Science.gov

Sample records for computing biological functions

  1. Computation of generating functions for biological molecules

    SciTech Connect

    Howell, J.A.; Smith, T.F.; Waterman, M.S.

    1980-08-01

    The object of this paper is to give algorithms and techniques for computing generating functions of certain RNA configurations. Combinatorics and symbolic computation are utilized to calculate the generating functions for small RNA molecules. From these generating functions, it is possible to obtain information about the bonding and structure of the molecules. Specific examples of interest to biology are given and discussed.

  2. Metacognition: computation, biology and function.

    PubMed

    Fleming, Stephen M; Dolan, Raymond J; Frith, Christopher D

    2012-05-19

    Many complex systems maintain a self-referential check and balance. In animals, such reflective monitoring and control processes have been grouped under the rubric of metacognition. In this introductory article to a Theme Issue on metacognition, we review recent and rapidly progressing developments from neuroscience, cognitive psychology, computer science and philosophy of mind. While each of these areas is represented in detail by individual contributions to the volume, we take this opportunity to draw links between disciplines, and highlight areas where further integration is needed. Specifically, we cover the definition, measurement, neurobiology and possible functions of metacognition, and assess the relationship between metacognition and consciousness. We propose a framework in which level of representation, order of behaviour and access consciousness are orthogonal dimensions of the conceptual landscape. PMID:22492746

  3. Metacognition: computation, biology and function

    PubMed Central

    Fleming, Stephen M.; Dolan, Raymond J.; Frith, Christopher D.

    2012-01-01

    Many complex systems maintain a self-referential check and balance. In animals, such reflective monitoring and control processes have been grouped under the rubric of metacognition. In this introductory article to a Theme Issue on metacognition, we review recent and rapidly progressing developments from neuroscience, cognitive psychology, computer science and philosophy of mind. While each of these areas is represented in detail by individual contributions to the volume, we take this opportunity to draw links between disciplines, and highlight areas where further integration is needed. Specifically, we cover the definition, measurement, neurobiology and possible functions of metacognition, and assess the relationship between metacognition and consciousness. We propose a framework in which level of representation, order of behaviour and access consciousness are orthogonal dimensions of the conceptual landscape. PMID:22492746

  4. Structure, function, and behaviour of computational models in systems biology

    PubMed Central

    2013-01-01

    Background Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. Results We present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. Conclusions The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research

  5. Function and dynamics of macromolecular complexes explored by integrative structural and computational biology.

    PubMed

    Purdy, Michael D; Bennett, Brad C; McIntire, William E; Khan, Ali K; Kasson, Peter M; Yeager, Mark

    2014-08-01

    Three vignettes exemplify the potential of combining EM and X-ray crystallographic data with molecular dynamics (MD) simulation to explore the architecture, dynamics and functional properties of multicomponent, macromolecular complexes. The first two describe how EM and X-ray crystallography were used to solve structures of the ribosome and the Arp2/3-actin complex, which enabled MD simulations that elucidated functional dynamics. The third describes how EM, X-ray crystallography, and microsecond MD simulations of a GPCR:G protein complex were used to explore transmembrane signaling by the β-adrenergic receptor. Recent technical advancements in EM, X-ray crystallography and computational simulation create unprecedented synergies for integrative structural biology to reveal new insights into heretofore intractable biological systems.

  6. Computational Systems Biology

    SciTech Connect

    McDermott, Jason E.; Samudrala, Ram; Bumgarner, Roger E.; Montogomery, Kristina; Ireton, Renee

    2009-05-01

    Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (“systems”) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing and computational processing power has driven a reinvention and expansion of this field. The expansions include not only modeling of small metabolic{Ishii, 2004 #1129; Ekins, 2006 #1601; Lafaye, 2005 #1744} and signaling systems{Stevenson-Paulik, 2006 #1742; Lafaye, 2005 #1744} but also modeling of the relationships between biological components in very large systems, incluyding whole cells and organisms {Ideker, 2001 #1124; Pe'er, 2001 #1172; Pilpel, 2001 #393; Ideker, 2002 #327; Kelley, 2003 #1117; Shannon, 2003 #1116; Ideker, 2004 #1111}{Schadt, 2003 #475; Schadt, 2006 #1661}{McDermott, 2002 #878; McDermott, 2005 #1271}. Generally these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships and general features that are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (for example, the identification of an evolutionary pattern), or are interesting and valuable to researchers working on a particular problem (for example highlight a previously unknown functional pathway). Two events have occurred to bring about the field computational systems biology to the forefront. One is the advent of high throughput methods that have generated large amounts of information about particular systems in the form of genetic studies, gene expression analyses (both protein and

  7. Synthetic biology: insights into biological computation.

    PubMed

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

    2016-04-18

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

  8. Synthetic biology: insights into biological computation.

    PubMed

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

    2016-04-18

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

  9. Computational systems chemical biology.

    PubMed

    Oprea, Tudor I; May, Elebeoba E; Leitão, Andrei; Tropsha, Alexander

    2011-01-01

    There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

  10. Computational biology for ageing.

    PubMed

    Wieser, Daniela; Papatheodorou, Irene; Ziehm, Matthias; Thornton, Janet M

    2011-01-12

    High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions.

  11. Computational biology for ageing

    PubMed Central

    Wieser, Daniela; Papatheodorou, Irene; Ziehm, Matthias; Thornton, Janet M.

    2011-01-01

    High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions. PMID:21115530

  12. Redox Biology: Computational Approaches to the Investigation of Functional Cysteine Residues

    PubMed Central

    Marino, Stefano M.

    2011-01-01

    Abstract Cysteine (Cys) residues serve many functions, such as catalysis, stabilization of protein structure through disulfides, metal binding, and regulation of protein function. Cys residues are also subject to numerous post-translational modifications. In recent years, various computational tools aiming at classifying and predicting different functional categories of Cys have been developed, particularly for structural and catalytic Cys. On the other hand, given complexity of the subject, bioinformatics approaches have been less successful for the investigation of regulatory Cys sites. In this review, we introduce different functional categories of Cys residues. For each category, an overview of state-of-the-art bioinformatics methods and tools is provided, along with examples of successful applications and potential limitations associated with each approach. Finally, we discuss Cys-based redox switches, which modify the view of distinct functional categories of Cys in proteins. Antioxid. Redox Signal. 15, 135–146. PMID:20812876

  13. Computational optimization and biological evolution.

    PubMed

    Goryanin, Igor

    2010-10-01

    Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

  14. Integrating cell biology, image analysis, and computational mechanical modeling to analyze the contributions of cellulose and xyloglucan to stomatal function.

    PubMed

    Rui, Yue; Yi, Hojae; Kandemir, Baris; Wang, James Z; Puri, Virendra M; Anderson, Charles T

    2016-06-01

    Cell walls are likely to be essential determinants of the amazing strength and flexibility of the guard cells that surround each stomatal pore in plants, but surprisingly little is known about cell wall composition, organization, and dynamics in guard cells. Recent analyses of cell wall organization and stomatal function in the guard cells of Arabidopsis thaliana mutants with defects in cellulose and xyloglucan have allowed for the development of new hypotheses about the relative contributions of these components to guard cell function. Advanced image analysis methods can allow for the automated detection of key structures, such as microtubules (MTs) and Cellulose Synthesis Complexes (CSCs), in guard cells, to help determine their contributions to stomatal function. A major challenge in the mechanical modeling of dynamic biological structures, such as guard cell walls, is to connect nanoscale features (e.g., wall polymers and their molecular interactions) with cell-scale mechanics; this challenge can be addressed by applying multiscale computational modeling that spans multiple spatial scales and physical attributes for cell walls.

  15. Integrating cell biology, image analysis, and computational mechanical modeling to analyze the contributions of cellulose and xyloglucan to stomatal function.

    PubMed

    Rui, Yue; Yi, Hojae; Kandemir, Baris; Wang, James Z; Puri, Virendra M; Anderson, Charles T

    2016-06-01

    Cell walls are likely to be essential determinants of the amazing strength and flexibility of the guard cells that surround each stomatal pore in plants, but surprisingly little is known about cell wall composition, organization, and dynamics in guard cells. Recent analyses of cell wall organization and stomatal function in the guard cells of Arabidopsis thaliana mutants with defects in cellulose and xyloglucan have allowed for the development of new hypotheses about the relative contributions of these components to guard cell function. Advanced image analysis methods can allow for the automated detection of key structures, such as microtubules (MTs) and Cellulose Synthesis Complexes (CSCs), in guard cells, to help determine their contributions to stomatal function. A major challenge in the mechanical modeling of dynamic biological structures, such as guard cell walls, is to connect nanoscale features (e.g., wall polymers and their molecular interactions) with cell-scale mechanics; this challenge can be addressed by applying multiscale computational modeling that spans multiple spatial scales and physical attributes for cell walls. PMID:27220916

  16. Computational Biology and High Performance Computing 2000

    SciTech Connect

    Simon, Horst D.; Zorn, Manfred D.; Spengler, Sylvia J.; Shoichet, Brian K.; Stewart, Craig; Dubchak, Inna L.; Arkin, Adam P.

    2000-10-19

    The pace of extraordinary advances in molecular biology has accelerated in the past decade due in large part to discoveries coming from genome projects on human and model organisms. The advances in the genome project so far, happening well ahead of schedule and under budget, have exceeded any dreams by its protagonists, let alone formal expectations. Biologists expect the next phase of the genome project to be even more startling in terms of dramatic breakthroughs in our understanding of human biology, the biology of health and of disease. Only today can biologists begin to envision the necessary experimental, computational and theoretical steps necessary to exploit genome sequence information for its medical impact, its contribution to biotechnology and economic competitiveness, and its ultimate contribution to environmental quality. High performance computing has become one of the critical enabling technologies, which will help to translate this vision of future advances in biology into reality. Biologists are increasingly becoming aware of the potential of high performance computing. The goal of this tutorial is to introduce the exciting new developments in computational biology and genomics to the high performance computing community.

