader, Gary D; Golebiewski, Martin; Hucka, Michael; Kormeier, Benjamin; Le Novère, Nicolas; Myers, Chris; Nickerson, David; Sommer, Björn; Waltemath, Dagmar; Weise, Stephan

2015-09-04

Standards shape our everyday life. From nuts and bolts to electronic devices and technological processes, standardised products and processes are all around us. Standards have technological and economic benefits, such as making information exchange, production, and services more efficient. However, novel, innovative areas often either lack proper standards, or documents about standards in these areas are not available from a centralised platform or formal body (such as the International Standardisation Organisation). Systems and synthetic biology is a relatively novel area, and it is only in the last decade that the standardisation of data, information, and models related to systems and synthetic biology has become a community-wide effort. Several open standards have been established and are under continuous development as a community initiative. COMBINE, the ‘COmputational Modeling in BIology’ NEtwork has been established as an umbrella initiative to coordinate and promote the development of the various community standards and formats for computational models. There are yearly two meeting, HARMONY (Hackathons on Resources for Modeling in Biology), Hackathon-type meetings with a focus on development of the support for standards, and COMBINE forums, workshop-style events with oral presentations, discussion, poster, and breakout sessions for further developing the standards. For more information see http://co.mbine.org/. So far the different standards were published and made accessible through the standards’ web- pages or preprint services. The aim of this special issue is to provide a single, easily accessible and citable platform for the publication of standards in systems and synthetic biology. This special issue is intended to serve as a central access point to standards and related initiatives in systems and synthetic biology, it will be published annually to provide an opportunity for standard development groups to communicate updated specifications.
From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

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

PubMed

Dosta, J; Galí, A; Benabdallah El-Hadj, T; Macé, S; Mata-Alvarez, J

2007-08-01

The aim of this study was the operation and model description of a sequencing batch reactor (SBR) for biological nitrogen removal (BNR) from a reject water (800-900 mg NH(4)(+)-NL(-1)) from a municipal wastewater treatment plant (WWTP). The SBR was operated with three cycles per day, temperature 30 degrees C, SRT 11 days and HRT 1 day. During the operational cycle, three alternating oxic/anoxic periods were performed to avoid alkalinity restrictions. Oxygen supply and working pH range were controlled to achieve the BNR via nitrite, which makes the process more economical. Under steady state conditions, a total nitrogen removal of 0.87 kg N (m(3)day)(-1) was reached. A four-step nitrogen removal model was developed to describe the process. This model enlarges the IWA activated sludge models for a more detailed description of the nitrogen elimination processes and their inhibitions. A closed intermittent-flow respirometer was set up for the estimation of the most relevant model parameters. Once calibrated, model predictions reproduced experimental data accurately.
A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level.

PubMed

Gasqui, Patrick; Trommenschlager, Jean-Marie

2017-08-21

Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.
GERMcode: A Stochastic Model for Space Radiation Risk Assessment

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Ponomarev, Artem L.; Cucinotta, Francis A.

2012-01-01

A new computer model, the GCR Event-based Risk Model code (GERMcode), was developed to describe biophysical events from high-energy protons and high charge and energy (HZE) particles that have been studied at the NASA Space Radiation Laboratory (NSRL) for the purpose of simulating space radiation biological effects. In the GERMcode, the biophysical description of the passage of HZE particles in tissue and shielding materials is made with a stochastic approach that includes both particle track structure and nuclear interactions. The GERMcode accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections. For NSRL applications, the GERMcode evaluates a set of biophysical properties, such as the Poisson distribution of particles or delta-ray hits for a given cellular area and particle dose, the radial dose on tissue, and the frequency distribution of energy deposition in a DNA volume. By utilizing the ProE/Fishbowl ray-tracing analysis, the GERMcode will be used as a bi-directional radiation transport model for future spacecraft shielding analysis in support of Mars mission risk assessments. Recent radiobiological experiments suggest the need for new approaches to risk assessment that include time-dependent biological events due to the signaling times for activation and relaxation of biological processes in cells and tissue. Thus, the tracking of the temporal and spatial distribution of events in tissue is a major goal of the GERMcode in support of the simulation of biological processes important in GCR risk assessments. In order to validate our approach, basic radiobiological responses such as cell survival curves, mutation, chromosomal aberrations, and representative mouse tumor induction curves are implemented into the GERMcode. Extension of these descriptions to other endpoints related to non-targeted effects and biochemical pathway responses will be discussed.
Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6

USGS Publications Warehouse

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

2016-01-01

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

NASA Astrophysics Data System (ADS)

Zeng, Shaoqun; Zhang, Zhihong; Zhou, Wei; Luo, Qingming

2002-04-01

Green fluorescent protein (GFP) transfected Hela cell was planted in naked mice, to construct an in vivo model for monitoring the therapeutic effect of laser hyperthermia in real time. A cooled CCD fluorescence imaging system was used to record the tumor fluorescence image during the hyperthermia process. Primary experimental results were presented in this paper. To make sure the fluorescent probe GFP does not have strong effect on the biologic function of the host tumor cell (Hela cell), several conventional biological processes were observed in real time. First, neurons, which are much more tender than tumor cells, were transfected with GFP (cameleons). No morphological inhomogenities were observed, and normal functional responses of the neurons were observed when stimulated with histamine. In the second step, the mitosis process of cultured Hela cell was monitored. The features observed during mitosis confirmed that the transfection does not ruin the mitosis process of the tumor cell. At last, naked mice with tumor cell was constructed, which emit fluorescence in the tumor region when excited with faint laser. This presentation provides an in vivo biological model for quick monitoring of the therapeutic results of tumor hyperthermia.
Conway's “Game of Life” and the Epigenetic Principle

PubMed Central

Caballero, Lorena; Hodge, Bob; Hernandez, Sergio

2016-01-01

Cellular automatons and computer simulation games are widely used as heuristic devices in biology, to explore implications and consequences of specific theories. Conway's Game of Life has been widely used for this purpose. This game was designed to explore the evolution of ecological communities. We apply it to other biological processes, including symbiopoiesis. We show that Conway's organization of rules reflects the epigenetic principle, that genetic action and developmental processes are inseparable dimensions of a single biological system, analogous to the integration processes in symbiopoiesis. We look for similarities and differences between two epigenetic models, by Turing and Edelman, as they are realized in Game of Life objects. We show the value of computer simulations to experiment with and propose generalizations of broader scope with novel testable predictions. We use the game to explore issues in symbiopoiesis and evo-devo, where we explore a fractal hypothesis: that self-similarity exists at different levels (cells, organisms, ecological communities) as a result of homologous interactions of two as processes modeled in the Game of Life PMID:27379213
A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization.

PubMed

Rajaei, Karim; Khaligh-Razavi, Seyed-Mahdi; Ghodrati, Masoud; Ebrahimpour, Reza; Shiri Ahmad Abadi, Mohammad Ebrahim

2012-01-01

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.
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
Computer modeling in developmental biology: growing today, essential tomorrow.

PubMed

Sharpe, James

2017-12-01

D'Arcy Thompson was a true pioneer, applying mathematical concepts and analyses to the question of morphogenesis over 100 years ago. The centenary of his famous book, On Growth and Form , is therefore a great occasion on which to review the types of computer modeling now being pursued to understand the development of organs and organisms. Here, I present some of the latest modeling projects in the field, covering a wide range of developmental biology concepts, from molecular patterning to tissue morphogenesis. Rather than classifying them according to scientific question, or scale of problem, I focus instead on the different ways that modeling contributes to the scientific process and discuss the likely future of modeling in developmental biology. © 2017. Published by The Company of Biologists Ltd.
[Biology and culture: a dimension of collaboration between anthropology and epidemiology].

PubMed

Song, Leiming; Wang, Ning

2016-01-01

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

[The use of biological age on mental work capacity model in accelerated aging assessment of professional lorry-drivers].

PubMed

Bashkireva, A S

2012-01-01

The studies of biological age, aging rate, mental work capacity in professional drivers were conducted. The examination revealed peculiarities of system organization of functions determining the mental work capacity levels. Dynamics of the aging process of professional driver's organism in relation with calendar age and driving experience were shown using the biological age model. The results point at the premature decrease of the mental work capacity in professional drivers. It was proved, that premature age-related changes of physiologic and psychophysiologic indices in drivers are just "risk indicators", while long driving experience is a real risk factor, accelerating the aging process. The "risk group" with manifestations of accelerating aging was observed in 40-49-year old drivers with 15-19 years of professional experience. The expediency of using the following methods for the age rate estimation according to biologic age indices and necessity of prophylactic measures for premature and accelerated aging prevention among working population was demonstrated.
Quantum Information Biology: From Information Interpretation of Quantum Mechanics to Applications in Molecular Biology and Cognitive Psychology

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2015-10-01

We discuss foundational issues of quantum information biology (QIB)—one of the most successful applications of the quantum formalism outside of physics. QIB provides a multi-scale model of information processing in bio-systems: from proteins and cells to cognitive and social systems. This theory has to be sharply distinguished from "traditional quantum biophysics". The latter is about quantum bio-physical processes, e.g., in cells or brains. QIB models the dynamics of information states of bio-systems. We argue that the information interpretation of quantum mechanics (its various forms were elaborated by Zeilinger and Brukner, Fuchs and Mermin, and D' Ariano) is the most natural interpretation of QIB. Biologically QIB is based on two principles: (a) adaptivity; (b) openness (bio-systems are fundamentally open). These principles are mathematically represented in the framework of a novel formalism— quantum adaptive dynamics which, in particular, contains the standard theory of open quantum systems.
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.
Adaptive filtering in biological signal processing.

PubMed

Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A

1990-01-01

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.
Multiscale Systems Analysis of Root Growth and Development: Modeling Beyond the Network and Cellular Scales

PubMed Central

Band, Leah R.; Fozard, John A.; Godin, Christophe; Jensen, Oliver E.; Pridmore, Tony; Bennett, Malcolm J.; King, John R.

2012-01-01

Over recent decades, we have gained detailed knowledge of many processes involved in root growth and development. However, with this knowledge come increasing complexity and an increasing need for mechanistic modeling to understand how those individual processes interact. One major challenge is in relating genotypes to phenotypes, requiring us to move beyond the network and cellular scales, to use multiscale modeling to predict emergent dynamics at the tissue and organ levels. In this review, we highlight recent developments in multiscale modeling, illustrating how these are generating new mechanistic insights into the regulation of root growth and development. We consider how these models are motivating new biological data analysis and explore directions for future research. This modeling progress will be crucial as we move from a qualitative to an increasingly quantitative understanding of root biology, generating predictive tools that accelerate the development of improved crop varieties. PMID:23110897
An advanced environment for hybrid modeling of biological systems based on modelica.

PubMed

Pross, Sabrina; Bachmann, Bernhard

2011-01-20

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

PubMed

An, Gary C

2010-01-01

The greatest challenge facing the biomedical research community is the effective translation of basic mechanistic knowledge into clinically effective therapeutics. This challenge is most evident in attempts to understand and modulate "systems" processes/disorders, such as sepsis, cancer, and wound healing. Formulating an investigatory strategy for these issues requires the recognition that these are dynamic processes. Representation of the dynamic behavior of biological systems can aid in the investigation of complex pathophysiological processes by augmenting existing discovery procedures by integrating disparate information sources and knowledge. This approach is termed Translational Systems Biology. Focusing on the development of computational models capturing the behavior of mechanistic hypotheses provides a tool that bridges gaps in the understanding of a disease process by visualizing "thought experiments" to fill those gaps. Agent-based modeling is a computational method particularly well suited to the translation of mechanistic knowledge into a computational framework. Utilizing agent-based models as a means of dynamic hypothesis representation will be a vital means of describing, communicating, and integrating community-wide knowledge. The transparent representation of hypotheses in this dynamic fashion can form the basis of "knowledge ecologies," where selection between competing hypotheses will apply an evolutionary paradigm to the development of community knowledge.
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/
The Layer-Oriented Approach to Declarative Languages for Biological Modeling

PubMed Central

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language. PMID:22615554
The layer-oriented approach to declarative languages for biological modeling.

PubMed

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.
EVALUATION OF MULTIPLE PHARMACOKINETIC MODELING STRUCTURES FOR TRICHLOROETHYLENE

EPA Science Inventory

A series of PBPK models were developed for trichloroethylene (TCE) to evaluate biological processes that may affect the absorption, distribution, metabolism and excretion (ADME) of TCE and its metabolites.
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.
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.
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.
The Scientist as Illustrator.

PubMed

Iwasa, Janet H

2016-04-01

Proficiency in art and illustration was once considered an essential skill for biologists, because text alone often could not suffice to describe observations of biological systems. With modern imaging technology, it is no longer necessary to illustrate what we can see by eye. However, in molecular and cellular biology, our understanding of biological processes is dependent on our ability to synthesize diverse data to generate a hypothesis. Creating visual models of these hypotheses is important for generating new ideas and for communicating to our peers and to the public. Here, I discuss the benefits of creating visual models in molecular and cellular biology and consider steps to enable researchers to become more effective visual communicators. Copyright © 2016 Elsevier Ltd. All rights reserved.
A program in global biology. [biota-environment interaction important to life

NASA Technical Reports Server (NTRS)

Mooneyhan, D. W.

1983-01-01

NASA's Global Biology Research Program and its goals for greater understanding of planetary biological processes are discussed. Consideration is given to assessing major pathways and rates of exchange of elements such as carbon and nitrogen, extrapolating local rates of anaerobic activities, determining exchange rates of ocean nutrients, and developing models for the global cycles of carbon, nitrogen, sulfur, and phosphorus. Satellites and sensors operating today are covered: the Nimbus, NOAA, and Landsat series. Block diagrams of the software and hardware for a typical ground data processing and analysis system are provided. Samples of the surface cover data achieved with the Advanced Very High Resolution Radiometer, the Multispectral Scanner, and the Thematic Mapper are presented, as well as a productive capacity model for coastal wetlands. Finally, attention is given to future goals, their engineering requirements, and the necessary data analysis system.
Glycoengineering in CHO Cells: Advances in Systems Biology.

PubMed

Tejwani, Vijay; Andersen, Mikael R; Nam, Jong Hyun; Sharfstein, Susan T

2018-03-01

For several decades, glycoprotein biologics have been successfully produced from Chinese hamster ovary (CHO) cells. The therapeutic efficacy and potency of glycoprotein biologics are often dictated by their post-translational modifications, particularly glycosylation, which unlike protein synthesis, is a non-templated process. Consequently, both native and recombinant glycoprotein production generate heterogeneous mixtures containing variable amounts of different glycoforms. Stability, potency, plasma half-life, and immunogenicity of the glycoprotein biologic are directly influenced by the glycoforms. Recently, CHO cells have also been explored for production of therapeutic glycosaminoglycans (e.g., heparin), which presents similar challenges as producing glycoproteins biologics. Approaches to controlling heterogeneity in CHO cells and directing the biosynthetic process toward desired glycoforms are not well understood. A systems biology approach combining different technologies is needed for complete understanding of the molecular processes accounting for this variability and to open up new venues in cell line development. In this review, we describe several advances in genetic manipulation, modeling, and glycan and glycoprotein analysis that together will provide new strategies for glycoengineering of CHO cells with desired or enhanced glycosylation capabilities. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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.
A unified partial likelihood approach for X-chromosome association on time-to-event outcomes.

PubMed

Xu, Wei; Hao, Meiling

2018-02-01

The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E), and skewed X-chromosome inactivation (XCI-S). Although these expressions are included in various predesigned genetic variation chip platforms, the X-chromosome has generally been excluded from the majority of genome-wide association studies analyses; this is most likely due to the lack of a standardized method in handling X-chromosomal genotype data. To analyze the X-linked genetic association for time-to-event outcomes with the actual process unknown, we propose a unified approach of maximizing the partial likelihood over all of the potential biological processes. The proposed method can be used to infer the true biological process and derive unbiased estimates of the genetic association parameters. A partial likelihood ratio test statistic that has been proved asymptotically chi-square distributed can be used to assess the X-chromosome genetic association. Furthermore, if the X-chromosome expression pertains to the XCI-S process, we can infer the correct skewed direction and magnitude of inactivation, which can elucidate significant findings regarding the genetic mechanism. A population-level model and a more general subject-level model have been developed to model the XCI-S process. Finite sample performance of this novel method is examined via extensive simulation studies. An application is illustrated with implementation of the method on a cancer genetic study with survival outcome. © 2017 WILEY PERIODICALS, INC.
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

A neural model of hierarchical reinforcement learning.

PubMed

Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.
A Parallel Distributed Processing Approach to Behavior and Biology in Schizophrenia

DTIC Science & Technology

1989-10-01

delusions) and the other that reflects dopamine underactivity (negative symptoms - e.g., avolition, amotivation and withdrawal). Several authors have... amotivation . While both may be related to frontal lobe Behavior and Biology in Schizophrenia Cohen and Servan-Schreiber 32 deficits, the models in their
Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects.