  17. Computational biology of RNA interactions.

    PubMed

    Dieterich, Christoph; Stadler, Peter F

    2013-01-01

    The biodiversity of the RNA world has been underestimated for decades. RNA molecules are key building blocks, sensors, and regulators of modern cells. The biological function of RNA molecules cannot be separated from their ability to bind to and interact with a wide space of chemical species, including small molecules, nucleic acids, and proteins. Computational chemists, physicists, and biologists have developed a rich tool set for modeling and predicting RNA interactions. These interactions are to some extent determined by the binding conformation of the RNA molecule. RNA binding conformations are approximated with often acceptable accuracy by sequence and secondary structure motifs. Secondary structure ensembles of a given RNA molecule can be efficiently computed in many relevant situations by employing a standard energy model for base pair interactions and dynamic programming techniques. The case of bi-molecular RNA-RNA interactions can be seen as an extension of this approach. However, unbiased transcriptome-wide scans for local RNA-RNA interactions are computationally challenging yet become efficient if the binding motif/mode is known and other external information can be used to confine the search space. Computational methods are less developed for proteins and small molecules, which bind to RNA with very high specificity. Binding descriptors of proteins are usually determined by in vitro high-throughput assays (e.g., microarrays or sequencing). Intriguingly, recent experimental advances, which are mostly based on light-induced cross-linking of binding partners, render in vivo binding patterns accessible yet require new computational methods for careful data interpretation. The grand challenge is to model the in vivo situation where a complex interplay of RNA binders competes for the same target RNA molecule. Evidently, bioinformaticians are just catching up with the impressive pace of these developments.

  18. 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.

  19. Computational Skills for Biology Students

    ERIC Educational Resources Information Center

    Gross, Louis J.

    2008-01-01

    This interview with Distinguished Science Award recipient Louis J. Gross highlights essential computational skills for modern biology, including: (1) teaching concepts listed in the Math & Bio 2010 report; (2) illustrating to students that jobs today require quantitative skills; and (3) resources and materials that focus on computational skills.

  20. Limits of computational biology.

    PubMed

    Bray, Dennis

    2015-01-01

    Are we close to a complete inventory of living processes so that we might expect in the near future to reproduce every essential aspect necessary for life? Or are there mechanisms and processes in cells and organisms that are presently inaccessible to us? Here I argue that a close examination of a particularly well-understood system--that of Escherichia coli chemotaxis--shows we are still a long way from a complete description. There is a level of molecular uncertainty, particularly that responsible for fine-tuning and adaptation to myriad external conditions, which we presently cannot resolve or reproduce on a computer. Moreover, the same uncertainty exists for any process in any organism and is especially pronounced and important in higher animals such as humans. Embryonic development, tissue homeostasis, immune recognition, memory formation, and survival in the real world, all depend on vast numbers of subtle variations in cell chemistry most of which are presently unknown or only poorly characterized. Overcoming these limitations will require us to not only accumulate large quantities of highly detailed data but also develop new computational methods able to recapitulate the massively parallel processing of living cells.

  1. Limits of computational biology

    PubMed Central

    Bray, Dennis

    2015-01-01

    Abstract Are we close to a complete inventory of living processes so that we might expect in the near future to reproduce every essential aspect necessary for life? Or are there mechanisms and processes in cells and organisms that are presently inaccessible to us? Here I argue that a close examination of a particularly well-understood system— that of Escherichia coli chemotaxis— shows we are still a long way from a complete description. There is a level of molecular uncertainty, particularly that responsible for fine-tuning and adaptation to myriad external conditions, which we presently cannot resolve or reproduce on a computer. Moreover, the same uncertainty exists for any process in any organism and is especially pronounced and important in higher animals such as humans. Embryonic development, tissue homeostasis, immune recognition, memory formation, and survival in the real world, all depend on vast numbers of subtle variations in cell chemistry most of which are presently unknown or only poorly characterized. Overcoming these limitations will require us to not only accumulate large quantities of highly detailed data but also develop new computational methods able to recapitulate the massively parallel processing of living cells. PMID:25318467

  2. Aging and computational systems biology.

    PubMed

    Mooney, Kathleen M; Morgan, Amy E; Mc Auley, Mark T

    2016-01-01

    Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.

  3. Computational representation of biological systems

    SciTech Connect

    Frazier, Zach; McDermott, Jason E.; Guerquin, Michal; Samudrala, Ram

    2009-04-20

    Integration of large and diverse biological data sets is a daunting problem facing systems biology researchers. Exploring the complex issues of data validation, integration, and representation, we present a systematic approach for the management and analysis of large biological data sets based on data warehouses. Our system has been implemented in the Bioverse, a framework combining diverse protein information from a variety of knowledge areas such as molecular interactions, pathway localization, protein structure, and protein function.

  4. Molecular biology of chromosome function

    SciTech Connect

    Adolph, K.W. )

    1989-01-01

    The structure and function of chromosomes are closely linked since chromosome organization profoundly influences the activity of the genome in replication and transcription. Many fundamental results originated from studies of bacterial and viral systems chosen for their less-complex cycles. However, the processes of replication and transcription show differences between the higher and simpler systems. Three important subjects are covered in this volume: DNA replication and recombination, gene transcription, and chromosome organization. Eukaryotic, prokaryotic, and viral systems are discussed. The information presented is derived from techniques of structural biology and biophysics, including computer graphics and X-ray crystallography, as well as biochemistry, molecular and cell biology.

  5. Biomaterial science meets computational biology.

    PubMed

    Hutmacher, Dietmar W; Little, J Paige; Pettet, Graeme J; Loessner, Daniela

    2015-05-01

    There is a pressing need for a predictive tool capable of revealing a holistic understanding of fundamental elements in the normal and pathological cell physiology of organoids in order to decipher the mechanoresponse of cells. Therefore, the integration of a systems bioengineering approach into a validated mathematical model is necessary to develop a new simulation tool. This tool can only be innovative by combining biomaterials science with computational biology. Systems-level and multi-scale experimental data are incorporated into a single framework, thus representing both single cells and collective cell behaviour. Such a computational platform needs to be validated in order to discover key mechano-biological factors associated with cell-cell and cell-niche interactions.

  6. 2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research.

    PubMed

    Yang, Jack Y; Niemierko, Andrzej; Bajcsy, Ruzena; Xu, Dong; Athey, Brian D; Zhang, Aidong; Ersoy, Okan K; Li, Guo-Zheng; Borodovsky, Mark; Zhang, Joe C; Arabnia, Hamid R; Deng, Youping; Dunker, A Keith; Liu, Yunlong; Ghafoor, Arif

    2010-12-01

    Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT

  7. 2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research

    PubMed Central

    2010-01-01

    Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT

  8. Biological Basis For Computer Vision: Some Perspectives

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.

    1990-03-01

    Using biology as a basis for the development of sensors, devices and computer vision systems is a challenge to systems and vision scientists. It is also a field of promising research for engineering applications. Biological sensory systems, such as vision, touch and hearing, sense different physical phenomena from our environment, yet they possess some common mathematical functions. These mathematical functions are cast into the neural layers which are distributed throughout our sensory regions, sensory information transmission channels and in the cortex, the centre of perception. In this paper, we are concerned with the study of the biological vision system and the emulation of some of its mathematical functions, both retinal and visual cortex, for the development of a robust computer vision system. This field of research is not only intriguing, but offers a great challenge to systems scientists in the development of functional algorithms. These functional algorithms can be generalized for further studies in such fields as signal processing, control systems and image processing. Our studies are heavily dependent on the the use of fuzzy - neural layers and generalized receptive fields. Building blocks of such neural layers and receptive fields may lead to the design of better sensors and better computer vision systems. It is hoped that these studies will lead to the development of better artificial vision systems with various applications to vision prosthesis for the blind, robotic vision, medical imaging, medical sensors, industrial automation, remote sensing, space stations and ocean exploration.

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

    PubMed Central

    2010-01-01

    Background The robust storage, updating and utilization of information are necessary for the maintenance and perpetuation of dynamic systems. These systems can exist as constructs of metal-oxide semiconductors and silicon, as in a digital computer, or in the "wetware" of organic compounds, proteins and nucleic acids that make up biological organisms. We propose that there are essential functional properties of centralized information-processing systems; for digital computers these properties reside in the computer's hard drive, and for eukaryotic cells they are manifest in the DNA and associated structures. Methods Presented herein is a descriptive framework that compares DNA and its associated proteins and sub-nuclear structure with the structure and function of the computer hard drive. We identify four essential properties of information for a centralized storage and processing system: (1) orthogonal uniqueness, (2) low level formatting, (3) high level formatting and (4) translation of stored to usable form. The corresponding aspects of the DNA complex and a computer hard drive are categorized using this classification. This is intended to demonstrate a functional equivalence between the components of the two systems, and thus the systems themselves. Results Both the DNA complex and the computer hard drive contain components that fulfill the essential properties of a centralized information storage and processing system. The functional equivalence of these components provides insight into both the design process of engineered systems and the evolved solutions addressing similar system requirements. However, there are points where the comparison breaks down, particularly when there are externally imposed information-organizing structures on the computer hard drive. A specific example of this is the imposition of the File Allocation Table (FAT) during high level formatting of the computer hard drive and the subsequent loading of an operating system (OS). Biological

  10. Functional Aspects of Biological Networks

    NASA Astrophysics Data System (ADS)

    Sneppen, Kim

    2007-03-01

    We discuss biological networks with respect to 1) relative positioning and importance of high degree nodes, 2) function and signaling, 3) logic and dynamics of regulation. Visually the soft modularity of many real world networks can be characterized in terms of number of high and low degrees nodes positioned relative to each other in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). In these terms biological networks looks like rugged landscapes with separated peaks, hub proteins, which each are roughly as essential as any of the individual proteins on the periphery of the hub. Within each sup-domain of a molecular network one can often identify dynamical feedback mechanisms that falls into combinations of positive and negative feedback circuits. We will illustrate this with examples taken from phage regulation and bacterial uptake and regulation of small molecules. In particular we find that a double negative regulation often are replaced by a single positive link in unrelated organisms with same functional requirements. Overall we argue that network topology primarily reflects functional constraints. References: S. Maslov and K. Sneppen. ``Computational architecture of the yeast regulatory network." Phys. Biol. 2:94 (2005) A. Trusina et al. ``Functional alignment of regulatory networks: A study of temerate phages". Plos Computational Biology 1:7 (2005). J.B. Axelsen et al. ``Degree Landscapes in Scale-Free Networks" physics/0512075 (2005). A. Trusina et al. ``Hierarchy and Anti-Hierarchy in Real and Scale Free networks." PRL 92:178702 (2004) S. Semsey et al. ``Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli". (2006) q-bio.MN/0609042

  11. Deep learning for computational biology.

    PubMed

    Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold; Stegle, Oliver

    2016-01-01

    Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology. PMID:27474269

  12. Deep learning for computational biology.

    PubMed

    Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold; Stegle, Oliver

    2016-07-29

    Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

  13. Computational Biology, Advanced Scientific Computing, and Emerging Computational Architectures

    SciTech Connect

    2007-06-27

    This CRADA was established at the start of FY02 with $200 K from IBM and matching funds from DOE to support post-doctoral fellows in collaborative research between International Business Machines and Oak Ridge National Laboratory to explore effective use of emerging petascale computational architectures for the solution of computational biology problems. 'No cost' extensions of the CRADA were negotiated with IBM for FY03 and FY04.

  14. Computational Biology of Olfactory Receptors

    PubMed Central

    Crasto, Chiquito J.

    2011-01-01

    Olfactory receptors, in addition to being involved in first step of the physiological processes that leads to olfaction, occupy an important place in mammalian genomes. ORs constitute super families in these genomes. Elucidating ol-factory receptor function at a molecular level can be aided by a computationally derived structure and an understanding of its interactions with odor molecules. Experimental functional analyses of olfactory receptors in conjunction with computational studies serve to validate findings and generate hypotheses. We present here a review of the research efforts in: creating computational models of olfactory receptors, identifying binding strategies for these receptors with odorant molecules, performing medium to long range simulation studies of odor ligands in the receptor binding region, and identifying amino acid positions within the receptor that are responsible for ligand-binding and olfactory receptor activation. Written as a primer and a teaching tool, this review will help researchers extend the methodologies described herein to other GPCRs. PMID:21984880

  15. Multiscale Computational Models of Complex Biological Systems

    PubMed Central

    Walpole, Joseph; Papin, Jason A.; Peirce, Shayn M.

    2014-01-01

    Integration of data across spatial, temporal, and functional scales is a primary focus of biomedical engineering efforts. The advent of powerful computing platforms, coupled with quantitative data from high-throughput experimental platforms, has allowed multiscale modeling to expand as a means to more comprehensively investigate biological phenomena in experimentally relevant ways. This review aims to highlight recently published multiscale models of biological systems while using their successes to propose the best practices for future model development. We demonstrate that coupling continuous and discrete systems best captures biological information across spatial scales by selecting modeling techniques that are suited to the task. Further, we suggest how to best leverage these multiscale models to gain insight into biological systems using quantitative, biomedical engineering methods to analyze data in non-intuitive ways. These topics are discussed with a focus on the future of the field, the current challenges encountered, and opportunities yet to be realized. PMID:23642247

  16. Functions in Biological Kind Classification

    ERIC Educational Resources Information Center

    Lombrozo, Tania; Rehder, Bob

    2012-01-01

    Biological traits that serve functions, such as a zebra's coloration (for camouflage) or a kangaroo's tail (for balance), seem to have a special role in conceptual representations for biological kinds. In five experiments, we investigate whether and why functional features are privileged in biological kind classification. Experiment 1…

  17. Designing integrated computational biology pipelines visually.

    PubMed

    Jamil, Hasan M

    2013-01-01

    The long-term cost of developing and maintaining a computational pipeline that depends upon data integration and sophisticated workflow logic is too high to even contemplate "what if" or ad hoc type queries. In this paper, we introduce a novel application building interface for computational biology research, called VizBuilder, by leveraging a recent query language called BioFlow for life sciences databases. Using VizBuilder, it is now possible to develop ad hoc complex computational biology applications at throw away costs. The underlying query language supports data integration and workflow construction almost transparently and fully automatically, using a best effort approach. Users express their application by drawing it with VizBuilder icons and connecting them in a meaningful way. Completed applications are compiled and translated as BioFlow queries for execution by the data management system LifeDB, for which VizBuilder serves as a front end. We discuss VizBuilder features and functionalities in the context of a real life application after we briefly introduce BioFlow. The architecture and design principles of VizBuilder are also discussed. Finally, we outline future extensions of VizBuilder. To our knowledge, VizBuilder is a unique system that allows visually designing computational biology pipelines involving distributed and heterogeneous resources in an ad hoc manner.

  18. A computational functional genomics based self-limiting self-concentration mechanism of cell specialization as a biological role of jumping genes.

    PubMed

    Lötsch, Jörn; Ultsch, Alfred

    2016-01-01

    Specialization is ubiquitous in biological systems and its manifold mechanisms are active research topics. Although clearly adaptive, the way in which specialization of cells is realized remains incompletely understood as it requires the reshaping of a cell's genome to favor particular biological processes in the competition on a cell's functional capacity. Here, a self-specialization mechanism is identified as a possible biological role of jumping genes, in particular LINE-1 retrotransposition. The mechanism is self-limiting and consistent with its evolutionary preservation despite its likely gene-breaking effects. The scenario we studied was the need for a cell to process a longer exposition to an extraordinary situation, for example continuous exposure to the nociceptive input or the intake of addictive drugs. Both situations may evolve toward chronification. The mechanism involves competition within a gene set in which a subset of genes cooperating in particular biological processes. The subset carries a piece of information, consisting of the LINE-1 sequence, about the destruction of their functional competitor genes which are not involved in that process. During gene transcription, an active copy of LINE-1 is co-transcribed. At a certain low probability, a subsequently transcribed and thus actually exposed gene can be rendered nonfunctional by LINE-1 retrotransposition in a relevant gene part. As retrotransposition needs time it is unlikely that LINE-1 retrotranspose into its own carrier gene. This reshapes the cell genome toward self-specializing of those biological processes that are carried out with a high number of LINE-1 containing genes. Self-termination of the mechanism is achieved by allowing LINE-1 to also occasionally jump into the coding region of itself, thus destroying the information about competitor destruction by successively decreasing the number of LINE-1 until the mechanism ceases. Employing a computational functional genomics approach, we

  19. Computer Analogies: Teaching Molecular Biology and Ecology.

    ERIC Educational Resources Information Center

    Rice, Stanley; McArthur, John

    2002-01-01

    Suggests that computer science analogies can aid the understanding of gene expression, including the storage of genetic information on chromosomes. Presents a matrix of biology and computer science concepts. (DDR)

  20. The relativity of biological function.

    PubMed

    Laubichler, Manfred D; Stadler, Peter F; Prohaska, Sonja J; Nowick, Katja

    2015-12-01

    Function is a central concept in biological theories and explanations. Yet discussions about function are often based on a narrow understanding of biological systems and processes, such as idealized molecular systems or simple evolutionary, i.e., selective, dynamics. Conflicting conceptions of function continue to be used in the scientific literature to support certain claims, for instance about the fraction of "functional DNA" in the human genome. Here we argue that all biologically meaningful interpretations of function are necessarily context dependent. This implies that they derive their meaning as well as their range of applicability only within a specific theoretical and measurement context. We use this framework to shed light on the current debate about functional DNA and argue that without considering explicitly the theoretical and measurement contexts all attempts to integrate biological theories are prone to fail.

  1. The relativity of biological function.

    PubMed

    Laubichler, Manfred D; Stadler, Peter F; Prohaska, Sonja J; Nowick, Katja

    2015-12-01

    Function is a central concept in biological theories and explanations. Yet discussions about function are often based on a narrow understanding of biological systems and processes, such as idealized molecular systems or simple evolutionary, i.e., selective, dynamics. Conflicting conceptions of function continue to be used in the scientific literature to support certain claims, for instance about the fraction of "functional DNA" in the human genome. Here we argue that all biologically meaningful interpretations of function are necessarily context dependent. This implies that they derive their meaning as well as their range of applicability only within a specific theoretical and measurement context. We use this framework to shed light on the current debate about functional DNA and argue that without considering explicitly the theoretical and measurement contexts all attempts to integrate biological theories are prone to fail. PMID:26449352

  2. LINC: biology's revolutionary little computer.

    PubMed

    November, Joe

    2004-09-01

    The 1963 LINC (Laboratory INstrument Computer) stands at the center of two stories: the computerization of the biologist's laboratory and the advent of small-scale computing. The brainchild of Wesley Clark, 'the most brilliant computer designer of his generation', LINC was developed specifically to address the failure of biologists to adopt computer technology. To meet their unique needs, Clark built a machine the radical design of which defied and subverted the then dominant conventions of computer architecture.

  3. LINC: biology's revolutionary little computer.

    PubMed

    November, Joe

    2004-09-01

    The 1963 LINC (Laboratory INstrument Computer) stands at the center of two stories: the computerization of the biologist's laboratory and the advent of small-scale computing. The brainchild of Wesley Clark, 'the most brilliant computer designer of his generation', LINC was developed specifically to address the failure of biologists to adopt computer technology. To meet their unique needs, Clark built a machine the radical design of which defied and subverted the then dominant conventions of computer architecture. PMID:15350765

  4. Biological functions of sphingomyelins.

    PubMed

    Slotte, J Peter

    2013-10-01

    Sphingomyelin (SM) is a dominant sphingolipid in membranes of mammalian cells and this lipid class is specifically enriched in the plasma membrane, the endocytic recycling compartment, and the trans Golgi network. The distribution of SM and cholesterol among cellular compartments correlate. Sphingolipids have extensive hydrogen-bonding capabilities which together with their saturated nature facilitate the formation of sphingolipid and SM-enriched lateral domains in membranes. Cholesterol prefers to interact with SMs and this interaction has many important functional consequences. In this review, the synthesis, regulation, and intracellular distribution of SMs are discussed. The many direct roles played by membrane SM in various cellular functions and processes will also be discussed. These include involvement in the regulation of endocytosis and receptor-mediated ligand uptake, in ion channel and G-protein coupled receptor function, in protein sorting, and functioning as receptor molecules for various bacterial toxins, and for non-bacterial pore-forming toxins. SM is also an important constituent of the eye lens membrane, and is believed to participate in the regulation of various nuclear functions. SM is an independent risk factor in the development of cardiovascular disease, and new studies have shed light on possible mechanism behind its role in atherogenesis. PMID:23684760

  5. Computational Biology: A Strategic Initiative LDRD

    SciTech Connect

    Barksy, D; Colvin, M

    2002-02-07

    The goal of this Strategic Initiative LDRD project was to establish at LLNL a new core capability in computational biology, combining laboratory strengths in high performance computing, molecular biology, and computational chemistry and physics. As described in this report, this project has been very successful in achieving this goal. This success is demonstrated by the large number of referred publications, invited talks, and follow-on research grants that have resulted from this project. Additionally, this project has helped build connections to internal and external collaborators and funding agencies that will be critical to the long-term vitality of LLNL programs in computational biology. Most importantly, this project has helped establish on-going research groups in the Biology and Biotechnology Research Program, the Physics and Applied Technology Directorate, and the Computation Directorate. These groups include three laboratory staff members originally hired as post-doctoral researchers for this strategic initiative.

  6. Biocellion: accelerating computer simulation of multicellular biological system models

    PubMed Central

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

    2014-01-01

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

  7. Analog Computer Laboratory with Biological Examples.

    ERIC Educational Resources Information Center

    Strebel, Donald E.