PubMed

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

2010-10-24

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

PubMed

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

2018-01-01

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

PubMed Central

Chen, Yang; Shen, Kuang

2017-01-01

To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process. PMID:28943680
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.
Autodyne effect in a single-mode Er fibre laser and the possibility of its usage for recognising the evaporated biotissue type

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

Dmitriev, A K; Konovalov, A N; Ul'yanov, V A

2015-12-31

The autodyne signal arising in an Er fibre laser in the course of evaporating biological models of different types is studied and the possibility of recognising the biotissue type using the method of autodyne detection of the backscattered Doppler signal is assessed. In the experiments we modelled the process of surgical intervention using the contact (hole perforation with the Er laser fibre) and noncontact (surface evaporation with the focused radiation) regimes of impact on different biological models. The amplitude – frequency characteristic of the autodyne detection for the Er fibre laser is measured and the initial spectra of the backscatteredmore » Doppler signal arising under the action of laser radiation on the samples of biological models are obtained. The experiments have shown that the spectra of the backscattered Doppler signal, arising in the course of the contact and noncontact action of the Er fibre laser on different biological models, demonstrate clear-cut distinctions. (control of laser radiation parameters)« less
Advances in distributed watershed modeling: a review and application of the AgroEcoSystem-Watershed (AgES-W) model

USDA-ARS?s Scientific Manuscript database

Progress in the understanding of physical, chemical, and biological processes influencing water quality, coupled with advancements in the collection and analysis of hydrologic data, provide opportunities for significant innovations in the manner and level with which watershed-scale processes may be ...
Challenges and progress in distributed watershed modeling: applications of the AgroEcoSystem-Watershed (AgES-W) model

USDA-ARS?s Scientific Manuscript database

Progress in the understanding of physical, chemical, and biological processes influencing water quality, coupled with advances in the collection and analysis of hydrologic data, provide opportunities for significant innovations in the manner and level with which watershed-scale processes may be quan...
Ramping up to the Biology Workbench: A Multi-Stage Approach to Bioinformatics Education

ERIC Educational Resources Information Center

Greene, Kathleen; Donovan, Sam

2005-01-01

In the process of designing and field-testing bioinformatics curriculum materials, we have adopted a three-stage, progressive model that emphasizes collaborative scientific inquiry. The elements of the model include: (1) context setting, (2) introduction to concepts, processes, and tools, and (3) development of competent use of technologically…
Systems biology of cellular membranes: a convergence with biophysics.

PubMed

Chabanon, Morgan; Stachowiak, Jeanne C; Rangamani, Padmini

2017-09-01

Systems biology and systems medicine have played an important role in the last two decades in shaping our understanding of biological processes. While systems biology is synonymous with network maps and '-omics' approaches, it is not often associated with mechanical processes. Here, we make the case for considering the mechanical and geometrical aspects of biological membranes as a key step in pushing the frontiers of systems biology of cellular membranes forward. We begin by introducing the basic components of cellular membranes, and highlight their dynamical aspects. We then survey the functions of the plasma membrane and the endomembrane system in signaling, and discuss the role and origin of membrane curvature in these diverse cellular processes. We further give an overview of the experimental and modeling approaches to study membrane phenomena. We close with a perspective on the converging futures of systems biology and membrane biophysics, invoking the need to include physical variables such as location and geometry in the study of cellular membranes. WIREs Syst Biol Med 2017, 9:e1386. doi: 10.1002/wsbm.1386 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.
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.
The dynamics of correlated novelties.

PubMed

Tria, F; Loreto, V; Servedio, V D P; Strogatz, S H

2014-07-31

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called "expanding the adjacent possible". The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.
The dynamics of correlated novelties

NASA Astrophysics Data System (ADS)

Tria, F.; Loreto, V.; Servedio, V. D. P.; Strogatz, S. H.

2014-07-01

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called ``expanding the adjacent possible''. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.
The dynamics of correlated novelties

PubMed Central

Tria, F.; Loreto, V.; Servedio, V. D. P.; Strogatz, S. H.

2014-01-01

Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called “expanding the adjacent possible”. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution. PMID:25080941
A mechano-biological model of multi-tissue evolution in bone

NASA Astrophysics Data System (ADS)

Frame, Jamie; Rohan, Pierre-Yves; Corté, Laurent; Allena, Rachele

2017-12-01

Successfully simulating tissue evolution in bone is of significant importance in predicting various biological processes such as bone remodeling, fracture healing and osseointegration of implants. Each of these processes involves in different ways the permanent or transient formation of different tissue types, namely bone, cartilage and fibrous tissues. The tissue evolution in specific circumstances such as bone remodeling and fracturing healing is currently able to be modeled. Nevertheless, it remains challenging to predict which tissue types and organization can develop without any a priori assumptions. In particular, the role of mechano-biological coupling in this selective tissue evolution has not been clearly elucidated. In this work, a multi-tissue model has been created which simultaneously describes the evolution of bone, cartilage and fibrous tissues. The coupling of the biological and mechanical factors involved in tissue formation has been modeled by defining two different tissue states: an immature state corresponding to the early stages of tissue growth and representing cell clusters in a weakly neo-formed Extra Cellular Matrix (ECM), and a mature state corresponding to well-formed connective tissues. This has allowed for the cellular processes of migration, proliferation and apoptosis to be described simultaneously with the changing ECM properties through strain driven diffusion, growth, maturation and resorption terms. A series of finite element simulations were carried out on idealized cantilever bending geometries. Starting from a tissue composition replicating a mid-diaphysis section of a long bone, a steady-state tissue formation was reached over a statically loaded period of 10,000 h (60 weeks). The results demonstrated that bone formation occurred in regions which are optimally physiologically strained. In two additional 1000 h bending simulations both cartilaginous and fibrous tissues were shown to form under specific geometrical and loading cases and cartilage was shown to lead to the formation of bone in a beam replicating a fracture healing initial tissue distribution. This finding is encouraging in that it is corroborated by similar experimental observations of cartilage leading bone formation during the fracture healing process. The results of this work demonstrate that a multi-tissue mechano-biological model of tissue evolution has the potential for predictive analysis in the design and implementations of implants, describing fracture healing and bone remodeling processes.
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.
Development of a GCR Event-based Risk Model

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

Stochastic model of template-directed elongation processes in biology.

PubMed

Schilstra, Maria J; Nehaniv, Chrystopher L

2010-10-01

We present a novel modular, stochastic model for biological template-based linear chain elongation processes. In this model, elongation complexes (ECs; DNA polymerase, RNA polymerase, or ribosomes associated with nascent chains) that span a finite number of template units step along the template, one after another, with semaphore constructs preventing overtaking. The central elongation module is readily extended with modules that represent initiation and termination processes. The model was used to explore the effect of EC span on motor velocity and dispersion, and the effect of initiation activator and repressor binding kinetics on the overall elongation dynamics. The results demonstrate that (1) motors that move smoothly are able to travel at a greater velocity and closer together than motors that move more erratically, and (2) the rate at which completed chains are released is proportional to the occupancy or vacancy of activator or repressor binding sites only when initiation or activator/repressor dissociation is slow in comparison with elongation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
BioModels Database: a repository of mathematical models of biological processes.

PubMed

Chelliah, Vijayalakshmi; Laibe, Camille; Le Novère, Nicolas

2013-01-01

BioModels Database is a public online resource that allows storing and sharing of published, peer-reviewed quantitative, dynamic models of biological processes. The model components and behaviour are thoroughly checked to correspond the original publication and manually curated to ensure reliability. Furthermore, the model elements are annotated with terms from controlled vocabularies as well as linked to relevant external data resources. This greatly helps in model interpretation and reuse. Models are stored in SBML format, accepted in SBML and CellML formats, and are available for download in various other common formats such as BioPAX, Octave, SciLab, VCML, XPP and PDF, in addition to SBML. The reaction network diagram of the models is also available in several formats. BioModels Database features a search engine, which provides simple and more advanced searches. Features such as online simulation and creation of smaller models (submodels) from the selected model elements of a larger one are provided. BioModels Database can be accessed both via a web interface and programmatically via web services. New models are available in BioModels Database at regular releases, about every 4 months.
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.
Towards the understanding of network information processing in biology

NASA Astrophysics Data System (ADS)

Singh, Vijay

Living organisms perform incredibly well in detecting a signal present in the environment. This information processing is achieved near optimally and quite reliably, even though the sources of signals are highly variable and complex. The work in the last few decades has given us a fair understanding of how individual signal processing units like neurons and cell receptors process signals, but the principles of collective information processing on biological networks are far from clear. Information processing in biological networks, like the brain, metabolic circuits, cellular-signaling circuits, etc., involves complex interactions among a large number of units (neurons, receptors). The combinatorially large number of states such a system can exist in makes it impossible to study these systems from the first principles, starting from the interactions between the basic units. The principles of collective information processing on such complex networks can be identified using coarse graining approaches. This could provide insights into the organization and function of complex biological networks. Here I study models of biological networks using continuum dynamics, renormalization, maximum likelihood estimation and information theory. Such coarse graining approaches identify features that are essential for certain processes performed by underlying biological networks. We find that long-range connections in the brain allow for global scale feature detection in a signal. These also suppress the noise and remove any gaps present in the signal. Hierarchical organization with long-range connections leads to large-scale connectivity at low synapse numbers. Time delays can be utilized to separate a mixture of signals with temporal scales. Our observations indicate that the rules in multivariate signal processing are quite different from traditional single unit signal processing.
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
A practical approach for exploration and modeling of the design space of a bacterial vaccine cultivation process.

PubMed

Streefland, M; Van Herpen, P F G; Van de Waterbeemd, B; Van der Pol, L A; Beuvery, E C; Tramper, J; Martens, D E; Toft, M

2009-10-15

A licensed pharmaceutical process is required to be executed within the validated ranges throughout the lifetime of product manufacturing. Changes to the process, especially for processes involving biological products, usually require the manufacturer to demonstrate that the safety and efficacy of the product remains unchanged by new or additional clinical testing. Recent changes in the regulations for pharmaceutical processing allow broader ranges of process settings to be submitted for regulatory approval, the so-called process design space, which means that a manufacturer can optimize his process within the submitted ranges after the product has entered the market, which allows flexible processes. In this article, the applicability of this concept of the process design space is investigated for the cultivation process step for a vaccine against whooping cough disease. An experimental design (DoE) is applied to investigate the ranges of critical process parameters that still result in a product that meets specifications. The on-line process data, including near infrared spectroscopy, are used to build a descriptive model of the processes used in the experimental design. Finally, the data of all processes are integrated in a multivariate batch monitoring model that represents the investigated process design space. This article demonstrates how the general principles of PAT and process design space can be applied for an undefined biological product such as a whole cell vaccine. The approach chosen for model development described here, allows on line monitoring and control of cultivation batches in order to assure in real time that a process is running within the process design space.
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.
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
The model of drugs distribution dynamics in biological tissue

NASA Astrophysics Data System (ADS)

Ginevskij, D. A.; Izhevskij, P. V.; Sheino, I. N.

2017-09-01

The dose distribution by Neutron Capture Therapy follows the distribution of 10B in the tissue. The modern models of pharmacokinetics of drugs describe the processes occurring in conditioned "chambers" (blood-organ-tumor), but fail to describe the spatial distribution of the drug in the tumor and in normal tissue. The mathematical model of the spatial distribution dynamics of drugs in the tissue, depending on the concentration of the drug in the blood, was developed. The modeling method is the representation of the biological structure in the form of a randomly inhomogeneous medium in which the 10B distribution occurs. The parameters of the model, which cannot be determined rigorously in the experiment, are taken as the quantities subject to the laws of the unconnected random processes. The estimates of 10B distribution preparations in the tumor and healthy tissue, inside/outside the cells, are obtained.
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.
Birth/birth-death processes and their computable transition probabilities with biological applications.

PubMed

Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A

2018-03-01

Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.
Biologically inspired information theory: Adaptation through construction of external reality models by living systems.

PubMed

Nakajima, Toshiyuki

2015-12-01

Higher animals act in the world using their external reality models to cope with the uncertain environment. Organisms that have not developed such information-processing organs may also have external reality models built in the form of their biochemical, physiological, and behavioral structures, acquired by natural selection through successful models constructed internally. Organisms subject to illusions would fail to survive in the material universe. How can organisms, or living systems in general, determine the external reality from within? This paper starts with a phenomenological model, in which the self constitutes a reality model developed through the mental processing of phenomena. Then, the it-from-bit concept is formalized using a simple mathematical model. For this formalization, my previous work on an algorithmic process is employed to constitute symbols referring to the external reality, called the inverse causality, with additional improvements to the previous work. Finally, as an extension of this model, the cognizers system model is employed to describe the self as one of many material entities in a world, each of which acts as a subject by responding to the surrounding entities. This model is used to propose a conceptual framework of information theory that can deal with both the qualitative (semantic) and quantitative aspects of the information involved in biological processes. Copyright © 2015 Elsevier Ltd. All rights reserved.
Exploring an Alternative Model of Human Reproductive Capability: A Creative Learning Activity

ERIC Educational Resources Information Center

Cherif, Abour H.; Jedlicka, Dianne M.

2012-01-01

Biological and social evolutionary processes, along with social and cultural developments, have allowed humans to separate procreation from pleasurable/recreational sexual activity. As a class learning project, an alternative, hypothetical reproductive scenario is presented: "What if humans were biologically ready to conceive only during one…
BioASF: a framework for automatically generating executable pathway models specified in BioPAX.

PubMed

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

2016-06-15

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

NASA Astrophysics Data System (ADS)

Hülse, Dominik; Arndt, Sandra; Ridgwell, Andy; Wilson, Jamie

2016-04-01

The ocean-sediment system, as the biggest carbon reservoir in the Earth's carbon cycle, plays a crucial role in regulating atmospheric carbon dioxide concentrations and climate. Therefore, it is essential to constrain the importance of marine carbon cycle feedbacks on global warming and ocean acidification. Arguably, the most important single component of the ocean's carbon cycle is the so-called "biological carbon pump". It transports carbon that is fixed in the light-flooded surface layer of the ocean to the deep ocean and the surface sediment, where it is degraded/dissolved or finally buried in the deep sediments. Over the past decade, progress has been made in understanding different factors that control the efficiency of the biological carbon pump and their feedbacks on the global carbon cycle and climate (i.e. ballasting = ocean acidification feedback; temperature dependant organic matter degradation = global warming feedback; organic matter sulphurisation = anoxia/euxinia feedback). Nevertheless, many uncertainties concerning the interplay of these processes and/or their relative significance remain. In addition, current Earth System Models tend to employ empirical and static parameterisations of the biological pump. As these parametric representations are derived from a limited set of present-day observations, their ability to represent carbon cycle feedbacks under changing climate conditions is limited. The aim of my research is to combine past carbon cycling information with a spatially resolved global biogeochemical model to constrain the functioning of the biological pump and to base its mathematical representation on a more mechanistic approach. Here, I will discuss important aspects that control the efficiency of the ocean's biological carbon pump, review how these processes of first order importance are mathematically represented in existing Earth system Models of Intermediate Complexity (EMIC) and distinguish different approaches to approximate biogeochemical processes in the sediments. The performance of the respective mathematical representations in constraining the importance of carbon pump feedbacks on marine biogeochemical dynamics is then compared and evaluated under different extreme climate scenarios (e.g. OAE2, Eocene) using the Earth system model 'GENIE' and proxy records. The compiled mathematical descriptions and the model results underline the lack of a complete and mechanistic framework to represent the short-term carbon cycle in most EMICs which seriously limits the ability of these models to constrain the response of the ocean's carbon cycle to past and in particular future climate change. In conclusion, this presentation will critically evaluate the approaches currently used in marine biogeochemical modelling and outline key research directions concerning model development in the future.
Old knowledge and new technologies allow rapid development of model organisms

PubMed Central

Cook, Charles E.; Chenevert, Janet; Larsson, Tomas A.; Arendt, Detlev; Houliston, Evelyn; Lénárt, Péter

2016-01-01

Until recently the set of “model” species used commonly for cell biology was limited to a small number of well-understood organisms, and developing a new model was prohibitively expensive or time-consuming. With the current rapid advances in technology, in particular low-cost high-throughput sequencing, it is now possible to develop molecular resources fairly rapidly. Wider sampling of biological diversity can only accelerate progress in addressing cellular mechanisms and shed light on how they are adapted to varied physiological contexts. Here we illustrate how historical knowledge and new technologies can reveal the potential of nonconventional organisms, and we suggest guidelines for selecting new experimental models. We also present examples of nonstandard marine metazoan model species that have made important contributions to our understanding of biological processes. PMID:26976934
Concepts in Cancer Modeling: A Brief History

PubMed Central

Thomas, Renee M.; Van Dyke, Terry; Merlino, Glenn; Day, Chi-Ping

2016-01-01

Modeling, an experimental approach to investigate complex biological systems, has significantly contributed to our understanding of cancer. While extensive cancer research has been conducted utilizing animal models for elucidating mechanisms and developing therapeutics, the concepts in a good model design and its application have not been well elaborated. In this review, we discuss the theory underlying biological modeling and the process of producing a valuable and relevant animal model. Several renowned examples in the history of cancer research will be used to illustrate how modeling can be translatable to clinical applications. Finally, we will also discuss how the advances in cancer genomics and cancer modeling will influence each other going forward. PMID:27694601
Kramers problem in evolutionary strategies

NASA Astrophysics Data System (ADS)

Dunkel, J.; Ebeling, W.; Schimansky-Geier, L.; Hänggi, P.