    1979-01-01

    The use of biological examples in teaching applications of the analog computer is discussed and several examples from mathematical ecology, enzyme kinetics, and tracer dynamics are described. (Author/GA)

  8. Computational Biology in microRNA.

    PubMed

    Li, Yue; Zhang, Zhaolei

    2015-01-01

    MicroRNA (miRNA) is a class of small endogenous noncoding RNA species, which regulate gene expression post-transcriptionally by forming imperfect base-pair at the 3' untranslated regions of the messenger RNAs. Since the 1993 discovery of the first miRNA let-7 in worms, a vast number of studies have been dedicated to functionally characterizing miRNAs with a special emphasis on their roles in cancer. A single miRNA can potentially target ∼ 400 distinct genes, and there are over a 1000 distinct endogenous miRNAs in the human genome. Thus, miRNAs are likely involved in virtually all biological processes and pathways including carcinogenesis. However, functionally characterizing miRNAs hinges on the accurate identification of their mRNA targets, which has been a challenging problem due to imperfect base-pairing and condition-specific miRNA regulatory dynamics. In this review, we will survey the current state-of-the-art computational methods to predict miRNA targets, which are divided into three main categories: (1) sequence-based methods that primarily utilizes the canonical seed-match model, evolutionary conservation, and binding energy; (2) expression-based target prediction methods using the increasingly available miRNA and mRNA expression data measured for the same sample; and (3) network-based method that aims identify miRNA regulatory modules, which reflect their synergism in conferring a global impact to the biological system of interest. We hope that the review will serve as a good reference to the new comers to the ever-growing miRNA research field as well as veterans, who would appreciate the detailed review on the technicalities, strength, and limitations of each representative computational method.

  9. Computational investigations of HNO in biology

    PubMed Central

    Zhang, Yong

    2013-01-01

    HNO (nitroxyl) has been found to have many physiological effects in numerous biological processes. Computational investigations have been employed to help understand the structural properties of HNO complexes and HNO reactivities in some interesting biologically relevant systems. The following computational aspects were reviewed in this work: 1) structural and energetic properties of HNO isomers; 2) interactions between HNO and non-metal molecules; 3) structural and spectroscopic properties of HNO metal complexes; 4) HNO reactions with biologically important non-metal systems; 5) involvement of HNO in reactions of metal complexes and metalloproteins. Results indicate that computational investigations are very helpful to elucidate interesting experimental phenomena and provide new insights into unique structural, spectroscopic, and mechanistic properties of HNO involvement in biology. PMID:23103077

  10. India's Computational Biology Growth and Challenges.

    PubMed

    Chakraborty, Chiranjib; Bandyopadhyay, Sanghamitra; Agoramoorthy, Govindasamy

    2016-09-01

    India's computational science is growing swiftly due to the outburst of internet and information technology services. The bioinformatics sector of India has been transforming rapidly by creating a competitive position in global bioinformatics market. Bioinformatics is widely used across India to address a wide range of biological issues. Recently, computational researchers and biologists are collaborating in projects such as database development, sequence analysis, genomic prospects and algorithm generations. In this paper, we have presented the Indian computational biology scenario highlighting bioinformatics-related educational activities, manpower development, internet boom, service industry, research activities, conferences and trainings undertaken by the corporate and government sectors. Nonetheless, this new field of science faces lots of challenges.

  11. 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.

  12. 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. PMID:25790483

  13. Computational approaches to metabolic engineering utilizing systems biology and synthetic biology.

    PubMed

    Fong, Stephen S

    2014-08-01

    Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.

  14. Biological pathways as communicating computer systems.

    PubMed

    Kwiatkowska, Marta Z; Heath, John K

    2009-08-15

    Time and cost are the enemies of cell biology. The number of experiments required to rigorously dissect and comprehend a pathway of even modest complexity is daunting. Methods are needed to formulate biological pathways in a machine-analysable fashion, which would automate the process of considering all possible experiments in a complex pathway and identify those that command attention. In this Essay, we describe a method that is based on the exploitation of computational tools that were originally developed to analyse reactive communicating computer systems such as mobile phones and web browsers. In this approach, the biological process is articulated as an executable computer program that can be interrogated using methods that were developed to analyse complex software systems. Using case studies of the FGF, MAPK and Delta/Notch pathways, we show that the application of this technology can yield interesting insights into the behaviour of signalling pathways, which have subsequently been corroborated by experimental data. PMID:19657015

  15. BIONET: national computer resource for molecular biology.

    PubMed Central

    Smith, D H; Brutlag, D; Friedland, P; Kedes, L H

    1986-01-01

    This paper describes briefly the BIONET National Computer Resource for Molecular Biology. This presentation is intended as information for scientists in molecular biology and related disciplines who require access to computational methods for sequence analysis. We describe the goals, and the service and research opportunities offered to the community by BIONET, the relationship of BIONET to other national and regional resources, our recent efforts toward distribution of the resource to BIONET Satellites, and procedures for investigators to gain access to the Resource. PMID:3945548

  16. Computational systems biology for aging research.

    PubMed

    Mc Auley, Mark T; Mooney, Kathleen M

    2015-01-01

    Computational modelling is a key component of systems biology and integrates with the other techniques discussed thus far in this book by utilizing a myriad of data that are being generated to quantitatively represent and simulate biological systems. This chapter will describe what computational modelling involves; the rationale for using it, and the appropriateness of modelling for investigating the aging process. How a model is assembled and the different theoretical frameworks that can be used to build a model are also discussed. In addition, the chapter will describe several models which demonstrate the effectiveness of each computational approach for investigating the constituents of a healthy aging trajectory. Specifically, a number of models will be showcased which focus on the complex age-related disorders associated with unhealthy aging. To conclude, we discuss the future applications of computational systems modelling to aging research.

  17. Applicability of computational systems biology in toxicology.

    PubMed

    Kongsbak, Kristine; Hadrup, Niels; Audouze, Karine; Vinggaard, Anne Marie

    2014-07-01

    Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research.

  18. The biological function of consciousness

    PubMed Central

    Earl, Brian

    2014-01-01

    This research is an investigation of whether consciousness—one's ongoing experience—influences one's behavior and, if so, how. Analysis of the components, structure, properties, and temporal sequences of consciousness has established that, (1) contrary to one's intuitive understanding, consciousness does not have an active, executive role in determining behavior; (2) consciousness does have a biological function; and (3) consciousness is solely information in various forms. Consciousness is associated with a flexible response mechanism (FRM) for decision-making, planning, and generally responding in nonautomatic ways. The FRM generates responses by manipulating information and, to function effectively, its data input must be restricted to task-relevant information. The properties of consciousness correspond to the various input requirements of the FRM; and when important information is missing from consciousness, functions of the FRM are adversely affected; both of which indicate that consciousness is the input data to the FRM. Qualitative and quantitative information (shape, size, location, etc.) are incorporated into the input data by a qualia array of colors, sounds, and so on, which makes the input conscious. This view of the biological function of consciousness provides an explanation why we have experiences; why we have emotional and other feelings, and why their loss is associated with poor decision-making; why blindsight patients do not spontaneously initiate responses to events in their blind field; why counter-habitual actions are only possible when the intended action is in mind; and the reason for inattentional blindness. PMID:25140159

  19. The "Biologically-Inspired Computing" Column

    NASA Technical Reports Server (NTRS)

    Hinchey, Mike

    2007-01-01

    Self-managing systems, whether viewed from the perspective of Autonomic Computing, or from that of another initiative, offers a holistic vision for the development and evolution of biologically-inspired computer-based systems. It aims to bring new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing costs. A case can certainly be made that all computer-based systems should exhibit autonomic properties [6], and we envisage greater interest in, and uptake of, autonomic principles in future system development.

  20. A Computer Applications Course for Biology Students.

    ERIC Educational Resources Information Center

    Ralph, Charles L.

    1989-01-01

    Describes a computer applications course developed for undergraduate biology students (primarily sophomores) to teach word processing, database and spreadsheet programs, graphing programs, telecommunications, and file transfer procedures. AppleWorks software is discussed, course content is explained, and the microcomputer laboratory is described.…

  1. Micro-Computers in Biology Inquiry.

    ERIC Educational Resources Information Center

    Barnato, Carolyn; Barrett, Kathy

    1981-01-01

    Describes the modification of computer programs (BISON and POLLUT) to accommodate species and areas indigenous to the Pacific Coast area. Suggests that these programs, suitable for PET microcomputers, may foster a long-term, ongoing, inquiry-directed approach in biology. (DS)

  2. Network Coding for Function Computation

    ERIC Educational Resources Information Center

    Appuswamy, Rathinakumar

    2011-01-01

    In this dissertation, the following "network computing problem" is considered. Source nodes in a directed acyclic network generate independent messages and a single receiver node computes a target function f of the messages. The objective is to maximize the average number of times f can be computed per network usage, i.e., the "computing…

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

    PubMed

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

    2012-01-01

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

  4. Understanding biological computation: reliable learning and recognition.

    PubMed Central

    Hogg, T; Huberman, B A

    1984-01-01

    We experimentally examine the consequences of the hypothesis that the brain operates reliably, even though individual components may intermittently fail, by computing with dynamical attractors. Specifically, such a mechanism exploits dynamic collective behavior of a system with attractive fixed points in its phase space. In contrast to the usual methods of reliable computation involving a large number of redundant elements, this technique of self-repair only requires collective computation with a few units, and it is amenable to quantitative investigation. Experiments on parallel computing arrays show that this mechanism leads naturally to rapid self-repair, adaptation to the environment, recognition and discrimination of fuzzy inputs, and conditional learning, properties that are commonly associated with biological computation. PMID:6593731

  5. India's Computational Biology Growth and Challenges.

    PubMed

    Chakraborty, Chiranjib; Bandyopadhyay, Sanghamitra; Agoramoorthy, Govindasamy

    2016-09-01

    India's computational science is growing swiftly due to the outburst of internet and information technology services. The bioinformatics sector of India has been transforming rapidly by creating a competitive position in global bioinformatics market. Bioinformatics is widely used across India to address a wide range of biological issues. Recently, computational researchers and biologists are collaborating in projects such as database development, sequence analysis, genomic prospects and algorithm generations. In this paper, we have presented the Indian computational biology scenario highlighting bioinformatics-related educational activities, manpower development, internet boom, service industry, research activities, conferences and trainings undertaken by the corporate and government sectors. Nonetheless, this new field of science faces lots of challenges. PMID:27465042

  6. A formal language for computational systems biology.

    PubMed

    Errampalli, Daniel D; Priami, Corrado; Quaglia, Paola

    2004-01-01

    The post-genomic era has opened new insights into the complex biochemical reaction systems present in the cell and has generated huge amount of information. The biological systems are highly complex and can overwhelm the numerically computable models. Therefore, models employing symbolical techniques might provide a faster insight. This paper presents some preliminary results and recent trends in the above direction. Specifically, it presents an overview of the main features of some formalisms and techniques from the field of specification languages for concurrency and mobility, which have been proposed to model and simulate the dynamics of the interaction of complex biological systems. The ultimate goal of these symbolic approaches is the modeling, analysis, simulation, and hopefully prediction of the behavior of biological systems (vs. biological components).