2003-06-01

We calculate the escape rates of different dynamical processes for the case of a one-dimensional symmetric double-well potential. In particular, we compare the escape rates of a Smoluchowski process, i.e., a corresponding overdamped Brownian motion dynamics in a metastable potential landscape, with the escape rates obtained for a biologically motivated model known as the Fisher-Eigen process. The main difference between the two models is that the dynamics of the Smoluchowski process is determined by local quantities, whereas the Fisher-Eigen process is based on a global coupling (nonlocal interaction). If considered in the context of numerical optimization algorithms, both processes can be interpreted as archetypes of physically or biologically inspired evolutionary strategies. In this sense, the results discussed in this work are utile in order to evaluate the efficiency of such strategies with regard to the problem of surmounting various barriers. We find that a combination of both scenarios, starting with the Fisher-Eigen strategy, provides a most effective evolutionary strategy.

Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development

PubMed Central

Chatgilialoglu, Chryssostomos; Ferreri, Carla; Masi, Annalisa; Melchiorre, Michele; Sansone, Anna; Terzidis, Michael A.; Torreggiani, Armida

2013-01-01

The involvement of free radicals in life sciences has constantly increased with time and has been connected to several physiological and pathological processes. This subject embraces diverse scientific areas, spanning from physical, biological and bioorganic chemistry to biology and medicine, with applications to the amelioration of quality of life, health and aging. Multidisciplinary skills are required for the full investigation of the many facets of radical processes in the biological environment and chemical knowledge plays a crucial role in unveiling basic processes and mechanisms. We developed a chemical biology approach able to connect free radical chemical reactivity with biological processes, providing information on the mechanistic pathways and products. The core of this approach is the design of biomimetic models to study biomolecule behavior (lipids, nucleic acids and proteins) in aqueous systems, obtaining insights of the reaction pathways as well as building up molecular libraries of the free radical reaction products. This context can be successfully used for biomarker discovery and examples are provided with two classes of compounds: mono-trans isomers of cholesteryl esters, which are synthesized and used as references for detection in human plasma, and purine 5',8-cyclo-2'-deoxyribonucleosides, prepared and used as reference in the protocol for detection of such lesions in DNA samples, after ionizing radiations or obtained from different health conditions. PMID:23629513
Mathematical Simulation of the Process of Aerobic Treatment of Wastewater under Conditions of Diffusion and Mass Transfer Perturbations

NASA Astrophysics Data System (ADS)

Bomba, A. Ya.; Safonik, A. P.

2018-05-01

A mathematical model of the process of aerobic treatment of wastewater has been refined. It takes into account the interaction of bacteria, as well as of organic and biologically nonoxidizing substances under conditions of diffusion and mass transfer perturbations. An algorithm of the solution of the corresponding nonlinear perturbed problem of convection-diffusion-mass transfer type has been constructed, with a computer experiment carried out based on it. The influence of the concentration of oxygen and of activated sludge on the quality of treatment is shown. Within the framework of the model suggested, a possibility of automated control of the process of deposition of impurities in a biological filter depending on the initial parameters of the water medium is suggested.
Mathematical Simulation of the Process of Aerobic Treatment of Wastewater under Conditions of Diffusion and Mass Transfer Perturbations

NASA Astrophysics Data System (ADS)

Bomba, A. Ya.; Safonik, A. P.

2018-03-01

A mathematical model of the process of aerobic treatment of wastewater has been refined. It takes into account the interaction of bacteria, as well as of organic and biologically nonoxidizing substances under conditions of diffusion and mass transfer perturbations. An algorithm of the solution of the corresponding nonlinear perturbed problem of convection-diffusion-mass transfer type has been constructed, with a computer experiment carried out based on it. The influence of the concentration of oxygen and of activated sludge on the quality of treatment is shown. Within the framework of the model suggested, a possibility of automated control of the process of deposition of impurities in a biological filter depending on the initial parameters of the water medium is suggested.
Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

PubMed

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

2014-01-15

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

PubMed

Thomas, Frédéric; Fisher, Daniel; Fort, Philippe; Marie, Jean-Pierre; Daoust, Simon; Roche, Benjamin; Grunau, Christoph; Cosseau, Céline; Mitta, Guillaume; Baghdiguian, Stephen; Rousset, François; Lassus, Patrice; Assenat, Eric; Grégoire, Damien; Missé, Dorothée; Lorz, Alexander; Billy, Frédérique; Vainchenker, William; Delhommeau, François; Koscielny, Serge; Itzykson, Raphael; Tang, Ruoping; Fava, Fanny; Ballesta, Annabelle; Lepoutre, Thomas; Krasinska, Liliana; Dulic, Vjekoslav; Raynaud, Peggy; Blache, Philippe; Quittau-Prevostel, Corinne; Vignal, Emmanuel; Trauchessec, Hélène; Perthame, Benoit; Clairambault, Jean; Volpert, Vitali; Solary, Eric; Hibner, Urszula; Hochberg, Michael E

2013-01-01

Since the mid 1970s, cancer has been described as a process of Darwinian evolution, with somatic cellular selection and evolution being the fundamental processes leading to malignancy and its many manifestations (neoangiogenesis, evasion of the immune system, metastasis, and resistance to therapies). Historically, little attention has been placed on applications of evolutionary biology to understanding and controlling neoplastic progression and to prevent therapeutic failures. This is now beginning to change, and there is a growing international interest in the interface between cancer and evolutionary biology. The objective of this introduction is first to describe the basic ideas and concepts linking evolutionary biology to cancer. We then present four major fronts where the evolutionary perspective is most developed, namely laboratory and clinical models, mathematical models, databases, and techniques and assays. Finally, we discuss several of the most promising challenges and future prospects in this interdisciplinary research direction in the war against cancer.
SEEK: a systems biology data and model management platform.

PubMed

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

2015-07-11

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

PubMed

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

2018-05-12

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

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; Friedrichs, Marjorie A. M.; Robinson, Allan R.; Rose, Kenneth A.; Schlitzer, Reiner; Thompson, Keith R.; Doney, Scott C.

2008-01-01

There is growing recognition that rigorous skill assessment is required to understand the ability of ocean biological models to represent ocean processes and distributions. Statistical analysis of model results with observations represents the most quantitative form of skill assessment, and this principle serves as well for data assimilation models. However, skill assessment for data assimilation requires special consideration. This is because there are three sets of information in the free-run model, data, and the assimilation model, which uses Data assimilation information from both the flee-run model and the data. Intercom parison of results among the three sets of information is important and useful for assessment, but is not conclusive since the three information sets are intertwined. An independent data set is necessary for an objective determination. Other useful measures of ocean biological data assimilation assessment include responses of unassimilated variables to the data assimilation, performance outside the prescribed region/time of interest, forecasting, and trend analysis. Examples of each approach from the literature are provided. A comprehensive list of ocean biological data assimilation and their applications of skill assessment, in both ecosystem/biogeochemical and fisheries efforts, is summarized.
VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine).

PubMed

Wong, Darren C J; Sweetman, Crystal; Drew, Damian P; Ford, Christopher M

2013-12-16

Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis) whereby the recovered sub-networks reconfirm established plant gene functions and also identify novel associations. Together, we present valuable insights into grapevine transcriptional regulation by developing network models applicable to researchers in their prioritisation of gene candidates, for on-going study of biological processes related to grapevine development, metabolism and stress responses.
Improvements in continuum modeling for biomolecular systems

NASA Astrophysics Data System (ADS)

Yu, Qiao; Ben-Zhuo, Lu

2016-01-01

Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson- Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of the biological systems. Considerable efforts have been made to improve the continuum model to account for discrete particle interactions and to make progress in numerical methods to provide accurate and efficient simulations. This review will summarize recent main improvements in continuum modeling for biomolecular systems, with focus on the size-modified models, the coupling of the classical density functional theory and the PNP equations, the coupling of polar and nonpolar interactions, and numerical progress. Project supported by the National Natural Science Foundation of China (Grant No. 91230106) and the Chinese Academy of Sciences Program for Cross & Cooperative Team of the Science & Technology Innovation.
Understanding a Basic Biological Process: Expert and Novice Models of Meiosis.

ERIC Educational Resources Information Center

Kindfield, Ann C. H.

The results of a study of the meiosis models utilized by individuals at varying levels of expertise while reasoning about the process of meiosis are presented. Based on these results, the issues of sources of misconceptions/difficulties and the construction of a sound understanding of meiosis are discussed. Five individuals from each of three…
Introducing memory and association mechanism into a biologically inspired visual model.

PubMed

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

2014-09-01

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

PubMed

Karvounis, E C; Exarchos, T P; Fotiou, E; Sakellarios, A I; Iliopoulou, D; Koutsouris, D; Fotiadis, D I

2013-01-01

With an ever increasing number of biological models available on the internet, a standardized modelling framework is required to allow information to be accessed and visualized. In this paper we propose a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of geometry, blood flow, plaque progression and stent modelling, exported by any cardiovascular disease modelling software. ART-ML has been developed and tested using ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. The images and their content are fused to develop morphological models of the arteries in 3D representations. All the above described procedures integrate disparate data formats, protocols and tools. ART-ML proposes a representation way, expanding ARTool, for interpretability of the individual resources, creating a standard unified model for the description of data and, consequently, a format for their exchange and representation that is machine independent. More specifically, ARTool platform incorporates efficient algorithms which are able to perform blood flow simulations and atherosclerotic plaque evolution modelling. Integration of data layers between different modules within ARTool are based upon the interchange of information included in the ART-ML model repository. ART-ML provides a markup representation that enables the representation and management of embedded models within the cardiovascular disease modelling platform, the storage and interchange of well-defined information. The corresponding ART-ML model incorporates all relevant information regarding geometry, blood flow, plaque progression and stent modelling procedures. All created models are stored in a model repository database which is accessible to the research community using efficient web interfaces, enabling the interoperability of any cardiovascular disease modelling software models. ART-ML can be used as a reference ML model in multiscale simulations of plaque formation and progression, incorporating all scales of the biological processes.
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.
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351
Biology-Culture Co-evolution in Finite Populations.

PubMed

de Boer, Bart; Thompson, Bill

2018-01-19

Language is the result of two concurrent evolutionary processes: biological and cultural inheritance. An influential evolutionary hypothesis known as the moving target problem implies inherent limitations on the interactions between our two inheritance streams that result from a difference in pace: the speed of cultural evolution is thought to rule out cognitive adaptation to culturally evolving aspects of language. We examine this hypothesis formally by casting it as as a problem of adaptation in time-varying environments. We present a mathematical model of biology-culture co-evolution in finite populations: a generalisation of the Moran process, treating co-evolution as coupled non-independent Markov processes, providing a general formulation of the moving target hypothesis in precise probabilistic terms. Rapidly varying culture decreases the probability of biological adaptation. However, we show that this effect declines with population size and with stronger links between biology and culture: in realistically sized finite populations, stochastic effects can carry cognitive specialisations to fixation in the face of variable culture, especially if the effects of those specialisations are amplified through cultural evolution. These results support the view that language arises from interactions between our two major inheritance streams, rather than from one primary evolutionary process that dominates another.
A Case Study Documenting the Process by Which Biology Instructors Transition from Teacher-Centered to Learner-Centered Teaching

PubMed Central

Marbach-Ad, Gili; Hunt Rietschel, Carly

2016-01-01

In this study, we used a case study approach to obtain an in-depth understanding of the change process of two university instructors who were involved with redesigning a biology course. Given the hesitancy of many biology instructors to adopt evidence-based, learner-centered teaching methods, there is a critical need to understand how biology instructors transition from teacher-centered (i.e., lecture-based) instruction to teaching that focuses on the students. Using the innovation-decision model for change, we explored the motivation, decision-making, and reflective processes of the two instructors through two consecutive, large-enrollment biology course offerings. Our data reveal that the change process is somewhat unpredictable, requiring patience and persistence during inevitable challenges that arise for instructors and students. For example, the change process requires instructors to adopt a teacher-facilitator role as opposed to an expert role, to cover fewer course topics in greater depth, and to give students a degree of control over their own learning. Students must adjust to taking responsibility for their own learning, working collaboratively, and relinquishing the anonymity afforded by lecture-based teaching. We suggest implications for instructors wishing to change their teaching and administrators wishing to encourage adoption of learner-centered teaching at their institutions. PMID:27856550
Interannual variability of primary production and air-sea CO2 flux in the Atlantic and Indian sectors of the Southern Ocean.

NASA Astrophysics Data System (ADS)

Dufour, Carolina; Merlivat, Liliane; Le Sommer, Julien; Boutin, Jacqueline; Antoine, David

2013-04-01

As one of the major oceanic sinks of anthropogenic CO2, the Southern Ocean plays a critical role in the climate system. However, due to the scarcity of observations, little is known about physical and biological processes that control air-sea CO2 fluxes and how these processes might respond to climate change. It is well established that primary production is one of the major drivers of air-sea CO2 fluxes, consuming surface Dissolved Inorganic Carbon (DIC) during Summer. Southern Ocean primary production is though constrained by several limiting factors such as iron and light availability, which are both sensitive to mixed layer depth. Mixed layer depth is known to be affected by current changes in wind stress or freshwater fluxes over the Southern Ocean. But we still don't know how primary production may respond to anomalous mixed layer depth neither how physical processes may balance this response to set the seasonal cycle of air-sea CO2 fluxes. In this study, we investigate the impact of anomalous mixed layer depth on surface DIC in the Atlantic and Indian sectors of the Subantarctic zone of the Southern Ocean (60W-60E, 38S-55S) with a combination of in situ data, satellite data and model experiment. We use both a regional eddy permitting ocean biogeochemical model simulation based on NEMO-PISCES and data-based reconstruction of biogeochemical fields based on CARIOCA buoys and SeaWiFS data. A decomposition of the physical and biological processes driving the seasonal variability of surface DIC is performed with both the model data and observations. A good agreement is found between the model and the data for the amplitude of biological and air-sea flux contributions. The model data are further used to investigate the impact of winter and summer anomalies in mixed layer depth on surface DIC over the period 1990-2004. The relative changes of each physical and biological process contribution are quantified and discussed.
Modeling and simulation of an aquatic habitat for bioregenerative life support research

NASA Astrophysics Data System (ADS)

Drayer, Gregorio E.; Howard, Ayanna M.

2014-01-01

Long duration human spaceflight poses challenges for spacecraft autonomy and the regeneration of life support consumables, such as oxygen and water. Bioregenerative life support systems (BLSS), which make use of biological processes to transform biological byproducts back into consumables, have the ability to recycle organic byproducts and are the preferred option for food production. A limitation in BLSS research is in the non-availability of small-scale experimental capacities that may help to better understand the challenges in system closure, integration, and control. Ground-based aquatic habitats are an option for small-scale research relevant to bioregenerative life support systems (BLSS), given that they can operate as self-contained systems enclosing a habitat composed of various species in a single volume of water. The purpose of this paper is to present the modeling and simulation of a reconfigurable aquatic habitat for experiments in regenerative life support automation; it supports the use of aquatic habitats as a small-scale approach to experiments relevant to larger-scale regenerative life support systems. It presents ground-based aquatic habitats as an option for small-scale BLSS research focusing on the process of respiration, and elaborates on the description of biological processes by introducing models of ecophysiological phenomena for consumers and producers: higher plants of the species Bacopa monnieri produce O2 for snails of the genus Pomacea; the snails consume O2 and generate CO2, which is used by the plants in combination with radiant energy to generate O2 through the process of photosynthesis. Feedback controllers are designed to regulate the concentration of dissolved oxygen in the water. This paper expands the description of biological processes by introducing models of ecophysiological phenomena of the organisms involved. The model of the plants includes a description of the rate of CO2 assimilation as a function of irradiance. Simulations and validation runs with hardware show how these phenomena may act as disturbances to the control mechanisms that maintain safe concentration levels of dissolved oxygen in the habitat.
Mathematical modeling of gene expression: a guide for the perplexed biologist

PubMed Central

Ay, Ahmet; Arnosti, David N.

2011-01-01

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean and differential equation models we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems. PMID:21417596

Coupled Modeling of Rhizosphere and Reactive Transport Processes

NASA Astrophysics Data System (ADS)

Roque-Malo, S.; Kumar, P.