  7. Program Computes Thermodynamic Functions

    NASA Technical Reports Server (NTRS)

    Mcbride, Bonnie J.; Gordon, Sanford

    1994-01-01

    PAC91 is latest in PAC (Properties and Coefficients) series. Two principal features are to provide means of (1) generating theoretical thermodynamic functions from molecular constants and (2) least-squares fitting of these functions to empirical equations. PAC91 written in FORTRAN 77 to be machine-independent.

  8. Computational biology and bioinformatics in Nigeria.

    PubMed

    Fatumo, Segun A; Adoga, Moses P; Ojo, Opeolu O; Oluwagbemi, Olugbenga; Adeoye, Tolulope; Ewejobi, Itunuoluwa; Adebiyi, Marion; Adebiyi, Ezekiel; Bewaji, Clement; Nashiru, Oyekanmi

    2014-04-01

    Over the past few decades, major advances in the field of molecular biology, coupled with advances in genomic technologies, have led to an explosive growth in the biological data generated by the scientific community. The critical need to process and analyze such a deluge of data and turn it into useful knowledge has caused bioinformatics to gain prominence and importance. Bioinformatics is an interdisciplinary research area that applies techniques, methodologies, and tools in computer and information science to solve biological problems. In Nigeria, bioinformatics has recently played a vital role in the advancement of biological sciences. As a developing country, the importance of bioinformatics is rapidly gaining acceptance, and bioinformatics groups comprised of biologists, computer scientists, and computer engineers are being constituted at Nigerian universities and research institutes. In this article, we present an overview of bioinformatics education and research in Nigeria. We also discuss professional societies and academic and research institutions that play central roles in advancing the discipline in Nigeria. Finally, we propose strategies that can bolster bioinformatics education and support from policy makers in Nigeria, with potential positive implications for other developing countries.

  9. Computational Biology and Bioinformatics in Nigeria

    PubMed Central

    Fatumo, Segun A.; Adoga, Moses P.; Ojo, Opeolu O.; Oluwagbemi, Olugbenga; Adeoye, Tolulope; Ewejobi, Itunuoluwa; Adebiyi, Marion; Adebiyi, Ezekiel; Bewaji, Clement; Nashiru, Oyekanmi

    2014-01-01

    Over the past few decades, major advances in the field of molecular biology, coupled with advances in genomic technologies, have led to an explosive growth in the biological data generated by the scientific community. The critical need to process and analyze such a deluge of data and turn it into useful knowledge has caused bioinformatics to gain prominence and importance. Bioinformatics is an interdisciplinary research area that applies techniques, methodologies, and tools in computer and information science to solve biological problems. In Nigeria, bioinformatics has recently played a vital role in the advancement of biological sciences. As a developing country, the importance of bioinformatics is rapidly gaining acceptance, and bioinformatics groups comprised of biologists, computer scientists, and computer engineers are being constituted at Nigerian universities and research institutes. In this article, we present an overview of bioinformatics education and research in Nigeria. We also discuss professional societies and academic and research institutions that play central roles in advancing the discipline in Nigeria. Finally, we propose strategies that can bolster bioinformatics education and support from policy makers in Nigeria, with potential positive implications for other developing countries. PMID:24763310

  10. Molecular biology for the computer scientist

    SciTech Connect

    Subramaniam, S.

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. This tutorial focuses on the following: the cell and the structural and energetic basis of life molecules; membrane structure; bioenergetics; eukaryotes and functional specialization; examples of molecular systems; evolution and diversity; and modern techniques in molecular biology.

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

    PubMed

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

    2012-01-01

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

  12. Standards and Ontologies in Computational Systems Biology

    PubMed Central

    Sauro, Herbert M.; Bergmann, Frank

    2009-01-01

    With the growing importance of computational models in systems biology there has been much interest in recent years to develop standard model interchange languages that permit biologists to easily exchange models between different software tools. In this chapter two chief model exchange standards, SBML and CellML are described. In addition, other related features including visual layout initiatives, ontologies and best practices for model annotation are discussed. Software tools such as developer libraries and basic editing tools are also introduced together with a discussion on the future of modeling languages and visualization tools in systems biology. PMID:18793134

  13. Computational Models for Neuromuscular Function

    PubMed Central

    Valero-Cuevas, Francisco J.; Hoffmann, Heiko; Kurse, Manish U.; Kutch, Jason J.; Theodorou, Evangelos A.

    2011-01-01

    Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data. PMID:21687779

  14. A new online computational biology curriculum.

    PubMed

    Searls, David B

    2014-06-01

    A recent proliferation of Massive Open Online Courses (MOOCs) and other web-based educational resources has greatly increased the potential for effective self-study in many fields. This article introduces a catalog of several hundred free video courses of potential interest to those wishing to expand their knowledge of bioinformatics and computational biology. The courses are organized into eleven subject areas modeled on university departments and are accompanied by commentary and career advice.

  15. Computational Biology: Modeling Chronic Renal Allograft Injury.

    PubMed

    Stegall, Mark D; Borrows, Richard

    2015-01-01

    New approaches are needed to develop more effective interventions to prevent long-term rejection of organ allografts. Computational biology provides a powerful tool to assess the large amount of complex data that is generated in longitudinal studies in this area. This manuscript outlines how our two groups are using mathematical modeling to analyze predictors of graft loss using both clinical and experimental data and how we plan to expand this approach to investigate specific mechanisms of chronic renal allograft injury.

  16. A New Online Computational Biology Curriculum

    PubMed Central

    Searls, David B.

    2014-01-01

    A recent proliferation of Massive Open Online Courses (MOOCs) and other web-based educational resources has greatly increased the potential for effective self-study in many fields. This article introduces a catalog of several hundred free video courses of potential interest to those wishing to expand their knowledge of bioinformatics and computational biology. The courses are organized into eleven subject areas modeled on university departments and are accompanied by commentary and career advice. PMID:24921255

  17. Computational systems biology in cancer brain metastasis.

    PubMed

    Peng, Huiming; Tan, Hua; Zhao, Weiling; Jin, Guangxu; Sharma, Sambad; Xing, Fei; Watabe, Kounosuke; Zhou, Xiaobo

    2016-01-01

    Brain metastases occur in 20-40% of patients with advanced malignancies. A better understanding of the mechanism of this disease will help us to identify novel therapeutic strategies. In this review, we will discuss the systems biology approaches used in this area, including bioinformatics and mathematical modeling. Bioinformatics has been used for identifying the molecular mechanisms driving brain metastasis and mathematical modeling methods for analyzing dynamics of a system and predicting optimal therapeutic strategies. We will illustrate the strategies, procedures, and computational techniques used for studying systems biology in cancer brain metastases. We will give examples on how to use a systems biology approach to analyze a complex disease. Some of the approaches used to identify relevant networks, pathways, and possibly biomarkers in metastasis will be reviewed into details. Finally, certain challenges and possible future directions in this area will also be discussed.

  18. The case for biological quantum computer elements

    NASA Astrophysics Data System (ADS)

    Baer, Wolfgang; Pizzi, Rita

    2009-05-01

    An extension to vonNeumann's analysis of quantum theory suggests self-measurement is a fundamental process of Nature. By mapping the quantum computer to the brain architecture we will argue that the cognitive experience results from a measurement of a quantum memory maintained by biological entities. The insight provided by this mapping suggests quantum effects are not restricted to small atomic and nuclear phenomena but are an integral part of our own cognitive experience and further that the architecture of a quantum computer system parallels that of a conscious brain. We will then review the suggestions for biological quantum elements in basic neural structures and address the de-coherence objection by arguing for a self- measurement event model of Nature. We will argue that to first order approximation the universe is composed of isolated self-measurement events which guaranties coherence. Controlled de-coherence is treated as the input/output interactions between quantum elements of a quantum computer and the quantum memory maintained by biological entities cognizant of the quantum calculation results. Lastly we will present stem-cell based neuron experiments conducted by one of us with the aim of demonstrating the occurrence of quantum effects in living neural networks and discuss future research projects intended to reach this objective.

  19. Automatic computation of transfer functions

    SciTech Connect

    Atcitty, Stanley; Watson, Luke Dale

    2015-04-14

    Technologies pertaining to the automatic computation of transfer functions for a physical system are described herein. The physical system is one of an electrical system, a mechanical system, an electromechanical system, an electrochemical system, or an electromagnetic system. A netlist in the form of a matrix comprises data that is indicative of elements in the physical system, values for the elements in the physical system, and structure of the physical system. Transfer functions for the physical system are computed based upon the netlist.

  20. From computational quantum chemistry to computational biology: experiments and computations are (full) partners.

    PubMed

    Ma, Buyong; Nussinov, Ruth

    2004-12-01

    Computations are being integrated into biological research at an increasingly fast pace. This has not only changed the way in which biological information is managed; it has also changed the way in which experiments are planned in order to obtain information from nature. Can experiments and computations be full partners? Computational chemistry has expanded over the years, proceeding from computations of a hydrogen molecule toward the challenging goal of systems biology, which attempts to handle the entire living cell. Applying theories from ab initio quantum mechanics to simplified models, the virtual worlds explored by computations provide replicas of real-world phenomena. At the same time, the virtual worlds can affect our perception of the real world. Computational biology targets a world of complex organization, for which a unified theory is unlikely to exist. A computational biology model, even if it has a clear physical or chemical basis, may not reduce to physics and chemistry. At the molecular level, computational biology and experimental biology have already been partners, mutually benefiting from each other. For the perception to become reality, computation and experiment should be united as full partners in biological research.

  1. PERSPECTIVE: From computational quantum chemistry to computational biology: experiments and computations are (full) partners

    NASA Astrophysics Data System (ADS)

    Ma, Buyong; Nussinov, Ruth

    2004-12-01

    Computations are being integrated into biological research at an increasingly fast pace. This has not only changed the way in which biological information is managed; it has also changed the way in which experiments are planned in order to obtain information from nature. Can experiments and computations be full partners? Computational chemistry has expanded over the years, proceeding from computations of a hydrogen molecule toward the challenging goal of systems biology, which attempts to handle the entire living cell. Applying theories from ab initio quantum mechanics to simplified models, the virtual worlds explored by computations provide replicas of real-world phenomena. At the same time, the virtual worlds can affect our perception of the real world. Computational biology targets a world of complex organization, for which a unified theory is unlikely to exist. A computational biology model, even if it has a clear physical or chemical basis, may not reduce to physics and chemistry. At the molecular level, computational biology and experimental biology have already been partners, mutually benefiting from each other. For the perception to become reality, computation and experiment should be united as full partners in biological research.