2017-12-01

The rhizosphere, as a bio-diverse plant root-soil interface, hosts many hydrologic and biochemical processes, including nutrient cycling, hydraulic redistribution, and soil carbon dynamics among others. The biogeochemical function of root networks, including the facilitation of nutrient cycling through absorption and rhizodeposition, interaction with micro-organisms and fungi, contribution to biomass, etc., plays an important role in myriad Critical Zone processes. Despite this knowledge, the role of the rhizosphere on watershed-scale ecohydrologic functions in the Critical Zone has not been fully characterized, and specifically, the extensive capabilities of reactive transport models (RTMs) have not been applied to these hydrobiogeochemical dynamics. This study uniquely links rhizospheric processes with reactive transport modeling to couple soil biogeochemistry, biological processes, hydrologic flow, hydraulic redistribution, and vegetation dynamics. Key factors in the novel modeling approach are: (i) bi-directional effects of root-soil interaction, such as simultaneous root exudation and nutrient absorption; (ii) multi-state biomass fractions in soil (i.e. living, dormant, and dead biological and root materials); (iii) expression of three-dimensional fluxes to represent both vertical and lateral interconnected flows and processes; and (iv) the potential to include the influence of non-stationary external forcing and climatic factors. We anticipate that the resulting model will demonstrate the extensive effects of plant root dynamics on ecohydrologic functions at the watershed scale and will ultimately contribute to a better characterization of efflux from both agricultural and natural systems.
What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633
Improving the forecast for biodiversity under climate change.

PubMed

Urban, M C; Bocedi, G; Hendry, A P; Mihoub, J-B; Pe'er, G; Singer, A; Bridle, J R; Crozier, L G; De Meester, L; Godsoe, W; Gonzalez, A; Hellmann, J J; Holt, R D; Huth, A; Johst, K; Krug, C B; Leadley, P W; Palmer, S C F; Pantel, J H; Schmitz, A; Zollner, P A; Travis, J M J

2016-09-09

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity. Copyright © 2016, American Association for the Advancement of Science.
Linking growth and yield and process models to estimate impact of environmental changes on growth of loblolly pine

Treesearch

V. Clark Baldwin; Harold E. Burkhart; James A. Westfall; Kelly D. Peterson

2001-01-01

PTAEDA2 is a distance-dependent, individual tree model that simulates the growth and yield of a plantation of loblolly pine (Pinus taeda L.)on an annual basis. The MAESTRO model utilizes an array of trees in a stand to calculate and integrate the effects of biological and physical variables on the photosynthesis and respiration processes of a target...
Processing of Visual Imagery by an Adaptive Model of the Visual System: Its Performance and its Significance. Final Report, June 1969-March 1970.

ERIC Educational Resources Information Center

Tallman, Oliver H.

A digital simulation of a model for the processing of visual images is derived from known aspects of the human visual system. The fundamental principle of computation suggested by a biological model is a transformation that distributes information contained in an input stimulus everywhere in a transform domain. Each sensory input contributes under…
Agent-Based Phytoplankton Models of Cellular and Population Processes: Fostering Individual-Based Learning in Undergraduate Research

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

PubMed

Yates, Christian A

2014-06-07

The incorporation of domain growth into stochastic models of biological processes is of increasing interest to mathematical modellers and biologists alike. In many situations, especially in developmental biology, the growth of the underlying tissue domain plays an important role in the redistribution of particles (be they cells or molecules) which may move and react atop the domain. Although such processes have largely been modelled using deterministic, continuum models there is an increasing appetite for individual-based stochastic models which can capture the fine details of the biological movement processes which are being elucidated by modern experimental techniques, and also incorporate the inherent stochasticity of such systems. In this work we study a simple stochastic model of domain growth. From a basic version of this model, Hywood et al. (2013) were able to derive a Fokker-Plank equation (FPE) (in this case an advection-diffusion partial differential equation on a growing domain) which describes the evolution of the probability density of some tracer particles on the domain. We extend their work so that a variety of different domain growth mechanisms can be incorporated and demonstrate a good agreement between the mean tracer density and the solution of the FPE in each case. In addition we incorporate domain shrinkage (via element death) into our individual-level model and demonstrate that we are able to derive coefficients for the FPE in this case as well. For situations in which the drift and diffusion coefficients are not readily available we introduce a numerical coefficient estimation approach and demonstrate the accuracy of this approach by comparing it with situations in which an analytical solution is obtainable. Copyright © 2014 Elsevier Ltd. All rights reserved.
A theory of biological relativity: no privileged level of causation.

PubMed

Noble, Denis

2012-02-06

Must higher level biological processes always be derivable from lower level data and mechanisms, as assumed by the idea that an organism is completely defined by its genome? Or are higher level properties necessarily also causes of lower level behaviour, involving actions and interactions both ways? This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes. These insights are then generalized. A priori, there is no privileged level of causation. The relations between this form of 'biological relativity' and forms of relativity in physics are discussed. Biological relativity can be seen as an extension of the relativity principle by avoiding the assumption that there is a privileged scale at which biological functions are determined.
A theory of biological relativity: no privileged level of causation

PubMed Central

Noble, Denis

2012-01-01

Must higher level biological processes always be derivable from lower level data and mechanisms, as assumed by the idea that an organism is completely defined by its genome? Or are higher level properties necessarily also causes of lower level behaviour, involving actions and interactions both ways? This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes. These insights are then generalized. A priori, there is no privileged level of causation. The relations between this form of ‘biological relativity’ and forms of relativity in physics are discussed. Biological relativity can be seen as an extension of the relativity principle by avoiding the assumption that there is a privileged scale at which biological functions are determined. PMID:23386960
Recent Developments in the Application of Biologically Inspired Computation to Chemical Sensing

NASA Astrophysics Data System (ADS)

Marco, S.; Gutierrez-Gálvez, A.

2009-05-01

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. In this work, the state of the art concerning biologically inspired computation for chemical sensing will be reviewed. Instead of reviewing the whole body of computational neuroscience of olfaction, we restrict this review to the application of models to the processing of real chemical sensor data.
Brain age and other bodily 'ages': implications for neuropsychiatry.

PubMed

Cole, James H; Marioni, Riccardo E; Harris, Sarah E; Deary, Ian J

2018-06-11

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.
From bottles to stream reaches and networks: Consequences of scale in how we interpret the function of freshwaters in the carbon cycle

NASA Astrophysics Data System (ADS)

Hotchkiss, E. R.

2017-12-01

Freshwater biological processes can alter the quantity and quality of organic carbon (OC) inputs from land before they are transported downstream, but the relative role of hydrologic transport and in-stream processing is still not well quantified at the scale of fluvial networks. Despite much research on the role of biology and hydrology in governing the form and fate of C in inland waters, conclusions about the function of freshwater ecosystems in modifying OC still largely depend on where we draw our ecosystem boundaries, i.e., the spatial scale of measurements used to assess OC transformations. Here I review freshwater OC uptake rates derived from bioassay incubations, synoptic modeling, reach-scale experiments, and ecosystem OC spiraling estimates. Median OC uptake velocities from standard bioassay incubations (0.02 m/d) and synoptic modeling (0.04 m/d) are 1-2 orders of magnitude lower than reach-scale experimental DOC additions and ecosystem OC spiraling estimates (2.2 and 0.27 m/d, respectively) in streams and rivers. Together, ecosystem metabolism and OC fluxes can be used to estimate the distance OC travels before being consumed and respired as CO2 through biological processes (i.e., OC spiraling), allowing for a more mechanistic understanding of the role of ecosystem processes and hydrologic fluxes in modifying downstream OC transport. Beyond the reach scale, data from stream network and stream-lake-river modeling simulations show how we may use linked sampling sites within networks to better understand the integrated sources and fate of OC in freshwaters. We currently underestimate the role of upstream processes in contributing to downstream fluxes: moving from single-ecosystem comparisons to linked-ecosystem simulations increases the contribution of in situ OC processing to CO2 emissions from 30% to >40%. Insights from literature reviews, ecosystem process measurements, and model simulations provide a framework for future considerations of integrated C transport, transformations, and fate when scaling patterns and processes in inland waters.
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.
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
CellML metadata standards, associated tools and repositories

PubMed Central

Beard, Daniel A.; Britten, Randall; Cooling, Mike T.; Garny, Alan; Halstead, Matt D.B.; Hunter, Peter J.; Lawson, James; Lloyd, Catherine M.; Marsh, Justin; Miller, Andrew; Nickerson, David P.; Nielsen, Poul M.F.; Nomura, Taishin; Subramanium, Shankar; Wimalaratne, Sarala M.; Yu, Tommy

2009-01-01

The development of standards for encoding mathematical models is an important component of model building and model sharing among scientists interested in understanding multi-scale physiological processes. CellML provides such a standard, particularly for models based on biophysical mechanisms, and a substantial number of models are now available in the CellML Model Repository. However, there is an urgent need to extend the current CellML metadata standard to provide biological and biophysical annotation of the models in order to facilitate model sharing, automated model reduction and connection to biological databases. This paper gives a broad overview of a number of new developments on CellML metadata and provides links to further methodological details available from the CellML website. PMID:19380315
Using Osteoclast Differentiation as a Model for Gene Discovery in an Undergraduate Cell Biology Laboratory

ERIC Educational Resources Information Center

Birnbaum, Mark J.; Picco, Jenna; Clements, Meghan; Witwicka, Hanna; Yang, Meiheng; Hoey, Margaret T.; Odgren, Paul R.

2010-01-01

A key goal of molecular/cell biology/biotechnology is to identify essential genes in virtually every physiological process to uncover basic mechanisms of cell function and to establish potential targets of drug therapy combating human disease. This article describes a semester-long, project-oriented molecular/cellular/biotechnology laboratory…
Students' Usability Evaluation of a Web-Based Tutorial Program for College Biology Problem Solving

ERIC Educational Resources Information Center

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

2015-01-01

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

ERIC Educational Resources Information Center

Castera, Jeremy; Bruguiere, Catherine; Clement, Pierre

2008-01-01

The presentation of genetic diseases in French secondary school biology textbooks is analysed to determine the major conceptions taught in the field of human genetics. References to genetic diseases, and the processes by which they are explained (monogeny, polygeny, chromosomal anomaly and environmental influence) are studied in recent French…
Spatial Patterns in the Efficiency of the Biological Pump: What Controls Export Ratios at the Global Scale?

NASA Astrophysics Data System (ADS)

Moore, J. K.

2016-02-01

The efficiency of the biological pump is influenced by complex interactions between chemical, biological, and physical processes. The efficiency of export out of surface waters and down through the water column to the deep ocean has been linked to a number of factors including biota community composition, production of mineral ballast components, physical aggregation and disaggregation processes, and ocean oxygen concentrations. I will examine spatial patterns in the export ratio and the efficiency of the biological pump at the global scale using the Community Earth System Model (CESM). There are strong spatial variations in the export efficiency as simulated by the CESM, which are strongly correlated with new nutrient inputs to the euphotic zone and their impacts on phytoplankton community structure. I will compare CESM simulations that include dynamic, variable export ratios driven by the phytoplankton community structure, with simulations that impose a near-constant export ratio to examine the effects of export efficiency on nutrient and surface chlorophyll distributions. The model predicted export ratios will also be compared with recent satellite-based estimates.
Rosen's (M,R) system in process algebra.

PubMed

Gatherer, Derek; Galpin, Vashti

2013-11-17

Robert Rosen's Metabolism-Replacement, or (M,R), system can be represented as a compact network structure with a single source and three products derived from that source in three consecutive reactions. (M,R) has been claimed to be non-reducible to its components and algorithmically non-computable, in the sense of not being evaluable as a function by a Turing machine. If (M,R)-like structures are present in real biological networks, this suggests that many biological networks will be non-computable, with implications for those branches of systems biology that rely on in silico modelling for predictive purposes. We instantiate (M,R) using the process algebra Bio-PEPA, and discuss the extent to which our model represents a true realization of (M,R). We observe that under some starting conditions and parameter values, stable states can be achieved. Although formal demonstration of algorithmic computability remains elusive for (M,R), we discuss the extent to which our Bio-PEPA representation of (M,R) allows us to sidestep Rosen's fundamental objections to computational systems biology. We argue that the behaviour of (M,R) in Bio-PEPA shows life-like properties.

Using simple manipulatives to improve student comprehension of a complex biological process: protein synthesis.

PubMed

Guzman, Karen; Bartlett, John

2012-01-01

Biological systems and living processes involve a complex interplay of biochemicals and macromolecular structures that can be challenging for undergraduate students to comprehend and, thus, misconceptions abound. Protein synthesis, or translation, is an example of a biological process for which students often hold many misconceptions. This article describes an exercise that was developed to illustrate the process of translation using simple objects to represent complex molecules. Animations, 3D physical models, computer simulations, laboratory experiments and classroom lectures are also used to reinforce the students' understanding of translation, but by focusing on the simple manipulatives in this exercise, students are better able to visualize concepts that can elude them when using the other methods. The translation exercise is described along with suggestions for background material, questions used to evaluate student comprehension and tips for using the manipulatives to identify common misconceptions. Copyright © 2012 Wiley Periodicals, Inc.
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
Comparative techno-economic analysis of steam explosion, dilute sulfuric acid, ammonia fiber explosion and biological pretreatments of corn stover.

PubMed

Baral, Nawa Raj; Shah, Ajay

2017-05-01

Pretreatment is required to destroy recalcitrant structure of lignocelluloses and then transform into fermentable sugars. This study assessed techno-economics of steam explosion, dilute sulfuric acid, ammonia fiber explosion and biological pretreatments, and identified bottlenecks and operational targets for process improvement. Techno-economic models of these pretreatment processes for a cellulosic biorefinery of 113.5 million liters butanol per year excluding fermentation and wastewater treatment sections were developed using a modelling software-SuperPro Designer. Experimental data of the selected pretreatment processes based on corn stover were gathered from recent publications, and used for this analysis. Estimated sugar production costs ($/kg) via steam explosion, dilute sulfuric acid, ammonia fiber explosion and biological methods were 0.43, 0.42, 0.65 and 1.41, respectively. The results suggest steam explosion and sulfuric acid pretreatment methods might be good alternatives at present state of technology and other pretreatment methods require research and development efforts to be competitive with these pretreatment methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

PubMed Central

Cai, Yizhi; Lux, Matthew W.; Adam, Laura; Peccoud, Jean

2009-01-01

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology. PMID:19816554
Assessing Understanding of Biological Processes: Elucidating Students' Models of Meiosis.

ERIC Educational Resources Information Center

Kindfield, Ann C.

1994-01-01

Presents a meiosis reasoning problem that provides direct access to students' current models of chromosomes and meiosis. Also included in the article are tips for classroom implementation and a summary of the solution evaluation. (ZWH)
QUANTITATIVE PROCEDURES FOR NEUROTOXICOLOGY RISK ASSESSMENT

EPA Science Inventory

In this project, previously published information on biologically based dose-response model for brain development was used to quantitatively evaluate critical neurodevelopmental processes, and to assess potential chemical impacts on early brain development. This model has been ex...
Self-organized pattern dynamics of somitogenesis model in embryos

NASA Astrophysics Data System (ADS)

Guan, Linan; Shen, Jianwei

2018-09-01

Somitogenesis, the sequential formation of a periodic pattern along the anteroposterior axis of vertebrate embryos, is one of the most obvious examples of the segmental patterning processes that take place during embryogenesis and also one of the major unresolved events in developmental biology. In this paper, we investigate the effect of diffusion on pattern formation use a modified two dimensional model which can be used to explain somitogenesis during embryonic development. This model is suitable for exploring a design space of somitogenesis and can explain many aspects of somitogenesis that previous models cannot. In the present paper, by analyzing the local linear stability of the equation, we acquired the conditions of Hopf bifurcation and Turing bifurcation. In addition, the amplitude equation near the Turing bifurcation point is obtained by using the methods of multi-scale expansion and symmetry analysis. By analyzing the stability of the amplitude equation, we know that there are various complex phenomena, including Spot pattern, mixture of spot-stripe patterns and labyrinthine. Finally, numerical simulation are given to verify the correctness of our theoretical results. Somitogenesis occupies an important position in the process of biological development, and as a pattern process can be used to investigate many aspects of embryogenesis. Therefore, our study helps greatly to cell differentiation, gene expression and embryonic development. What is more, it is of great significance for the diagnosis and treatment of human diseases to study the related knowledge of model biology.
Agent Based Modeling Applications for Geosciences

NASA Astrophysics Data System (ADS)

Stein, J. S.

2004-12-01

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

PubMed

Stadler, Tanja

2009-11-07

The constant rate birth-death process is used as a stochastic model for many biological systems, for example phylogenies or disease transmission. As the biological data are usually not fully available, it is crucial to understand the effect of incomplete sampling. In this paper, we analyze the constant rate birth-death process with incomplete sampling. We derive the density of the bifurcation events for trees on n leaves which evolved under this birth-death-sampling process. This density is used for calculating prior distributions in Bayesian inference programs and for efficiently simulating trees. We show that the birth-death-sampling process can be interpreted as a birth-death process with reduced rates and complete sampling. This shows that joint inference of birth rate, death rate and sampling probability is not possible. The birth-death-sampling process is compared to the sampling-based population genetics model, the coalescent. It is shown that despite many similarities between these two models, the distribution of bifurcation times remains different even in the case of very large population sizes. We illustrate these findings on an Hepatitis C virus dataset from Egypt. We show that the transmission times estimates are significantly different-the widely used Gamma statistic even changes its sign from negative to positive when switching from the coalescent to the birth-death process.
Modeling and simulation of viscoelastic biological particles' 3D manipulation using atomic force microscopy

NASA Astrophysics Data System (ADS)

Korayem, M. H.; Habibi Sooha, Y.; Rastegar, Z.

2018-05-01

Manipulation of the biological particles by atomic force microscopy is used to transfer these particles inside body's cells, diagnosis and destruction of the cancer cells and drug delivery to damaged cells. According to the impossibility of simultaneous observation of this process, the importance of modeling and simulation can be realized. The contact of the tip with biological particle is important during manipulation, therefore, the first step of the modeling is choosing appropriate contact model. Most of the studies about contact between atomic force microscopy and biological particles, consider the biological particle as an elastic material. This is not an appropriate assumption because biological cells are basically soft and this assumption ignores loading history. In this paper, elastic and viscoelastic JKR theories were used in modeling and simulation of the 3D manipulation for three modes of tip-particle sliding, particle-substrate sliding and particle-substrate rolling. Results showed that critical force and time in motion modes (sliding and rolling) for two elastic and viscoelastic states are very close but these magnitudes were lower in the viscoelastic state. Then, three friction models, Coulomb, LuGre and HK, were used for tip-particle sliding mode in the first phase of manipulation to make results closer to reality. In both Coulomb and LuGre models, critical force and time are very close for elastic and viscoelastic states but in general critical force and time prediction of HK model was higher than LuGre and the LuGre model itself had higher prediction than Coulomb.
Virtual Reconstruction and Three-Dimensional Printing of Blood Cells as a Tool in Cell Biology Education.

PubMed

Augusto, Ingrid; Monteiro, Douglas; Girard-Dias, Wendell; Dos Santos, Thaisa Oliveira; Rosa Belmonte, Simone Letícia; Pinto de Oliveira, Jairo; Mauad, Helder; da Silva Pacheco, Marcos; Lenz, Dominik; Stefanon Bittencourt, Athelson; Valentim Nogueira, Breno; Lopes Dos Santos, Jorge Roberto; Miranda, Kildare; Guimarães, Marco Cesar Cunegundes

2016-01-01

The cell biology discipline constitutes a highly dynamic field whose concepts take a long time to be incorporated into the educational system, especially in developing countries. Amongst the main obstacles to the introduction of new cell biology concepts to students is their general lack of identification with most teaching methods. The introduction of elaborated figures, movies and animations to textbooks has given a tremendous contribution to the learning process and the search for novel teaching methods has been a central goal in cell biology education. Some specialized tools, however, are usually only available in advanced research centers or in institutions that are traditionally involved with the development of novel teaching/learning processes, and are far from becoming reality in the majority of life sciences schools. When combined with the known declining interest in science among young people, a critical scenario may result. This is especially important in the field of electron microscopy and associated techniques, methods that have greatly contributed to the current knowledge on the structure and function of different cell biology models but are rarely made accessible to most students. In this work, we propose a strategy to increase the engagement of students into the world of cell and structural biology by combining 3D electron microscopy techniques and 3D prototyping technology (3D printing) to generate 3D physical models that accurately and realistically reproduce a close-to-the native structure of the cell and serve as a tool for students and teachers outside the main centers. We introduce three strategies for 3D imaging, modeling and prototyping of cells and propose the establishment of a virtual platform where different digital models can be deposited by EM groups and subsequently downloaded and printed in different schools, universities, research centers and museums, thereby modernizing teaching of cell biology and increasing the accessibility to modern approaches in basic science.
Virtual Reconstruction and Three-Dimensional Printing of Blood Cells as a Tool in Cell Biology Education

PubMed Central

Girard-Dias, Wendell; dos Santos, Thaisa Oliveira; Rosa Belmonte, Simone Letícia; Pinto de Oliveira, Jairo; Mauad, Helder; da Silva Pacheco, Marcos; Lenz, Dominik; Stefanon Bittencourt, Athelson; Valentim Nogueira, Breno; Lopes dos Santos, Jorge Roberto; Miranda, Kildare; Guimarães, Marco Cesar Cunegundes

2016-01-01

The cell biology discipline constitutes a highly dynamic field whose concepts take a long time to be incorporated into the educational system, especially in developing countries. Amongst the main obstacles to the introduction of new cell biology concepts to students is their general lack of identification with most teaching methods. The introduction of elaborated figures, movies and animations to textbooks has given a tremendous contribution to the learning process and the search for novel teaching methods has been a central goal in cell biology education. Some specialized tools, however, are usually only available in advanced research centers or in institutions that are traditionally involved with the development of novel teaching/learning processes, and are far from becoming reality in the majority of life sciences schools. When combined with the known declining interest in science among young people, a critical scenario may result. This is especially important in the field of electron microscopy and associated techniques, methods that have greatly contributed to the current knowledge on the structure and function of different cell biology models but are rarely made accessible to most students. In this work, we propose a strategy to increase the engagement of students into the world of cell and structural biology by combining 3D electron microscopy techniques and 3D prototyping technology (3D printing) to generate 3D physical models that accurately and realistically reproduce a close-to-the native structure of the cell and serve as a tool for students and teachers outside the main centers. We introduce three strategies for 3D imaging, modeling and prototyping of cells and propose the establishment of a virtual platform where different digital models can be deposited by EM groups and subsequently downloaded and printed in different schools, universities, research centers and museums, thereby modernizing teaching of cell biology and increasing the accessibility to modern approaches in basic science. PMID:27526196
Twelve testable hypotheses on the geobiology of weathering.

PubMed

Brantley, S L; Megonigal, J P; Scatena, F N; Balogh-Brunstad, Z; Barnes, R T; Bruns, M A; Van Cappellen, P; Dontsova, K; Hartnett, H E; Hartshorn, A S; Heimsath, A; Herndon, E; Jin, L; Keller, C K; Leake, J R; McDowell, W H; Meinzer, F C; Mozdzer, T J; Petsch, S; Pett-Ridge, J; Pregitzer, K S; Raymond, P A; Riebe, C S; Shumaker, K; Sutton-Grier, A; Walter, R; Yoo, K

2011-03-01

Critical Zone (CZ) research investigates the chemical, physical, and biological processes that modulate the Earth's surface. Here, we advance 12 hypotheses that must be tested to improve our understanding of the CZ: (1) Solar-to-chemical conversion of energy by plants regulates flows of carbon, water, and nutrients through plant-microbe soil networks, thereby controlling the location and extent of biological weathering. (2) Biological stoichiometry drives changes in mineral stoichiometry and distribution through weathering. (3) On landscapes experiencing little erosion, biology drives weathering during initial succession, whereas weathering drives biology over the long term. (4) In eroding landscapes, weathering-front advance at depth is coupled to surface denudation via biotic processes. (5) Biology shapes the topography of the Critical Zone. (6) The impact of climate forcing on denudation rates in natural systems can be predicted from models incorporating biogeochemical reaction rates and geomorphological transport laws. (7) Rising global temperatures will increase carbon losses from the Critical Zone. (8) Rising atmospheric P(CO2) will increase rates and extents of mineral weathering in soils. (9) Riverine solute fluxes will respond to changes in climate primarily due to changes in water fluxes and secondarily through changes in biologically mediated weathering. (10) Land use change will impact Critical Zone processes and exports more than climate change. (11) In many severely altered settings, restoration of hydrological processes is possible in decades or less, whereas restoration of biodiversity and biogeochemical processes requires longer timescales. (12) Biogeochemical properties impart thresholds or tipping points beyond which rapid and irreversible losses of ecosystem health, function, and services can occur. © 2011 Blackwell Publishing Ltd.
Models of Small-Scale Patchiness

NASA Technical Reports Server (NTRS)

McGillicuddy, D. J.

2001-01-01

Patchiness is perhaps the most salient characteristic of plankton populations in the ocean. The scale of this heterogeneity spans many orders of magnitude in its spatial extent, ranging from planetary down to microscale. It has been argued that patchiness plays a fundamental role in the functioning of marine ecosystems, insofar as the mean conditions may not reflect the environment to which organisms are adapted. Understanding the nature of this patchiness is thus one of the major challenges of oceanographic ecology. The patchiness problem is fundamentally one of physical-biological-chemical interactions. This interconnection arises from three basic sources: (1) ocean currents continually redistribute dissolved and suspended constituents by advection; (2) space-time fluctuations in the flows themselves impact biological and chemical processes, and (3) organisms are capable of directed motion through the water. This tripartite linkage poses a difficult challenge to understanding oceanic ecosystems: differentiation between the three sources of variability requires accurate assessment of property distributions in space and time, in addition to detailed knowledge of organismal repertoires and the processes by which ambient conditions control the rates of biological and chemical reactions. Various methods of observing the ocean tend to lie parallel to the axes of the space/time domain in which these physical-biological-chemical interactions take place. Given that a purely observational approach to the patchiness problem is not tractable with finite resources, the coupling of models with observations offers an alternative which provides a context for synthesis of sparse data with articulations of fundamental principles assumed to govern functionality of the system. In a sense, models can be used to fill the gaps in the space/time domain, yielding a framework for exploring the controls on spatially and temporally intermittent processes. The following discussion highlights only a few of the multitude of models which have yielded insight into the dynamics of plankton patchiness. In addition, this particular collection of examples is intended to furnish some exposure to the diversity of modeling approaches which can be brought to bear on the problem. These approaches range from abstract theoretical models intended to elucidate specific processes, to complex numerical formulations which can be used to actually simulate observed distributions in detail.
Simulations of Living Cell Origins Using a Cellular Automata Model

NASA Astrophysics Data System (ADS)

Ishida, Takeshi

2014-04-01

Understanding the generalized mechanisms of cell self-assembly is fundamental for applications in various fields, such as mass producing molecular machines in nanotechnology. Thus, the details of real cellular reaction networks and the necessary conditions for self-organized cells must be elucidated. We constructed a 2-dimensional cellular automata model to investigate the emergence of biological cell formation, which incorporated a looped membrane and a membrane-bound information system (akin to a genetic code and gene expression system). In particular, with an artificial reaction system coupled with a thermal system, the simultaneous formation of a looped membrane and an inner reaction process resulted in a more stable structure. These double structures inspired the primitive biological cell formation process from chemical evolution stage. With a model to simulate cellular self-organization in a 2-dimensional cellular automata model, 3 phenomena could be realized: (1) an inner reaction system developed as an information carrier precursor (akin to DNA); (2) a cell border emerged (akin to a cell membrane); and (3) these cell structures could divide into 2. This double-structured cell was considered to be a primary biological cell. The outer loop evolved toward a lipid bilayer membrane, and inner polymeric particles evolved toward precursor information carriers (evolved toward DNA). This model did not completely clarify all the necessary and sufficient conditions for biological cell self-organization. Further, our virtual cells remained unstable and fragile. However, the "garbage bag model" of Dyson proposed that the first living cells were deficient; thus, it would be reasonable that the earliest cells were more unstable and fragile than the simplest current unicellular organisms.
Simulations of living cell origins using a cellular automata model.

PubMed

Ishida, Takeshi

2014-04-01

Understanding the generalized mechanisms of cell self-assembly is fundamental for applications in various fields, such as mass producing molecular machines in nanotechnology. Thus, the details of real cellular reaction networks and the necessary conditions for self-organized cells must be elucidated. We constructed a 2-dimensional cellular automata model to investigate the emergence of biological cell formation, which incorporated a looped membrane and a membrane-bound information system (akin to a genetic code and gene expression system). In particular, with an artificial reaction system coupled with a thermal system, the simultaneous formation of a looped membrane and an inner reaction process resulted in a more stable structure. These double structures inspired the primitive biological cell formation process from chemical evolution stage. With a model to simulate cellular self-organization in a 2-dimensional cellular automata model, 3 phenomena could be realized: (1) an inner reaction system developed as an information carrier precursor (akin to DNA); (2) a cell border emerged (akin to a cell membrane); and (3) these cell structures could divide into 2. This double-structured cell was considered to be a primary biological cell. The outer loop evolved toward a lipid bilayer membrane, and inner polymeric particles evolved toward precursor information carriers (evolved toward DNA). This model did not completely clarify all the necessary and sufficient conditions for biological cell self-organization. Further, our virtual cells remained unstable and fragile. However, the "garbage bag model" of Dyson proposed that the first living cells were deficient; thus, it would be reasonable that the earliest cells were more unstable and fragile than the simplest current unicellular organisms.
Synthetic biology, inspired by synthetic chemistry.

PubMed

Malinova, V; Nallani, M; Meier, W P; Sinner, E K

2012-07-16

The topic synthetic biology appears still as an 'empty basket to be filled'. However, there is already plenty of claims and visions, as well as convincing research strategies about the theme of synthetic biology. First of all, synthetic biology seems to be about the engineering of biology - about bottom-up and top-down approaches, compromising complexity versus stability of artificial architectures, relevant in biology. Synthetic biology accounts for heterogeneous approaches towards minimal and even artificial life, the engineering of biochemical pathways on the organismic level, the modelling of molecular processes and finally, the combination of synthetic with nature-derived materials and architectural concepts, such as a cellular membrane. Still, synthetic biology is a discipline, which embraces interdisciplinary attempts in order to have a profound, scientific base to enable the re-design of nature and to compose architectures and processes with man-made matter. We like to give an overview about the developments in the field of synthetic biology, regarding polymer-based analogs of cellular membranes and what questions can be answered by applying synthetic polymer science towards the smallest unit in life, namely a cell. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Penrose-Hameroff orchestrated objective-reduction proposal for human consciousness is not biologically feasible.

PubMed

McKemmish, Laura K; Reimers, Jeffrey R; McKenzie, Ross H; Mark, Alan E; Hush, Noel S

2009-08-01

Penrose and Hameroff have argued that the conventional models of a brain function based on neural networks alone cannot account for human consciousness, claiming that quantum-computation elements are also required. Specifically, in their Orchestrated Objective Reduction (Orch OR) model [R. Penrose and S. R. Hameroff, J. Conscious. Stud. 2, 99 (1995)], it is postulated that microtubules act as quantum processing units, with individual tubulin dimers forming the computational elements. This model requires that the tubulin is able to switch between alternative conformational states in a coherent manner, and that this process be rapid on the physiological time scale. Here, the biological feasibility of the Orch OR proposal is examined in light of recent experimental studies on microtubule assembly and dynamics. It is shown that the tubulins do not possess essential properties required for the Orch OR proposal, as originally proposed, to hold. Further, we consider also recent progress in the understanding of the long-lived coherent motions in biological systems, a feature critical to Orch OR, and show that no reformation of the proposal based on known physical paradigms could lead to quantum computing within microtubules. Hence, the Orch OR model is not a feasible explanation of the origin of consciousness.
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET

PubMed Central

Androsova, Ganna; del Sol, Antonio

2015-01-01

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states. PMID:26058016
Of truth and pathways: chasing bits of information through myriads of articles.

PubMed

Krauthammer, Michael; Kra, Pauline; Iossifov, Ivan; Gomez, Shawn M; Hripcsak, George; Hatzivassiloglou, Vasileios; Friedman, Carol; Rzhetsky, Andrey

2002-01-01

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

PubMed

Zhan, Mei; Crane, Matthew M; Entchev, Eugeni V; Caballero, Antonio; Fernandes de Abreu, Diana Andrea; Ch'ng, QueeLim; Lu, Hang

2015-04-01

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.
GUI to Facilitate Research on Biological Damage from Radiation

NASA Technical Reports Server (NTRS)

Cucinotta, Frances A.; Ponomarev, Artem Lvovich

2010-01-01

A graphical-user-interface (GUI) computer program has been developed to facilitate research on the damage caused by highly energetic particles and photons impinging on living organisms. The program brings together, into one computational workspace, computer codes that have been developed over the years, plus codes that will be developed during the foreseeable future, to address diverse aspects of radiation damage. These include codes that implement radiation-track models, codes for biophysical models of breakage of deoxyribonucleic acid (DNA) by radiation, pattern-recognition programs for extracting quantitative information from biological assays, and image-processing programs that aid visualization of DNA breaks. The radiation-track models are based on transport models of interactions of radiation with matter and solution of the Boltzmann transport equation by use of both theoretical and numerical models. The biophysical models of breakage of DNA by radiation include biopolymer coarse-grained and atomistic models of DNA, stochastic- process models of deposition of energy, and Markov-based probabilistic models of placement of double-strand breaks in DNA. The program is designed for use in the NT, 95, 98, 2000, ME, and XP variants of the Windows operating system.
A Systems' Biology Approach to Study MicroRNA-Mediated Gene Regulatory Networks

PubMed Central

Kunz, Manfred; Vera, Julio; Wolkenhauer, Olaf

2013-01-01

MicroRNAs (miRNAs) are potent effectors in gene regulatory networks where aberrant miRNA expression can contribute to human diseases such as cancer. For a better understanding of the regulatory role of miRNAs in coordinating gene expression, we here present a systems biology approach combining data-driven modeling and model-driven experiments. Such an approach is characterized by an iterative process, including biological data acquisition and integration, network construction, mathematical modeling and experimental validation. To demonstrate the application of this approach, we adopt it to investigate mechanisms of collective repression on p21 by multiple miRNAs. We first construct a p21 regulatory network based on data from the literature and further expand it using algorithms that predict molecular interactions. Based on the network structure, a detailed mechanistic model is established and its parameter values are determined using data. Finally, the calibrated model is used to study the effect of different miRNA expression profiles and cooperative target regulation on p21 expression levels in different biological contexts. PMID:24350286
Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions

PubMed Central

Hucka, Michael; Bergmann, Frank T.; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M.; Le Novére, Nicolas; Myers, Chris J.; Olivier, Brett G.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Waltemath, Dagmar; Wilkinson, Darren J.