  2. Computational Tools to Assess Turbine Biological Performance

    SciTech Connect

    Richmond, Marshall C.; Serkowski, John A.; Rakowski, Cynthia L.; Strickler, Brad; Weisbeck, Molly; Dotson, Curtis L.

    2014-07-24

    Public Utility District No. 2 of Grant County (GCPUD) operates the Priest Rapids Dam (PRD), a hydroelectric facility on the Columbia River in Washington State. The dam contains 10 Kaplan-type turbine units that are now more than 50 years old. Plans are underway to refit these aging turbines with new runners. The Columbia River at PRD is a migratory pathway for several species of juvenile and adult salmonids, so passage of fish through the dam is a major consideration when upgrading the turbines. In this paper, a method for turbine biological performance assessment (BioPA) is demonstrated. Using this method, a suite of biological performance indicators is computed based on simulated data from a CFD model of a proposed turbine design. Each performance indicator is a measure of the probability of exposure to a certain dose of an injury mechanism. Using known relationships between the dose of an injury mechanism and frequency of injury (dose–response) from laboratory or field studies, the likelihood of fish injury for a turbine design can be computed from the performance indicator. By comparing the values of the indicators from proposed designs, the engineer can identify the more-promising alternatives. We present an application of the BioPA method for baseline risk assessment calculations for the existing Kaplan turbines at PRD that will be used as the minimum biological performance that a proposed new design must achieve.

  3. Computer Experiments for Function Approximations

    SciTech Connect

    Chang, A; Izmailov, I; Rizzo, S; Wynter, S; Alexandrov, O; Tong, C

    2007-10-15

    This research project falls in the domain of response surface methodology, which seeks cost-effective ways to accurately fit an approximate function to experimental data. Modeling and computer simulation are essential tools in modern science and engineering. A computer simulation can be viewed as a function that receives input from a given parameter space and produces an output. Running the simulation repeatedly amounts to an equivalent number of function evaluations, and for complex models, such function evaluations can be very time-consuming. It is then of paramount importance to intelligently choose a relatively small set of sample points in the parameter space at which to evaluate the given function, and then use this information to construct a surrogate function that is close to the original function and takes little time to evaluate. This study was divided into two parts. The first part consisted of comparing four sampling methods and two function approximation methods in terms of efficiency and accuracy for simple test functions. The sampling methods used were Monte Carlo, Quasi-Random LP{sub {tau}}, Maximin Latin Hypercubes, and Orthogonal-Array-Based Latin Hypercubes. The function approximation methods utilized were Multivariate Adaptive Regression Splines (MARS) and Support Vector Machines (SVM). The second part of the study concerned adaptive sampling methods with a focus on creating useful sets of sample points specifically for monotonic functions, functions with a single minimum and functions with a bounded first derivative.

  4. [Biological function of vitamin A].

    PubMed

    Dusheiko, A A

    1980-01-01

    The paper deals with the generalized results of complex studies in the biological function of vitamin A conducted on the chicken glandular stomach. It is found out that at a certain diet the glandular stomach of chickens manifests a specific response to vitamin A deficiency: the amount of cells in the organ increases, their differentiation changes, the tissues relation is disturbed, hyperfunction is observed. The content of acid glycosaminglycanes varies, their biosynthesis is inhibited, the concentration of cations in the intercellular space increases. On the basis of these facts a conclusion is drawn that disturbances in the structure and function of glycocalyx are decisive in development of A-avitaminosis. It is established that the stomach intermediate zone mucosa secretion contains vitamin A which is strongly bound with a specific water-insoluble glycolipoprotein. The absence of vitamin A causes a disturbance in lipidation, glycosylation sulphatation and hydration of the secretion. A hypothesis is put forward according to which, vitamin A, joining the specific protein synthetized on ribosomes, initiates organization of the lipid phase. The latter might determine the direction and rate of protein transport in the system of smooth membranes where it is glycosidated. Formation of the lipid phase is disturbed when vitamin A is absent. This leads to changes in the protein migration pathways in the Golgi apparatus, which results in glycosidation disturbance as well. According to the same principle vitamin A may articipate in formation of glycolipoproteins not only of secretion but also of the intercellular substance, plasmatic membranes nuclei, lysosomes and other organelles.

  5. Developing an online programme in computational biology.

    PubMed

    Vincent, Heather M; Page, Christopher

    2013-11-01

    Much has been written about the need for continuing education and training to enable life scientists and computer scientists to manage and exploit the different types of biological data now becoming available. Here we describe the development of an online programme that combines short training courses, so that those who require an educational programme can progress to complete a formal qualification. Although this flexible approach fits the needs of course participants, it does not fit easily within the organizational structures of a campus-based university.

  6. Multiobjective optimization in bioinformatics and computational biology.

    PubMed

    Handl, Julia; Kell, Douglas B; Knowles, Joshua

    2007-01-01

    This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

  7. A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

    SciTech Connect

    James, Conrad D.; Schiess, Adrian B.; Howell, Jamie; Baca, Michael J.; Partridge, L. Donald; Finnegan, Patrick Sean; Wolfley, Steven L.; Dagel, Daryl James; Spahn, Olga Blum; Harper, Jason C.; Pohl, Kenneth Roy; Mickel, Patrick R.; Lohn, Andrew; Marinella, Matthew

    2013-10-01

    The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we will instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.

  8. Nanomaterials in biological environment: a review of computer modelling studies.

    PubMed

    Makarucha, A J; Todorova, N; Yarovsky, I

    2011-02-01

    Nanotechnology is set to impact a vast range of fields, including computer science, materials technology, engineering/manufacturing and medicine. As nanotechnology grows so does exposure to nanostructured materials, thus investigation of the effects of nanomaterials on biological systems is paramount. Computational techniques can allow investigation of these systems at the nanoscale, providing insight into otherwise unexaminable properties, related to both the intentional and unintentional effects of nanomaterials. Herein, we review the current literature involving computational modelling of nanoparticles and biological systems. This literature has highlighted the common modes in which nanostructured materials interact with biological molecules such as membranes, peptides/proteins and DNA. Hydrophobic interactions are the most favoured, with π-stacking of the aromatic side-chains common when binding to a carbonaceous nanoparticle or surface. van der Waals forces are found to dominate in the insertion process of DNA molecules into carbon nanotubes. Generally, nanoparticles have been observed to disrupt the tertiary structure of proteins due to the curvature and atomic arrangement of the particle surface. Many hydrophobic nanoparticles are found to be able to transverse a lipid membrane, with some nanoparticles even causing mechanical damage to the membrane, thus potentially leading to cytotoxic effects. Current computational techniques have revealed how some nanoparticles interact with biological systems. However, further research is required to determine both useful applications and possible cytotoxic effects that nanoparticles may have on DNA, protein and membrane structure and function within biosystems.

  9. Systems Biology in Immunology – A Computational Modeling Perspective

    PubMed Central

    Germain, Ronald N.; Meier-Schellersheim, Martin; Nita-Lazar, Aleksandra; Fraser, Iain D. C.

    2011-01-01

    Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and conduct simulations of immune function, We provide descriptions of the key data gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease. PMID:21219182

  10. Computational Biology Support: RECOMB Conference Series (Conference Support)

    SciTech Connect

    Michael Waterman

    2006-06-15

    This funding was support for student and postdoctoral attendance at the Annual Recomb Conference from 2001 to 2005. The RECOMB Conference series was founded in 1997 to provide a scientific forum for theoretical advances in computational biology and their applications in molecular biology and medicine. The conference series aims at attracting research contributions in all areas of computational molecular biology. Typical, but not exclusive, the topics of interest are: Genomics, Molecular sequence analysis, Recognition of genes and regulatory elements, Molecular evolution, Protein structure, Structural genomics, Gene Expression, Gene Networks, Drug Design, Combinatorial libraries, Computational proteomics, and Structural and functional genomics. The origins of the conference came from the mathematical and computational side of the field, and there remains to be a certain focus on computational advances. However, the effective use of computational techniques to biological innovation is also an important aspect of the conference. The conference had a growing number of attendees, topping 300 in recent years and often exceeding 500. The conference program includes between 30 and 40 contributed papers, that are selected by a international program committee with around 30 experts during a rigorous review process rivaling the editorial procedure for top-rate scientific journals. In previous years papers selection has been made from up to 130--200 submissions from well over a dozen countries. 10-page extended abstracts of the contributed papers are collected in a volume published by ACM Press and Springer, and are available at the conference. Full versions of a selection of the papers are published annually in a special issue of the Journal of Computational Biology devoted to the RECOMB Conference. A further point in the program is a lively poster session. From 120-300 posters have been presented each year at RECOMB 2000. One of the highlights of each RECOMB conference is a

  11. Neural computation of arithmetic functions

    NASA Technical Reports Server (NTRS)

    Siu, Kai-Yeung; Bruck, Jehoshua

    1990-01-01

    An area of application of neural networks is considered. A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n-bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions.

  12. Computational design of digital and memory biological devices

    PubMed Central

    Rodrigo, Guillermo

    2008-01-01

    The use of combinatorial optimization techniques with computational design allows the development of automated methods to design biological systems. Automatic design integrates design principles in an unsupervised algorithm to sample a larger region of the biological network space, at the topology and parameter levels. The design of novel synthetic transcriptional networks with targeted behaviors will be key to understand the design principles underlying biological networks. In this work, we evolve transcriptional networks towards a targeted dynamics, by using a library of promoters and coding sequences, to design a complex biological memory device. The designed sequential transcription network implements a JK-Latch, which is fully predictable and richer than other memory devices. Furthermore, we present designs of transcriptional devices behaving as logic gates, and we show how to create digital behavior from analog promoters. Our procedure allows us to propose a scenario for the evolution of multi-functional genetic networks. In addition, we discuss the decomposability of regulatory networks in terms of genetic modules to develop a given cellular function. Summary. We show how to use an automated procedure to design logic and sequential transcription circuits. This methodology will allow advancing the rational design of biological devices to more complex systems, and we propose the first design of a biological JK-latch memory device. Electronic supplementary material The online version of this article (doi:10.1007/s11693-008-9017-0) contains supplementary material, which is available to authorized users. PMID:19003443

  13. Toward computational cumulative biology by combining models of biological datasets.

    PubMed

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

    2014-01-01

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

  14. Toward Computational Cumulative Biology by Combining Models of Biological Datasets

    PubMed Central

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

    2014-01-01

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

  15. FUNCTION GENERATOR FOR ANALOGUE COMPUTERS

    DOEpatents

    Skramstad, H.K.; Wright, J.H.; Taback, L.