2017-01-01

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/. PMID:26528569
Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2015-09-04

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.
Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

Hucka, Michael; Bergmann, Frank T; Dräger, Andreas; Hoops, Stefan; Keating, Sarah M; Le Novère, Nicolas; Myers, Chris J; Olivier, Brett G; Sahle, Sven; Schaff, James C; Smith, Lucian P; Waltemath, Dagmar; Wilkinson, Darren J

2015-06-01

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
Population-expression models of immune response

NASA Astrophysics Data System (ADS)

Stromberg, Sean P.; Antia, Rustom; Nemenman, Ilya

2013-06-01

The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable.
Development of a Model for Measuring Scientific Processing Skills Based on Brain-Imaging Technology: Focused on the Experimental Design Process

ERIC Educational Resources Information Center

Lee, Il-Sun; Byeon, Jung-Ho; Kim, Young-shin; Kwon, Yong-Ju

2014-01-01

The purpose of this study was to develop a model for measuring experimental design ability based on functional magnetic resonance imaging (fMRI) during biological inquiry. More specifically, the researchers developed an experimental design task that measures experimental design ability. Using the developed experimental design task, they measured…
Learning the Cell Structures with Three-Dimensional Models: Students' Achievement by Methods, Type of School and Questions' Cognitive Level

NASA Astrophysics Data System (ADS)

Lazarowitz, Reuven; Naim, Raphael

2013-08-01

The cell topic was taught to 9th-grade students in three modes of instruction: (a) students "hands-on," who constructed three-dimensional cell organelles and macromolecules during the learning process; (b) teacher demonstration of the three-dimensional model of the cell structures; and (c) teaching the cell topic with the regular learning material in an expository mode (which use one- or two-dimensional cell structures as are presented in charts, textbooks and microscopic slides). The sample included 669, 9th-grade students from 25 classes who were taught by 22 Biology teachers. Students were randomly assigned to the three modes of instruction, and two tests in content knowledge in Biology were used. Data were treated with multiple analyses of variance. The results indicate that entry behavior in Biology was equal for all the study groups and types of schools. The "hands-on" learning group who build three-dimensional models through the learning process achieved significantly higher on academic achievements and on the high and low cognitive questions' levels than the other two groups. The study indicates the advantages students may have being actively engaged in the learning process through the "hands-on" mode of instruction/learning.
Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

PubMed Central

Ferguson, Jake M; Ponciano, José M

2014-01-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. PMID:24304946
Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.
Thermodynamics of Biological Processes

PubMed Central

Garcia, Hernan G.; Kondev, Jane; Orme, Nigel; Theriot, Julie A.; Phillips, Rob

2012-01-01

There is a long and rich tradition of using ideas from both equilibrium thermodynamics and its microscopic partner theory of equilibrium statistical mechanics. In this chapter, we provide some background on the origins of the seemingly unreasonable effectiveness of ideas from both thermodynamics and statistical mechanics in biology. After making a description of these foundational issues, we turn to a series of case studies primarily focused on binding that are intended to illustrate the broad biological reach of equilibrium thinking in biology. These case studies include ligand-gated ion channels, thermodynamic models of transcription, and recent applications to the problem of bacterial chemotaxis. As part of the description of these case studies, we explore a number of different uses of the famed Monod–Wyman–Changeux (MWC) model as a generic tool for providing a mathematical characterization of two-state systems. These case studies should provide a template for tailoring equilibrium ideas to other problems of biological interest. PMID:21333788
Modeling biological problems in computer science: a case study in genome assembly.

PubMed

Medvedev, Paul

2018-01-30

As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. In this tutorial, we use the example of the genome assembly problem to demonstrate how to go from a question in the biological realm to a solution in the computer science realm. We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Mathematical modeling of heat treatment processes conserving biological activity of plant bioresources

NASA Astrophysics Data System (ADS)

Rodionova, N. S.; Popov, E. S.; Pozhidaeva, E. A.; Pynzar, S. S.; Ryaskina, L. O.

2018-05-01

The aim of this study is to develop a mathematical model of the heat exchange process of LT-processing to estimate the dynamics of temperature field changes and optimize the regime parameters, due to the non-stationarity process, the physicochemical and thermophysical properties of food systems. The application of LT-processing, based on the use of low-temperature modes in thermal culinary processing of raw materials with preliminary vacuum packaging in a polymer heat- resistant film is a promising trend in the development of technics and technology in the catering field. LT-processing application of food raw materials guarantees the preservation of biologically active substances in food environments, which are characterized by a certain thermolability, as well as extend the shelf life and high consumer characteristics of food systems that are capillary-porous bodies. When performing the mathematical modeling of the LT-processing process, the packet of symbolic mathematics “Maple” was used, as well as the mathematical packet flexPDE that uses the finite element method for modeling objects with distributed parameters. The processing of experimental results was evaluated with the help of the developed software in the programming language Python 3.4. To calculate and optimize the parameters of the LT processing process of polycomponent food systems, the differential equation of non-stationary thermal conductivity was used, the solution of which makes it possible to identify the temperature change at any point of the solid at different moments. The present study specifies data on the thermophysical characteristics of the polycomponent food system based on plant raw materials, with the help of which the physico-mathematical model of the LT- processing process has been developed. The obtained mathematical model allows defining of the dynamics of the temperature field in different sections of the LT-processed polycomponent food systems on the basis of calculating the evolution profiles of temperature fields, which enable one to analyze the efficiency of the regime parameters of heat treatment.
Life-Game, with Glass Beads and Molecules, on the Principles of the Origin of Life

ERIC Educational Resources Information Center

Eigen, Manfred; Haglund, Herman

1976-01-01

Discusses a theoretical model that uses a game as a base for studying processes of a stochastic nature, which involve chemical reactions, molecular systems, biological processes, cells, or people in a population. (MLH)
Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.
Systematic analysis of signaling pathways using an integrative environment.

PubMed

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

2007-01-01

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

PubMed

Ayllón, Daniel; Grimm, Volker; Attinger, Sabine; Hauhs, Michael; Simmer, Clemens; Vereecken, Harry; Lischeid, Gunnar

2018-05-01

Terrestrial environmental systems are characterised by numerous feedback links between their different compartments. However, scientific research is organized into disciplines that focus on processes within the respective compartments rather than on interdisciplinary links. Major feedback mechanisms between compartments might therefore have been systematically overlooked so far. Without identifying these gaps, initiatives on future comprehensive environmental monitoring schemes and experimental platforms might fail. We performed a comprehensive overview of feedbacks between compartments currently represented in environmental sciences and explores to what degree missing links have already been acknowledged in the literature. We focused on process models as they can be regarded as repositories of scientific knowledge that compile findings of numerous single studies. In total, 118 simulation models from 23 model types were analysed. Missing processes linking different environmental compartments were identified based on a meta-review of 346 published reviews, model intercomparison studies, and model descriptions. Eight disciplines of environmental sciences were considered and 396 linking processes were identified and ascribed to the physical, chemical or biological domain. There were significant differences between model types and scientific disciplines regarding implemented interdisciplinary links. The most wide-spread interdisciplinary links were between physical processes in meteorology, hydrology and soil science that drive or set the boundary conditions for other processes (e.g., ecological processes). In contrast, most chemical and biological processes were restricted to links within the same compartment. Integration of multiple environmental compartments and interdisciplinary knowledge was scarce in most model types. There was a strong bias of suggested future research foci and model extensions towards reinforcing existing interdisciplinary knowledge rather than to open up new interdisciplinary pathways. No clear pattern across disciplines exists with respect to suggested future research efforts. There is no evidence that environmental research would clearly converge towards more integrated approaches or towards an overarching environmental systems theory. Copyright © 2017 Elsevier B.V. All rights reserved.
Expectancies as core features of mental disorders.

PubMed

Rief, Winfried; Glombiewski, Julia A; Gollwitzer, Mario; Schubö, Anna; Schwarting, Rainer; Thorwart, Anna

2015-09-01

Expectancies are core features of mental disorders, and change in expectations is therefore one of the core mechanisms of treatment in psychiatry. We aim to improve our understanding of expectancies by summarizing factors that contribute to their development, persistence, and modification. We pay particular attention to the issue of persistence of expectancies despite experiences that contradict them. Based on recent research findings, we propose a new model for expectation persistence and expectation change. When expectations are established, effects are evident in neural and other biological systems, for example, via anticipatory reactions, different biological reactions to expected versus unexpected stimuli, etc. Psychological 'immunization' and 'assimilation', implicit self-confirming processes, and stability of biological processes help us to better understand why expectancies persist even in the presence of expectation violations. Learning theory, attentional processes, social influences, and biological determinants contribute to the development, persistence, and modification of expectancies. Psychological interventions should focus on optimizing expectation violation to achieve optimal treatment outcome and to avoid treatment failures.
Analysis and logical modeling of biological signaling transduction networks

NASA Astrophysics Data System (ADS)

Sun, Zhongyao

The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

PubMed Central

Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems. PMID:27187178
A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

PubMed

Pârvu, Ovidiu; Gilbert, David

2016-01-01

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.
Computational modeling of ion transport through nanopores.

PubMed

Modi, Niraj; Winterhalter, Mathias; Kleinekathöfer, Ulrich

2012-10-21

Nanoscale pores are ubiquitous in biological systems while artificial nanopores are being fabricated for an increasing number of applications. Biological pores are responsible for the transport of various ions and substrates between the different compartments of biological systems separated by membranes while artificial pores are aimed at emulating such transport properties. As an experimental method, electrophysiology has proven to be an important nano-analytical tool for the study of substrate transport through nanopores utilizing ion current measurements as a probe for the detection. Independent of the pore type, i.e., biological or synthetic, and objective of the study, i.e., to model cellular processes of ion transport or electrophysiological experiments, it has become increasingly important to understand the dynamics of ions in nanoscale confinements. To this end, numerical simulations have established themselves as an indispensable tool to decipher ion transport processes through biological as well as artificial nanopores. This article provides an overview of different theoretical and computational methods to study ion transport in general and to calculate ion conductance in particular. Potential new improvements in the existing methods and their applications are highlighted wherever applicable. Moreover, representative examples are given describing the ion transport through biological and synthetic nanopores as well as the high selectivity of ion channels. Special emphasis is placed on the usage of molecular dynamics simulations which already have demonstrated their potential to unravel ion transport properties at an atomic level.
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.
Mind-body interactions in pain: the neurophysiology of anxious and catastrophic pain-related thoughts.

PubMed

Campbell, Claudia M; Edwards, Robert R

2009-03-01

The well-accepted biopsychosocial model proposes that the experience of pain and responses to it result from a complex interaction of biological, psychological, and social factors. However, the separation of these constructs is substantially artificial, and we presume that psychological processes have biological effects, that biological processes affect an individual's psychosocial environment, and so on. Considerable research has demonstrated that pain-coping strategies influence perceived pain intensity and physical functioning, and individual differences in styles of pain coping even shape the persistence of long-term pain complaints in some populations. A good deal of this coping research has focused on catastrophizing, which is a generally maladaptive cognitive and emotional mental set that involves feelings of helplessness when in pain, rumination about pain symptoms, and magnification of pain-related complaints. Collectively, catastrophizing has been consistently associated with heightened experiences of pain across a variety of samples. Although catastrophic thinking regarding pain-related symptoms is often classified under the "psychologic" category within the broader biopsychosocial model, we propose that catastrophizing exerts biologic effects that may account for some of its negative consequences. In general, the cognitive and affective processes captured within the construct of catastrophizing may exert effects on the neuromuscular, cardiovascular, immune, and neuroendocrine systems, and on the activity in the pain neuromatrix within the brain. The interface between pain-related neurobiology and processes such as pain-related catastrophizing represents an important avenue for future pain research.
Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

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

PubMed

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

2016-10-01

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

PubMed Central

Rasmussen, Daniel; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain’s general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model’s behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions. PMID:28683111
A fractal model for nuclear organization: current evidence and biological implications

PubMed Central

Bancaud, Aurélien; Lavelle, Christophe; Huet, Sébastien; Ellenberg, Jan

2012-01-01

Chromatin is a multiscale structure on which transcription, replication, recombination and repair of the genome occur. To fully understand any of these processes at the molecular level under physiological conditions, a clear picture of the polymorphic and dynamic organization of chromatin in the eukaryotic nucleus is required. Recent studies indicate that a fractal model of chromatin architecture is consistent with both the reaction-diffusion properties of chromatin interacting proteins and with structural data on chromatin interminglement. In this study, we provide a critical overview of the experimental evidence that support a fractal organization of chromatin. On this basis, we discuss the functional implications of a fractal chromatin model for biological processes and propose future experiments to probe chromatin organization further that should allow to strongly support or invalidate the fractal hypothesis. PMID:22790985
Methods for improving simulations of biological systems: systemic computation and fractal proteins

PubMed Central

Bentley, Peter J.

2009-01-01

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

PubMed

Li, Chen; Nagasaki, Masao; Ueno, Kazuko; Miyano, Satoru

2009-04-27

Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher et al. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in C. elegans and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze in silico models incorporating the use of model checking approach. A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules - Rule I and Rule II - to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of lin-15ko; lin-12ko double mutants). More insights are also suggested. The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.
Achilles and the tortoise: Some caveats to mathematical modeling in biology.

PubMed

Gilbert, Scott F

2018-01-31

Mathematical modeling has recently become a much-lauded enterprise, and many funding agencies seek to prioritize this endeavor. However, there are certain dangers associated with mathematical modeling, and knowledge of these pitfalls should also be part of a biologist's training in this set of techniques. (1) Mathematical models are limited by known science; (2) Mathematical models can tell what can happen, but not what did happen; (3) A model does not have to conform to reality, even if it is logically consistent; (4) Models abstract from reality, and sometimes what they eliminate is critically important; (5) Mathematics can present a Platonic ideal to which biologically organized matter strives, rather than a trial-and-error bumbling through evolutionary processes. This "Unity of Science" approach, which sees biology as the lowest physical science and mathematics as the highest science, is part of a Western belief system, often called the Great Chain of Being (or Scala Natura), that sees knowledge emerge as one passes from biology to chemistry to physics to mathematics, in an ascending progression of reason being purification from matter. This is also an informal model for the emergence of new life. There are now other informal models for integrating development and evolution, but each has its limitations. Copyright © 2018 Elsevier Ltd. All rights reserved.
Chemical genomics in plant biology.

PubMed

Sadhukhan, Ayan; Sahoo, Lingaraj; Panda, Sanjib Kumar

2012-06-01

Chemical genomics is a newly emerged and rapidly progressing field in biology, where small chemical molecules bind specifically and reversibly to protein(s) to modulate their function(s), leading to the delineation and subsequent unravelling of biological processes. This approach overcomes problems like lethality and redundancy of classical genetics. Armed with the powerful techniques of combinatorial synthesis, high-throughput screening and target discovery chemical genomics expands its scope to diverse areas in biology. The well-established genetic system of Arabidopsis model allows chemical genomics to enter into the realm of plant biology exploring signaling pathways of growth regulators, endomembrane signaling cascades, plant defense mechanisms and many more events.
Biohorizons: An eConference to Assess Human Biology in Large, First-Year Classes

ERIC Educational Resources Information Center

Moni, Roger W.; Moni, Karen B.; Poronnik, Philip; Lluka, Lesley J.

2007-01-01

The authors detail the design, implementation and evaluation of an eConference entitled "Biohorizons," using a presage-process-product model to describe the development of an eLearning community. Biohorizons was a summative learning and assessment task aiming to introduce large classes of first-year Human Biology students to the practices of…
A Model System for the Study of Gene Expression in the Undergraduate Laboratory

ERIC Educational Resources Information Center

Hargadon, Kristian M.

2016-01-01

The flow of genetic information from DNA to RNA to protein, otherwise known as the "central dogma" of biology, is one of the most basic and overarching concepts in the biological sciences. Nevertheless, numerous studies have reported student misconceptions at the undergraduate level of this fundamental process of gene expression. This…
Terrestrial Slugs as a Model Organism for Inquiry-Based Experimentation in a Majors General Biology Laboratory

ERIC Educational Resources Information Center

Peters, Brenda J.; Blair, Amy C.

2013-01-01

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

ERIC Educational Resources Information Center

Araujo-Jorge, Tania C.; Cardona, Tania S.; Mendes, Claudia L. S.; Henriques-Pons, Andrea; Meirelles, Rosane M. S.; Coutinho, Claudia M. L. M.; Aguiar, Luiz Edmundo V.; Meirelles, Maria de Nazareth L.; de Castro, Solange L.; Barbosa, Helene S.; Luz, Mauricio R. M. P.

2004-01-01

The advent of genomics, proteomics, and microarray technology has brought much excitement to science, both in teaching and in learning. The public is eager to know about the processes of life. In the present context of the explosive growth of scientific information, a major challenge of modern cell biology is to popularize basic concepts of…
Extending the knowledge in histochemistry and cell biology.

PubMed

Heupel, Wolfgang-Moritz; Drenckhahn, Detlev

2010-01-01

Central to modern Histochemistry and Cell Biology stands the need for visualization of cellular and molecular processes. In the past several years, a variety of techniques has been achieved bridging traditional light microscopy, fluorescence microscopy and electron microscopy with powerful software-based post-processing and computer modeling. Researchers now have various tools available to investigate problems of interest from bird's- up to worm's-eye of view, focusing on tissues, cells, proteins or finally single molecules. Applications of new approaches in combination with well-established traditional techniques of mRNA, DNA or protein analysis have led to enlightening and prudent studies which have paved the way toward a better understanding of not only physiological but also pathological processes in the field of cell biology. This review is intended to summarize articles standing for the progress made in "histo-biochemical" techniques and their manifold applications.
Synthetic biology between challenges and risks: suggestions for a model of governance and a regulatory framework, based on fundamental rights.

PubMed

Colussi, Ilaria Anna

2013-01-01

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

Modeling the Soft Geometry of Biological Membranes

NASA Astrophysics Data System (ADS)

Daly, K.

This dissertation presents work done applying the techniques of physics to biological systems. The difference in length scales of the thickness of the phospolipid bilayer and overall size of a biological cell allows bilayer to be modeled elastically as a thin sheet. The Helfrich free energy is extended applied to models representing various biological systems, in order to find quasi-equilibrium states as well as transitions between states. Morphologies are approximated as axially sym-metric. Stable morphologies are de-termined analytically and through the use of computer simulation. The simple morphologies examined analytically give a model for the pearling transition seen in growing biological cells. An analytic model of celluar bulging in gram-negative bacteria predicts a critical pore radius for bulging of 20 nanometers. This model is extended to the membrane dynamics of human red blood cells, predicting three morphologic phases which are seen in vivo. A computer simulation was developed to study more complex morphologies with models representing different bilayer compositions. Single and multi-component bilayer models reproduce morphologies previously predicted by Seifert. A mean field model representing the intrinsic curvature of proteins coupling to membrane curvature is used to explore the stability of the particular morphology of rod outer segment cells. The process of pore formation and expansion in cell-cell fusion is not well understood. Simulation of the pore created in cell-cell fusion led to the finding of a minimal pore radius required for pore expansion, suggesting pores formed in nature are formed with a minimum size.
Derivation and computation of discrete-delay and continuous-delay SDEs in mathematical biology.

PubMed

Allen, Edward J

2014-06-01

Stochastic versions of several discrete-delay and continuous-delay differential equations, useful in mathematical biology, are derived from basic principles carefully taking into account the demographic, environmental, or physiological randomness in the dynamic processes. In particular, stochastic delay differential equation (SDDE) models are derived and studied for Nicholson's blowflies equation, Hutchinson's equation, an SIS epidemic model with delay, bacteria/phage dynamics, and glucose/insulin levels. Computational methods for approximating the SDDE models are described. Comparisons between computational solutions of the SDDEs and independently formulated Monte Carlo calculations support the accuracy of the derivations and of the computational methods.
Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study

NASA Astrophysics Data System (ADS)

Wieder, William R.; Cleveland, Cory C.; Lawrence, David M.; Bonan, Gordon B.

2015-04-01

Uncertainties in terrestrial carbon (C) cycle projections increase uncertainty of potential climate feedbacks. Efforts to improve model performance often include increased representation of biogeochemical processes, such as coupled carbon-nitrogen (N) cycles. In doing so, models are becoming more complex, generating structural uncertainties in model form that reflect incomplete knowledge of how to represent underlying processes. Here, we explore structural uncertainties associated with biological nitrogen fixation (BNF) and quantify their effects on C cycle projections. We find that alternative plausible structures to represent BNF result in nearly equivalent terrestrial C fluxes and pools through the twentieth century, but the strength of the terrestrial C sink varies by nearly a third (50 Pg C) by the end of the twenty-first century under a business-as-usual climate change scenario representative concentration pathway 8.5. These results indicate that actual uncertainty in future C cycle projections may be larger than previously estimated, and this uncertainty will limit C cycle projections until model structures can be evaluated and refined.
A Workflow for Global Sensitivity Analysis of PBPK Models

PubMed Central

McNally, Kevin; Cotton, Richard; Loizou, George D.

2011-01-01

Physiologically based pharmacokinetic (PBPK) models have a potentially significant role in the development of a reliable predictive toxicity testing strategy. The structure of PBPK models are ideal frameworks into which disparate in vitro and in vivo data can be integrated and utilized to translate information generated, using alternative to animal measures of toxicity and human biological monitoring data, into plausible corresponding exposures. However, these models invariably include the description of well known non-linear biological processes such as, enzyme saturation and interactions between parameters such as, organ mass and body mass. Therefore, an appropriate sensitivity analysis (SA) technique is required which can quantify the influences associated with individual parameters, interactions between parameters and any non-linear processes. In this report we have defined the elements of a workflow for SA of PBPK models that is computationally feasible, accounts for interactions between parameters, and can be displayed in the form of a bar chart and cumulative sum line (Lowry plot), which we believe is intuitive and appropriate for toxicologists, risk assessors, and regulators. PMID:21772819
CLIMATIC EFFECTS ON TUNDRA CARBON STORAGE INFERRED FROM EXPERIMENTAL DATA AND A MODEL

EPA Science Inventory

We used a process-based model of ecosystem carbon (C) and nitrogen (N)dynamics, MBL-GEM (Marine Biological Laboratory General Ecosystem Model), to integrated and analyze the results of several experiments that examined the response of arctic tussock tundra to manipulations of CO2...
Individual Differences in Boys' and Girls' Timing and Tempo of Puberty: Modeling Development with Nonlinear Growth Models

ERIC Educational Resources Information Center

Marceau, Kristine; Ram, Nilam; Houts, Renate M.; Grimm, Kevin J.; Susman, Elizabeth J.

2011-01-01

Pubertal development is a nonlinear process progressing from prepubescent beginnings through biological, physical, and psychological changes to full sexual maturity. To tether theoretical concepts of puberty with sophisticated longitudinal, analytical models capable of articulating pubertal development more accurately, we used nonlinear…
MATLAB-Based Teaching Modules in Biochemical Engineering

ERIC Educational Resources Information Center

Lee, Kilho; Comolli, Noelle K.; Kelly, William J.; Huang, Zuyi

2015-01-01

Mathematical models play an important role in biochemical engineering. For example, the models developed in the field of systems biology have been used to identify drug targets to treat pathogens such as Pseudomonas aeruginosa in biofilms. In addition, competitive binding models for chromatography processes have been developed to predict expanded…
Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology

PubMed Central

Quillin, Kim; Thomas, Stephen

2015-01-01

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

PubMed

MacLean, Adam L; Lo Celso, Cristina; Stumpf, Michael P H

2017-01-01

Stem cells are fundamental to human life and offer great therapeutic potential, yet their biology remains incompletely-or in cases even poorly-understood. The field of stem cell biology has grown substantially in recent years due to a combination of experimental and theoretical contributions: the experimental branch of this work provides data in an ever-increasing number of dimensions, while the theoretical branch seeks to determine suitable models of the fundamental stem cell processes that these data describe. The application of population dynamics to biology is amongst the oldest applications of mathematics to biology, and the population dynamics perspective continues to offer much today. Here we describe the impact that such a perspective has made in the field of stem cell biology. Using hematopoietic stem cells as our model system, we discuss the approaches that have been used to study their key properties, such as capacity for self-renewal, differentiation, and cell fate lineage choice. We will also discuss the relevance of population dynamics in models of stem cells and cancer, where competition naturally emerges as an influential factor on the temporal evolution of cell populations. Stem Cells 2017;35:80-88. © 2016 AlphaMed Press.
Simplification and its consequences in biological modelling: conclusions from a study of calcium oscillations in hepatocytes.

PubMed

Hetherington, James P J; Warner, Anne; Seymour, Robert M

2006-04-22

Systems Biology requires that biological modelling is scaled up from small components to system level. This can produce exceedingly complex models, which obscure understanding rather than facilitate it. The successful use of highly simplified models would resolve many of the current problems faced in Systems Biology. This paper questions whether the conclusions of simple mathematical models of biological systems are trustworthy. The simplification of a specific model of calcium oscillations in hepatocytes is examined in detail, and the conclusions drawn from this scrutiny generalized. We formalize our choice of simplification approach through the use of functional 'building blocks'. A collection of models is constructed, each a progressively more simplified version of a well-understood model. The limiting model is a piecewise linear model that can be solved analytically. We find that, as expected, in many cases the simpler models produce incorrect results. However, when we make a sensitivity analysis, examining which aspects of the behaviour of the system are controlled by which parameters, the conclusions of the simple model often agree with those of the richer model. The hypothesis that the simplified model retains no information about the real sensitivities of the unsimplified model can be very strongly ruled out by treating the simplification process as a pseudo-random perturbation on the true sensitivity data. We conclude that sensitivity analysis is, therefore, of great importance to the analysis of simple mathematical models in biology. Our comparisons reveal which results of the sensitivity analysis regarding calcium oscillations in hepatocytes are robust to the simplifications necessarily involved in mathematical modelling. For example, we find that if a treatment is observed to strongly decrease the period of the oscillations while increasing the proportion of the cycle during which cellular calcium concentrations are rising, without affecting the inter-spike or maximum calcium concentrations, then it is likely that the treatment is acting on the plasma membrane calcium pump.
System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses

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

Snyder-Talkington, Brandi N.; Dymacek, Julian; Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300

2013-10-15

The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chainmore » simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung inflammation and fibrosis were revealed. • Two functional, representative genes, ccl2 and vegfa, were validated in vitro.« less
Computer Simulation of Developmental Processes and Toxicities (SOT)

EPA Science Inventory

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 ...
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.
Perchlorate and nitrate treatment by ion exchange integrated with biological brine treatment.

PubMed

Lehman, S Geno; Badruzzaman, Mohammad; Adham, Samer; Roberts, Deborah J; Clifford, Dennis A

2008-02-01

Groundwater contaminated with perchlorate and nitrate was treated in a pilot plant using a commercially available ion exchange (IX) resin. Regenerant brine concentrate from the IX process, containing high perchlorate and nitrate, was treated biologically and the treated brine was reused in IX resin regeneration. The nitrate concentration of the feed water determined the exhaustion lifetime (i.e., regeneration frequency) of the resin; and the regeneration condition was determined by the perchlorate elution profile from the exhausted resin. The biological brine treatment system, using a salt-tolerant perchlorate- and nitrate-reducing culture, was housed in a sequencing batch reactor (SBR). The biological process consistently reduced perchlorate and nitrate concentrations in the spent brine to below the treatment goals of 500 microg ClO4(-)/L and 0.5mg NO3(-)-N/L determined by equilibrium multicomponent IX modeling. During 20 cycles of regeneration, the system consistently treated the drinking water to below the MCL of nitrate (10 mgNO3(-)-N/L) and the California Department of Health Services (CDHS) notification level of perchlorate (i.e., 6 microg/L). A conceptual cost analysis of the IX process estimated that perchlorate and nitrate treatment using the IX process with biological brine treatment to be approximately 20% less expensive than using the conventional IX with brine disposal.
Variation across mitochondrial gene trees provides evidence for systematic error: How much gene tree variation is biological?

PubMed

Richards, Emilie J; Brown, Jeremy M; Barley, Anthony J; Chong, Rebecca A; Thomson, Robert C

2018-02-19

The use of large genomic datasets in phylogenetics has highlighted extensive topological variation across genes. Much of this discordance is assumed to result from biological processes. However, variation among gene trees can also be a consequence of systematic error driven by poor model fit, and the relative importance of biological versus methodological factors in explaining gene tree variation is a major unresolved question. Using mitochondrial genomes to control for biological causes of gene tree variation, we estimate the extent of gene tree discordance driven by systematic error and employ posterior prediction to highlight the role of model fit in producing this discordance. We find that the amount of discordance among mitochondrial gene trees is similar to the amount of discordance found in other studies that assume only biological causes of variation. This similarity suggests that the role of systematic error in generating gene tree variation is underappreciated and critical evaluation of fit between assumed models and the data used for inference is important for the resolution of unresolved phylogenetic questions.
BMDExpress Data Viewer: A Visualization Tool to Analyze BMDExpress Datasets

EPA Science Inventory

Regulatory agencies increasingly apply benchmark dose (BMD) modeling to determine points of departure in human risk assessments. BMDExpress applies BMD modeling to transcriptomics datasets and groups genes to biological processes and pathways for rapid assessment of doses at whic...
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
LASSIE: simulating large-scale models of biochemical systems on GPUs.

PubMed

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

2017-05-10

Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs. LASSIE is a "black-box" GPU-accelerated deterministic simulator, specifically designed for large-scale models and not requiring any expertise in mathematical modeling, simulation algorithms or GPU programming. Given a reaction-based model of a cellular process, LASSIE automatically generates the corresponding system of Ordinary Differential Equations (ODEs), assuming mass-action kinetics. The numerical solution of the ODEs is obtained by automatically switching between the Runge-Kutta-Fehlberg method in the absence of stiffness, and the Backward Differentiation Formulae of first order in presence of stiffness. The computational performance of LASSIE are assessed using a set of randomly generated synthetic reaction-based models of increasing size, ranging from 64 to 8192 reactions and species, and compared to a CPU-implementation of the LSODA numerical integration algorithm. LASSIE adopts a novel fine-grained parallelization strategy to distribute on the GPU cores all the calculations required to solve the system of ODEs. By virtue of this implementation, LASSIE achieves up to 92× speed-up with respect to LSODA, therefore reducing the running time from approximately 1 month down to 8 h to simulate models consisting in, for instance, four thousands of reactions and species. Notably, thanks to its smaller memory footprint, LASSIE is able to perform fast simulations of even larger models, whereby the tested CPU-implementation of LSODA failed to reach termination. LASSIE is therefore expected to make an important breakthrough in Systems Biology applications, for the execution of faster and in-depth computational analyses of large-scale models of complex biological systems.
Maximum Drawdown of Atmospheric CO2 due to Biological Uptake in the Ocean and the Ocean Temperature Effect

NASA Astrophysics Data System (ADS)

Odalen, M.; Nycander, J.; Oliver, K. I. C.; Nilsson, J.; Brodeau, L.; Ridgwell, A.

2016-02-01

During glacials, atmospheric CO2 is significantly lowered; the decrease is about 1/3 or 90 ppm during the last four glacial cycles. Since the ocean reservoir of carbon, and hence the ocean capacity for storing carbon, is substantially larger than the atmospheric and terrestrial counterparts, it is likely that this lowering was caused by ocean processes, drawing the CO2 into the deep ocean. The Southern Ocean circulation and biological efficiency are widely accepted as having played an important part in this CO2 drawdown. However, the relative effects of different processes contributing to this oceanic uptake have not yet been well constrained. In this work, we focus on better constraining two of these processes; 1) the effect of increased efficiency of the biological carbon uptake, and 2) the effect of changes in global mean ocean temperature on the abiotic ocean-atmosphere CO2 equilibrium. By performing ensemble runs using an Earth System Model of Intermediate Complexity (EMIC) we examine the changes in atmospheric pCO2 achieved by 100% nutrient utilization efficiency of biology. The simulations display different ocean circulation patterns and hence different global ocean mean temperatures. By restoring the atmospheric pCO2 to a target value during the spin-up phase, the total carbon content differs between each of the ensemble members. The difference is due to circulation having direct effects on biology, but also on global ocean mean temperature, changing the solubility of CO2. This study reveals the relative importance of of the processes 1 and 2 (mentioned above) for atmospheric pCO2 in a changed climate. The results of this study also show that a difference in carbon content after spin-up can have a significant effect on the drawdown potential of a maximised biological efficiency. Thus, the choice of spin-up characteristics in a model study of climate change CO2 dynamics may significantly affect the outcome of the study.
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…

Petri net based model of the body iron homeostasis.