    1961-12-12

    An improved analogue computer is designed which can be used to determine the final ground position of radioactive fallout particles in an atomic cloud. The computer determines the fallout pattern on the basis of known wind velocity and direction at various altitudes, and intensity of radioactivity in the mushroom cloud as a function of particle size and initial height in the cloud. The output is then displayed on a cathode-ray tube so that the average or total luminance of the tube screen at any point represents the intensity of radioactive fallout at the geographical location represented by that point. (AEC)

  16. Computational Laser Spectroscopy in a Biological Tissue

    PubMed Central

    Gantri, M.; Trabelsi, H.; Sediki, E.; Ben Salah, R.

    2010-01-01

    We present a numerical spectroscopic study of visible and infrared laser radiation in a biological tissue. We derive a solution of a general two-dimensional time dependent radiative transfer equation in a tissue-like medium. The used model is suitable for many situations especially when the external source is time-dependent or continuous. We use a control volume-discrete ordinate method associated with an implicit three-level second-order time differencing scheme. We consider a very thin rectangular biological-tissue-like medium submitted to a visible or a near infrared light sources. The RTE is solved for a set of different wavelength source. All sources are assumed to be monochromatic and collimated. The energetic fluence rate is computed at a set of detector points on the boundaries. According to the source type, we investigate either the steady-state or transient response of the medium. The used model is validated in the case of a heterogeneous tissue-like medium using referencing experimental results from the literature. Also, the developed model is used to study changes on transmitted light in a rat-liver tissue-like medium. Optical properties depend on the source wavelength and they are taken from the literature. In particular, light-transmission in the medium is studied for continuous wave and for short pulse. PMID:20396377

  17. Applications of large-scale density functional theory in biology.

    PubMed

    Cole, Daniel J; Hine, Nicholas D M

    2016-10-01

    Density functional theory (DFT) has become a routine tool for the computation of electronic structure in the physics, materials and chemistry fields. Yet the application of traditional DFT to problems in the biological sciences is hindered, to a large extent, by the unfavourable scaling of the computational effort with system size. Here, we review some of the major software and functionality advances that enable insightful electronic structure calculations to be performed on systems comprising many thousands of atoms. We describe some of the early applications of large-scale DFT to the computation of the electronic properties and structure of biomolecules, as well as to paradigmatic problems in enzymology, metalloproteins, photosynthesis and computer-aided drug design. With this review, we hope to demonstrate that first principles modelling of biological structure-function relationships are approaching a reality. PMID:27494095

  18. Applications of large-scale density functional theory in biology.

    PubMed

    Cole, Daniel J; Hine, Nicholas D M

    2016-10-01

    Density functional theory (DFT) has become a routine tool for the computation of electronic structure in the physics, materials and chemistry fields. Yet the application of traditional DFT to problems in the biological sciences is hindered, to a large extent, by the unfavourable scaling of the computational effort with system size. Here, we review some of the major software and functionality advances that enable insightful electronic structure calculations to be performed on systems comprising many thousands of atoms. We describe some of the early applications of large-scale DFT to the computation of the electronic properties and structure of biomolecules, as well as to paradigmatic problems in enzymology, metalloproteins, photosynthesis and computer-aided drug design. With this review, we hope to demonstrate that first principles modelling of biological structure-function relationships are approaching a reality.

  19. Applications of large-scale density functional theory in biology

    NASA Astrophysics Data System (ADS)

    Cole, Daniel J.; Hine, Nicholas D. M.

    2016-10-01

    Density functional theory (DFT) has become a routine tool for the computation of electronic structure in the physics, materials and chemistry fields. Yet the application of traditional DFT to problems in the biological sciences is hindered, to a large extent, by the unfavourable scaling of the computational effort with system size. Here, we review some of the major software and functionality advances that enable insightful electronic structure calculations to be performed on systems comprising many thousands of atoms. We describe some of the early applications of large-scale DFT to the computation of the electronic properties and structure of biomolecules, as well as to paradigmatic problems in enzymology, metalloproteins, photosynthesis and computer-aided drug design. With this review, we hope to demonstrate that first principles modelling of biological structure-function relationships are approaching a reality.

  20. Integrating Functional, Developmental and Evolutionary Biology into Biology Curricula

    ERIC Educational Resources Information Center

    Haave, Neil

    2012-01-01

    A complete understanding of life involves how organisms are able to function in their environment and how they arise. Understanding how organisms arise involves both their evolution and development. Thus to completely comprehend living things, biology must study their function, development and evolution. Previous proposals for standardized…

  1. Reinforcement learning: Computational theory and biological mechanisms.

    PubMed

    Doya, Kenji

    2007-05-01

    Reinforcement learning is a computational framework for an active agent to learn behaviors on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial system such as a robot or a computer program. The reward can be food, water, money, or whatever measure of the performance of the agent. The theory of reinforcement learning, which was developed in an artificial intelligence community with intuitions from animal learning theory, is now giving a coherent account on the function of the basal ganglia. It now serves as the "common language" in which biologists, engineers, and social scientists can exchange their problems and findings. This article reviews the basic theoretical framework of reinforcement learning and discusses its recent and future contributions toward the understanding of animal behaviors and human decision making.

  2. Reinforcement learning: Computational theory and biological mechanisms

    PubMed Central

    Doya, Kenji

    2007-01-01

    Reinforcement learning is a computational framework for an active agent to learn behaviors on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial system such as a robot or a computer program. The reward can be food, water, money, or whatever measure of the performance of the agent. The theory of reinforcement learning, which was developed in an artificial intelligence community with intuitions from animal learning theory, is now giving a coherent account on the function of the basal ganglia. It now serves as the “common language” in which biologists, engineers, and social scientists can exchange their problems and findings. This article reviews the basic theoretical framework of reinforcement learning and discusses its recent and future contributions toward the understanding of animal behaviors and human decision making. PMID:19404458

  3. First Steps in Computational Systems Biology: A Practical Session in Metabolic Modeling and Simulation

    ERIC Educational Resources Information Center

    Reyes-Palomares, Armando; Sanchez-Jimenez, Francisca; Medina, Miguel Angel

    2009-01-01

    A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever…

  4. Structural biology computing: Lessons for the biomedical research sciences.

    PubMed

    Morin, Andrew; Sliz, Piotr

    2013-11-01

    The field of structural biology, whose aim is to elucidate the molecular and atomic structures of biological macromolecules, has long been at the forefront of biomedical sciences in adopting and developing computational research methods. Operating at the intersection between biophysics, biochemistry, and molecular biology, structural biology's growth into a foundational framework on which many concepts and findings of molecular biology are interpreted1 has depended largely on parallel advancements in computational tools and techniques. Without these computing advances, modern structural biology would likely have remained an exclusive pursuit practiced by few, and not become the widely practiced, foundational field it is today. As other areas of biomedical research increasingly embrace research computing techniques, the successes, failures and lessons of structural biology computing can serve as a useful guide to progress in other biomedically related research fields.

  5. Aluminium in Biological Environments: A Computational Approach

    PubMed Central

    Mujika, Jon I; Rezabal, Elixabete; Mercero, Jose M; Ruipérez, Fernando; Costa, Dominique; Ugalde, Jesus M; Lopez, Xabier

    2014-01-01

    The increased availability of aluminium in biological environments, due to human intervention in the last century, raises concerns on the effects that this so far “excluded from biology” metal might have on living organisms. Consequently, the bioinorganic chemistry of aluminium has emerged as a very active field of research. This review will focus on our contributions to this field, based on computational studies that can yield an understanding of the aluminum biochemistry at a molecular level. Aluminium can interact and be stabilized in biological environments by complexing with both low molecular mass chelants and high molecular mass peptides. The speciation of the metal is, nonetheless, dictated by the hydrolytic species dominant in each case and which vary according to the pH condition of the medium. In blood, citrate and serum transferrin are identified as the main low molecular mass and high molecular mass molecules interacting with aluminium. The complexation of aluminium to citrate and the subsequent changes exerted on the deprotonation pathways of its tritable groups will be discussed along with the mechanisms for the intake and release of aluminium in serum transferrin at two pH conditions, physiological neutral and endosomatic acidic. Aluminium can substitute other metals, in particular magnesium, in protein buried sites and trigger conformational disorder and alteration of the protonation states of the protein's sidechains. A detailed account of the interaction of aluminium with proteic sidechains will be given. Finally, it will be described how alumnium can exert oxidative stress by stabilizing superoxide radicals either as mononuclear aluminium or clustered in boehmite. The possibility of promotion of Fenton reaction, and production of hydroxyl radicals will also be discussed. PMID:24757505

  6. Computational complexity of Boolean functions

    NASA Astrophysics Data System (ADS)

    Korshunov, Aleksei D.

    2012-02-01

    Boolean functions are among the fundamental objects of discrete mathematics, especially in those of its subdisciplines which fall under mathematical logic and mathematical cybernetics. The language of Boolean functions is convenient for describing the operation of many discrete systems such as contact networks, Boolean circuits, branching programs, and some others. An important parameter of discrete systems of this kind is their complexity. This characteristic has been actively investigated starting from Shannon's works. There is a large body of scientific literature presenting many fundamental results. The purpose of this survey is to give an account of the main results over the last sixty years related to the complexity of computation (realization) of Boolean functions by contact networks, Boolean circuits, and Boolean circuits without branching. Bibliography: 165 titles.

  7. Biologically inspired path to quantum computer

    NASA Astrophysics Data System (ADS)

    Ogryzko, Vasily; Ozhigov, Yuri

    2014-12-01

    We describe an approach to quantum computer inspired by the information processing at the molecular level in living cells. It is based on the separation of a small ensemble of qubits inside the living system (e.g., a bacterial cell), such that coherent quantum states of this ensemble remain practically unchanged for a long time. We use the notion of a quantum kernel to describe such an ensemble. Quantum kernel is not strictly connected with certain particles; it permanently exchanges atoms and molecules with the environment, which makes quantum kernel a virtual notion. There are many reasons to expect that the state of quantum kernel of a living system can be treated as the stationary state of some Hamiltonian. While the quantum kernel is responsible for the stability of dynamics at the time scale of cellular life, at the longer inter-generation time scale it can change, varying smoothly in the course of biological evolution. To the first level of approximation, quantum kernel can be described in the framework of qubit modification of Jaynes-Cummings-Hubbard model, in which the relaxation corresponds to the exchange of matter between quantum kernel and the rest of the cell and is represented as Lindblad super-operators.