PubMed

Formanowicz, Dorota; Sackmann, Andrea; Formanowicz, Piotr; Błazewicz, Jacek

2007-10-01

The body iron homeostasis is a not fully understood complex process. Despite the fact that some components of this process have been described in the literature, the complete model of the whole process has not been proposed. In this paper a Petri net based model of the body iron homeostasis is presented. Recently, Petri nets have been used for describing and analyzing various biological processes since they allow modeling the system under consideration very precisely. The main result presented in the paper is twofold, i.e., an informal description of the main part of the whole iron homeostasis process is described, and then it is also formulated in the formal language of Petri net theory. This model allows for a possible simulation of the process, since Petri net theory provides a lot of established analysis techniques.
BioModels.net Web Services, a free and integrated toolkit for computational modelling software.

PubMed

Li, Chen; Courtot, Mélanie; Le Novère, Nicolas; Laibe, Camille

2010-05-01

Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.
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.
Entrainment to periodic initiation and transition rates in a computational model for gene translation.

PubMed

Margaliot, Michael; Sontag, Eduardo D; Tuller, Tamir

2014-01-01

Periodic oscillations play an important role in many biomedical systems. Proper functioning of biological systems that respond to periodic signals requires the ability to synchronize with the periodic excitation. For example, the sleep/wake cycle is a manifestation of an internal timing system that synchronizes to the solar day. In the terminology of systems theory, the biological system must entrain or phase-lock to the periodic excitation. Entrainment is also important in synthetic biology. For example, connecting several artificial biological systems that entrain to a common clock may lead to a well-functioning modular system. The cell-cycle is a periodic program that regulates DNA synthesis and cell division. Recent biological studies suggest that cell-cycle related genes entrain to this periodic program at the gene translation level, leading to periodically-varying protein levels of these genes. The ribosome flow model (RFM) is a deterministic model obtained via a mean-field approximation of a stochastic model from statistical physics that has been used to model numerous processes including ribosome flow along the mRNA. Here we analyze the RFM under the assumption that the initiation and/or transition rates vary periodically with a common period T. We show that the ribosome distribution profile in the RFM entrains to this periodic excitation. In particular, the protein synthesis pattern converges to a unique periodic solution with period T. To the best of our knowledge, this is the first proof of entrainment in a mathematical model for translation that encapsulates aspects such as initiation and termination rates, ribosomal movement and interactions, and non-homogeneous elongation speeds along the mRNA. Our results support the conjecture that periodic oscillations in tRNA levels and other factors related to the translation process can induce periodic oscillations in protein levels, and may suggest a new approach for re-engineering genetic systems to obtain a desired, periodic, protein synthesis rate.
Bridging paradigms: hybrid mechanistic-discriminative predictive models.

PubMed

Doyle, Orla M; Tsaneva-Atansaova, Krasimira; Harte, James; Tiffin, Paul A; Tino, Peter; Díaz-Zuccarini, Vanessa

2013-03-01

Many disease processes are extremely complex and characterized by multiple stochastic processes interacting simultaneously. Current analytical approaches have included mechanistic models and machine learning (ML), which are often treated as orthogonal viewpoints. However, to facilitate truly personalized medicine, new perspectives may be required. This paper reviews the use of both mechanistic models and ML in healthcare as well as emerging hybrid methods, which are an exciting and promising approach for biologically based, yet data-driven advanced intelligent systems.
Physics-based signal processing algorithms for micromachined cantilever arrays

DOEpatents

Candy, James V; Clague, David S; Lee, Christopher L; Rudd, Robert E; Burnham, Alan K; Tringe, Joseph W

2013-11-19

A method of using physics-based signal processing algorithms for micromachined cantilever arrays. The methods utilize deflection of a micromachined cantilever that represents the chemical, biological, or physical element being detected. One embodiment of the method comprises the steps of modeling the deflection of the micromachined cantilever producing a deflection model, sensing the deflection of the micromachined cantilever and producing a signal representing the deflection, and comparing the signal representing the deflection with the deflection model.
Applying complex models to poultry production in the future--economics and biology.

PubMed

Talpaz, H; Cohen, M; Fancher, B; Halley, J

2013-09-01

The ability to determine the optimal broiler feed nutrient density that maximizes margin over feeding cost (MOFC) has obvious economic value. To determine optimal feed nutrient density, one must consider ingredient prices, meat values, the product mix being marketed, and the projected biological performance. A series of 8 feeding trials was conducted to estimate biological responses to changes in ME and amino acid (AA) density. Eight different genotypes of sex-separate reared broilers were fed diets varying in ME (2,723-3,386 kcal of ME/kg) and AA (0.89-1.65% digestible lysine with all essential AA acids being indexed to lysine) levels. Broilers were processed to determine carcass component yield at many different BW (1.09-4.70 kg). Trial data generated were used in model constructed to discover the dietary levels of ME and AA that maximize MOFC on a per broiler or per broiler annualized basis (bird × number of cycles/year). The model was designed to estimate the effects of dietary nutrient concentration on broiler live weight, feed conversion, mortality, and carcass component yield. Estimated coefficients from the step-wise regression process are subsequently used to predict the optimal ME and AA concentrations that maximize MOFC. The effects of changing feed or meat prices across a wide spectrum on optimal ME and AA levels can be evaluated via parametric analysis. The model can rapidly compare both biological and economic implications of changing from current practice to the simulated optimal solution. The model can be exploited to enhance decision making under volatile market conditions.
Mass exchange in an experimental new-generation life support system model based on biological regeneration of environment.

PubMed

Tikhomirov, A A; Ushakova, S A; Manukovsky, N S; Lisovsky, G M; Kudenko, Yu A; Kovalev, V S; Gubanov, V G; Barkhatov, Yu V; Gribovskaya, I V; Zolotukhin, I G; Gros, J B; Lasseur, Ch

2003-01-01

An experimental model of a biological life support system was used to evaluate qualitative and quantitative parameters of the internal mass exchange. The photosynthesizing unit included the higher plant component (wheat and radish), and the heterotrophic unit consisted of a soil-like substrate, California worms, mushrooms and microbial microflora. The gas mass exchange involved evolution of oxygen by the photosynthesizing component and its uptake by the heterotroph component along with the formation and maintaining of the SLS structure, growth of mushrooms and California worms, human respiration, and some other processes. Human presence in the system in the form of "virtual human" that at regular intervals took part in the respirative gas exchange during the experiment. Experimental data demonstrated good oxygen/carbon dioxide balance, and the closure of the cycles of these gases was almost complete. The water cycle was nearly 100% closed. The main components in the water mass exchange were transpiration water and the watering solution with mineral elements. Human consumption of the edible plant biomass (grains and roots) was simulated by processing these products by a unique physicochemical method of oxidizing them to inorganic mineral compounds, which were then returned into the system and fully assimilated by the plants. The oxidation was achieved by "wet combustion" of organic biomass, using hydrogen peroxide following a special procedure, which does not require high temperature and pressure. Hydrogen peroxide is produced from the water inside the system. The closure of the cycle was estimated for individual elements and compounds. Stoichiometric proportions are given for the main components included in the experimental model of the system. Approaches to the mathematical modeling of the cycling processes are discussed, using the data of the experimental model. Nitrogen, as a representative of biogenic elements, shows an almost 100% closure of the cycle inside the system. The proposed experimental model of a biological system is discussed as a candidate for potential application in the investigations aimed at creating ecosystems with largely closed cycles of the internal mass exchange. The formation and maintenance of sustainable cycling of vitally important chemical elements and compounds in biological life support systems (BLSS) is an extremely pressing problem. To attain the stable functioning of biological life support systems (BLSS) and to maintain a high degree of closure of material cycles in than, it is essential to understand the character of mass exchange processes and stoichiometnc proportions of the initial and synthesized components of the system. c2003 COSPAR. Published by Elsevier Science Ltd. All rights reserved.
A stochastic evolution model for residue Insertion-Deletion Independent from Substitution.

PubMed

Lèbre, Sophie; Michel, Christian J

2010-12-01

We develop here a new class of stochastic models of gene evolution based on residue Insertion-Deletion Independent from Substitution (IDIS). Indeed, in contrast to all existing evolution models, insertions and deletions are modeled here by a concept in population dynamics. Therefore, they are not only independent from each other, but also independent from the substitution process. After a separate stochastic analysis of the substitution and the insertion-deletion processes, we obtain a matrix differential equation combining these two processes defining the IDIS model. By deriving a general solution, we give an analytical expression of the residue occurrence probability at evolution time t as a function of a substitution rate matrix, an insertion rate vector, a deletion rate and an initial residue probability vector. Various mathematical properties of the IDIS model in relation with time t are derived: time scale, time step, time inversion and sequence length. Particular expressions of the nucleotide occurrence probability at time t are given for classical substitution rate matrices in various biological contexts: equal insertion rate, insertion-deletion only and substitution only. All these expressions can be directly used for biological evolutionary applications. The IDIS model shows a strongly different stochastic behavior from the classical substitution only model when compared on a gene dataset. Indeed, by considering three processes of residue insertion, deletion and substitution independently from each other, it allows a more realistic representation of gene evolution and opens new directions and applications in this research field. Copyright © 2010 Elsevier Ltd. All rights reserved.
Effect of Methanethiol Concentration on Sulfur Production in Biological Desulfurization Systems under Haloalkaline Conditions.

PubMed

Roman, Pawel; Veltman, René; Bijmans, Martijn F M; Keesman, Karel J; Janssen, Albert J H

2015-08-04

Bioremoval of H2S from gas streams became popular in recent years because of high process efficiency and low operational costs. To expand the scope of these processes to gas streams containing volatile organic sulfur compounds, like thiols, it is necessary to provide new insights into their impact on overall biodesulfurization process. Published data on the effect of thiols on biodesulfurization processes are scarce. In this study, we investigated the effect of methanethiol on the selectivity for sulfur production in a bioreactor integrated with a gas absorber. This is the first time that the inhibition of biological sulfur formation by methanethiol is investigated. In our reactor system, inhibition of sulfur production started to occur at a methanethiol loading rate of 0.3 mmol L(-1) d(-1). The experimental results were also described by a mathematical model that includes recent findings on the mode of biomass inhibition by methanethiol. We also found that the negative effect of methanethiol can be mitigated by lowering the salinity of the bioreactor medium. Furthermore, we developed a novel approach to measure the biological activity by sulfide measurements using UV-spectrophotometry. On the basis of this measurement method, it is possible to accurately estimate the unknown kinetic parameters in the mathematical model.
A Case Study Documenting the Process by Which Biology Instructors Transition from Teacher-Centered to Learner-Centered Teaching.

PubMed

Marbach-Ad, Gili; Hunt Rietschel, Carly

2016-01-01

In this study, we used a case study approach to obtain an in-depth understanding of the change process of two university instructors who were involved with redesigning a biology course. Given the hesitancy of many biology instructors to adopt evidence-based, learner-centered teaching methods, there is a critical need to understand how biology instructors transition from teacher-centered (i.e., lecture-based) instruction to teaching that focuses on the students. Using the innovation-decision model for change, we explored the motivation, decision-making, and reflective processes of the two instructors through two consecutive, large-enrollment biology course offerings. Our data reveal that the change process is somewhat unpredictable, requiring patience and persistence during inevitable challenges that arise for instructors and students. For example, the change process requires instructors to adopt a teacher-facilitator role as opposed to an expert role, to cover fewer course topics in greater depth, and to give students a degree of control over their own learning. Students must adjust to taking responsibility for their own learning, working collaboratively, and relinquishing the anonymity afforded by lecture-based teaching. We suggest implications for instructors wishing to change their teaching and administrators wishing to encourage adoption of learner-centered teaching at their institutions. © 2016 G. Marbach-Ad and C. H. Rietschel. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
Issues In Space Radiation Protection: Galactic Cosmic Rays

NASA Technical Reports Server (NTRS)

Wilson, J. W.; Kim, M.; Schimmerling, W.; Badavi, F. F.; Thibeault, S. A.; Cucinotta, F. A.; Shinn, J. L.; Kiefer, R.

1995-01-01

When shielding from cosmic heavy ions, one is faced with limited knowledge about the physical properties and biological responses of these radiations. Herein, the current health is discussed in terms of conventional protection practice and a test biological response model. The impact of biological response on optimum materials selection for cosmic ray shielding is presented in terms of the transmission characteristics of the shield material. Although liquid hydrogen is an optimum shield material, evaluation of the effectiveness of polymeric structural materials must await improvement in our knowledge of both the biological response and the nuclear processes.
Illuminating the landscape of host–pathogen interactions with the bacterium Listeria monocytogenes

PubMed Central

Cossart, Pascale

2011-01-01

Listeria monocytogenes has, in 25 y, become a model in infection biology. Through the analysis of both its saprophytic life and infectious process, new concepts in microbiology, cell biology, and pathogenesis have been discovered. This review will update our knowledge on this intracellular pathogen and highlight the most recent breakthroughs. Promising areas of investigation such as the increasingly recognized relevance for the infectious process, of RNA-mediated regulations in the bacterium, and the role of bacterially controlled posttranslational and epigenetic modifications in the host will also be discussed. PMID:22114192
Mechanism of Action of Cyclophilin A Explored by Metadynamics Simulations

PubMed Central

Leone, Vanessa; Lattanzi, Gianluca; Molteni, Carla; Carloni, Paolo

2009-01-01

Trans/cis prolyl isomerisation is involved in several biological processes, including the development of numerous diseases. In the HIV-1 capsid protein (CA), such a process takes place in the uncoating and recruitment of the virion and is catalyzed by cyclophilin A (CypA). Here, we use metadynamics simulations to investigate the isomerization of CA's model substrate HAGPIA in water and in its target protein CypA. Our results allow us to propose a novel mechanistic hypothesis, which is finally consistent with all of the available molecular biology data. PMID:19282959
Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.
Can Horton hear the whos? The importance of scale in mosquito-borne disease.

PubMed

Lord, C C; Alto, B W; Anderson, S L; Connelly, C R; Day, J F; Richards, S L; Smartt, C T; Tabachnick, W J

2014-03-01

The epidemiology of vector-borne pathogens is determined by mechanisms and interactions at different scales of biological organization, from individual-level cellular processes to community interactions between species and with the environment. Most research, however, focuses on one scale or level with little integration between scales or levels within scales. Understanding the interactions between levels and how they influence our perception of vector-borne pathogens is critical. Here two examples of biological scales (pathogen transmission and mosquito mortality) are presented to illustrate some of the issues of scale and to explore how processes on different levels may interact to influence mosquito-borne pathogen transmission cycles. Individual variation in survival, vector competence, and other traits affect population abundance, transmission potential, and community structure. Community structure affects interactions between individuals such as competition and predation, and thus influences the individual-level dynamics and transmission potential. Modeling is a valuable tool to assess interactions between scales and how processes at different levels can affect transmission dynamics. We expand an existing model to illustrate the types of studies needed, showing that individual-level variation in viral dose acquired or needed for infection can influence the number of infectious vectors. It is critical that interactions within and among biological scales and levels of biological organization are understood for greater understanding of pathogen transmission with the ultimate goal of improving control of vector-borne pathogens.
Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series.

PubMed

Ferguson, Jake M; Ponciano, José M

2014-02-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. © 2013 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.
Exploring autonomy through computational biomodelling.

PubMed

Palfreyman, Niall

2009-07-01

The question of whether living organisms possess autonomy of action is tied up with the nature of causal efficacy. Yet the nature of organisms is such that they frequently defy conventional causal language. Did the fig wasp select the fig, or vice versa? Is this an epithelial cell because of its genetic structure, or because it develops within the epithelium? The intimate coupling of biological levels of organisation leads developmental systems theory to deconstruct the biological organism into a life-cycle process which constitutes itself from the resources available within a complete developmental system. This radical proposal necessarily raises questions regarding the ontological status of organisms: Does an organism possess existence distinguishable from its molecular composition and social environment? The ambiguity of biological causality makes such questions difficult to answer or even formulate, and computational biology has an important role to play in operationalising the language in which they are framed. In this article we review the role played by computational biomodels in shedding light on the ontological status of organisms. These models are drawn from backgrounds ranging from molecular kinetics to niche construction, and all attempt to trace biological processes to a causal, and therefore existent, source. We conclude that computational biomodelling plays a fertile role in furnishing a proof of concept for conjectures in the philosophy of biology, and suggests the need for a process-based ontology of biological systems.
Evolutionary game theory for physical and biological scientists. II. Population dynamics equations can be associated with interpretations

PubMed Central

Liao, David; Tlsty, Thea D.

2014-01-01

The use of mathematical equations to analyse population dynamics measurements is being increasingly applied to elucidate complex dynamic processes in biological systems, including cancer. Purely ‘empirical’ equations may provide sufficient accuracy to support predictions and therapy design. Nevertheless, interpretation of fitting equations in terms of physical and biological propositions can provide additional insights that can be used both to refine models that prove inconsistent with data and to understand the scope of applicability of models that validate. The purpose of this tutorial is to assist readers in mathematically associating interpretations with equations and to provide guidance in choosing interpretations and experimental systems to investigate based on currently available biological knowledge, techniques in mathematical and computational analysis and methods for in vitro and in vivo experiments. PMID:25097752
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…

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