  8. Functional Translational Readthrough: A Systems Biology Perspective

    PubMed Central

    Schueren, Fabian

    2016-01-01

    Translational readthrough (TR) has come into renewed focus because systems biology approaches have identified the first human genes undergoing functional translational readthrough (FTR). FTR creates functional extensions to proteins by continuing translation of the mRNA downstream of the stop codon. Here we review recent developments in TR research with a focus on the identification of FTR in humans and the systems biology methods that have spurred these discoveries. PMID:27490485

  9. Functional Translational Readthrough: A Systems Biology Perspective.

    PubMed

    Schueren, Fabian; Thoms, Sven

    2016-08-01

    Translational readthrough (TR) has come into renewed focus because systems biology approaches have identified the first human genes undergoing functional translational readthrough (FTR). FTR creates functional extensions to proteins by continuing translation of the mRNA downstream of the stop codon. Here we review recent developments in TR research with a focus on the identification of FTR in humans and the systems biology methods that have spurred these discoveries. PMID:27490485

  10. Computational Biology for Drug Discovery and Characterization

    SciTech Connect

    Lightstone, F C; Bennion, B J

    2009-02-24

    We proposed to determine the underpinnings of a high-throughput computational infrastructure that would support future efforts in therapeutics against biothreat pathogens. Existing modeling capabilities focus on pathogen detection, but extending such capabilities to high-throughput molecular docking would lead to a proactive method to guide the development of therapeutics. This project will focus on determining the feasibility of extending current databases to accommodate molecular docking. We will also examine the feasibility of massive parallelization of docking algorithms and the utility of docking libraries. Transferring this new technique to a high-performance computing (HPC) platform at LLNL would result in a unique capability not available elsewhere in government or industry. We have accomplished the proposed work defined in this LDRD FS study. (1) We successfully defined the feasibility of using three different small-molecule databases for high-throughput docking, the NCI diversity set, ZINC and the ACD. (2) We analyzed the accuracy and parallelization capabilities of six separate docking programs: DOCK, AutoDock, FlexX, Glide, and eHiTS. Each program is completely amenable to parallel execution. The fastest code was eHiTS, and Glide was the most accurate. (3) Customizing large libraries was cumbersome without the proper software, making the databases a bit difficult to tailor. The ZINC database has some prefiltered versions. (4) Scripts were created for quality and job control functions. Further development is needed for analysis and visualization needs. The successful conclusion of this project enables LLNL to have a high-throughput computational docking capability where we have evaluated the codes to specific docking problems and utilized LLNL's HPC for significant gains in performance. We have established a CRADA with an industrial partner (funded by the National Institutes of Health) that will fully utilize this technology for biodefense therapeutic

  11. A complex systems approach to computational molecular biology

    SciTech Connect

    Lapedes, A. |

    1993-09-01

    We report on the containing research program at Santa Fe Institute that applies complex systems methodology to computational molecular biology. Two aspects are stressed here are the use of co-evolving adaptive neutral networks for determining predictable protein structure classifications, and the use of information theory to elucidate protein structure and function. A ``snapshot`` of the current state of research in these two topics is presented, representing the present state of two major research thrusts in the program of Genetic Data and Sequence Analysis at the Santa Fe Institute.

  12. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  13. A first attempt to bring computational biology into advanced high school biology classrooms.

    PubMed

    Gallagher, Suzanne Renick; Coon, William; Donley, Kristin; Scott, Abby; Goldberg, Debra S

    2011-10-01

    Computer science has become ubiquitous in many areas of biological research, yet most high school and even college students are unaware of this. As a result, many college biology majors graduate without adequate computational skills for contemporary fields of biology. The absence of a computational element in secondary school biology classrooms is of growing concern to the computational biology community and biology teachers who would like to acquaint their students with updated approaches in the discipline. We present a first attempt to correct this absence by introducing a computational biology element to teach genetic evolution into advanced biology classes in two local high schools. Our primary goal was to show students how computation is used in biology and why a basic understanding of computation is necessary for research in many fields of biology. This curriculum is intended to be taught by a computational biologist who has worked with a high school advanced biology teacher to adapt the unit for his/her classroom, but a motivated high school teacher comfortable with mathematics and computing may be able to teach this alone. In this paper, we present our curriculum, which takes into consideration the constraints of the required curriculum, and discuss our experiences teaching it. We describe the successes and challenges we encountered while bringing this unit to high school students, discuss how we addressed these challenges, and make suggestions for future versions of this curriculum.We believe that our curriculum can be a valuable seed for further development of computational activities aimed at high school biology students. Further, our experiences may be of value to others teaching computational biology at this level. Our curriculum can be obtained at http://ecsite.cs.colorado.edu/?page_id=149#biology or by contacting the authors.

  14. Function-Based Algorithms for Biological Sequences

    ERIC Educational Resources Information Center

    Mohanty, Pragyan Sheela P.

    2015-01-01

    Two problems at two different abstraction levels of computational biology are studied. At the molecular level, efficient pattern matching algorithms in DNA sequences are presented. For gene order data, an efficient data structure is presented capable of storing all gene re-orderings in a systematic manner. A common characteristic of presented…

  15. Computer-Based Semantic Network in Molecular Biology: A Demonstration.

    ERIC Educational Resources Information Center

    Callman, Joshua L.; And Others

    This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

  16. MORT: a powerful foundational library for computational biology and CADD

    PubMed Central

    2014-01-01

    Background A foundational library called MORT (Molecular Objects and Relevant Templates) for the development of new software packages and tools employed in computational biology and computer-aided drug design (CADD) is described here. Results MORT contains several advantages compared with the other libraries. Firstly, MORT written in C++ natively supports the paradigm of object-oriented design, and thus it can be understood and extended easily. Secondly, MORT employs the relational model to represent a molecule, and it is more convenient and flexible than the traditional hierarchical model employed by many other libraries. Thirdly, a lot of functions have been included in this library, and a molecule can be manipulated easily at different levels. For example, it can parse a variety of popular molecular formats (MOL/SDF, MOL2, PDB/ENT, SMILES/SMARTS, etc.), create the topology and coordinate files for the simulations supported by AMBER, calculate the energy of a specific molecule based on the AMBER force fields, etc. Conclusions We believe that MORT can be used as a foundational library for programmers to develop new programs and applications for computational biology and CADD. Source code of MORT is available at http://cadd.suda.edu.cn/MORT/index.htm.

  17. Computational cell biology at the home of the helix.

    PubMed

    Ward, Jonathan J; Nédélec, Francois J

    2010-06-01

    The Computational Cell Biology Conference, held jointly by the Cold Spring Harbor Laboratory and the Wellcome Trust, was convened in the grand surroundings of Hinxton Hall near Cambridge, UK. The high quality of the research presented at the meeting confirmed that the field of computational cell biology is maturing rapidly, which mirrors the progression of cell biology from being mostly descriptive to a more quantitative discipline.

  18. ADVANCED COMPUTATIONAL METHODS IN DOSE MODELING: APPLICATION OF COMPUTATIONAL BIOPHYSICAL TRANSPORT, COMPUTATIONAL CHEMISTRY, AND COMPUTATIONAL BIOLOGY

    EPA Science Inventory

    Computational toxicology (CompTox) leverages the significant gains in computing power and computational techniques (e.g., numerical approaches, structure-activity relationships, bioinformatics) realized over the last few years, thereby reducing costs and increasing efficiency i...

  19. The biological microprocessor, or how to build a computer with biological parts.

    PubMed

    Moe-Behrens, Gerd Hg

    2013-01-01

    Systemics, a revolutionary paradigm shift in scientific thinking, with applications in systems biology, and synthetic biology, have led to the idea of using silicon computers and their engineering principles as a blueprint for the engineering of a similar machine made from biological parts. Here we describe these building blocks and how they can be assembled to a general purpose computer system, a biological microprocessor. Such a system consists of biological parts building an input / output device, an arithmetic logic unit, a control unit, memory, and wires (busses) to interconnect these components. A biocomputer can be used to monitor and control a biological system.

  20. The biological microprocessor, or how to build a computer with biological parts

    PubMed Central

    Moe-Behrens, Gerd HG

    2013-01-01

    Systemics, a revolutionary paradigm shift in scientific thinking, with applications in systems biology, and synthetic biology, have led to the idea of using silicon computers and their engineering principles as a blueprint for the engineering of a similar machine made from biological parts. Here we describe these building blocks and how they can be assembled to a general purpose computer system, a biological microprocessor. Such a system consists of biological parts building an input / output device, an arithmetic logic unit, a control unit, memory, and wires (busses) to interconnect these components. A biocomputer can be used to monitor and control a biological system. PMID:24688733

  1. Data-intensive computing laying foundation for biological breakthroughs

    SciTech Connect

    Hachigian, David J.

    2007-06-18

    Finding a different way is the goal of the Data-Intensive Computing for Complex Biological Systems (Biopilot) project—a joint research effort between the Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory funded by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. The two national laboratories, both of whom are world leaders in computing and computational sciences, are teaming to support areas of biological research in urgent need of data-intensive computing capabilities.

  2. Modeling Functional Motions of Biological Systems by Customized Natural Moves.

    PubMed

    Demharter, Samuel; Knapp, Bernhard; Deane, Charlotte M; Minary, Peter

    2016-08-23

    Simulating the functional motions of biomolecular systems requires large computational resources. We introduce a computationally inexpensive protocol for the systematic testing of hypotheses regarding the dynamic behavior of proteins and nucleic acids. The protocol is based on natural move Monte Carlo, a highly efficient conformational sampling method with built-in customization capabilities that allows researchers to design and perform a large number of simulations to investigate functional motions in biological systems. We demonstrate the use of this protocol on both a protein and a DNA case study. Firstly, we investigate the plasticity of a class II major histocompatibility complex in the absence of a bound peptide. Secondly, we study the effects of the epigenetic mark 5-hydroxymethyl on cytosine on the structure of the Dickerson-Drew dodecamer. We show how our customized natural moves protocol can be used to investigate causal relationships of functional motions in biological systems. PMID:27558715

  3. Biological cluster evaluation for gene function prediction.

    PubMed

    Klie, Sebastian; Nikoloski, Zoran; Selbig, Joachim

    2014-06-01

    Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set.

  4. Functional model of biological neural networks

    PubMed Central

    2010-01-01

    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. PMID:22132040

  5. Insights into the structure-function relationship of disease resistance protein HCTR in maize (Zea mays L.): a computational structural biology approach.

    PubMed

    Dehury, Budheswar; Sahu, Mousumi; Patra, Mahesh Chandra; Sarma, Kishore; Sahu, Jagajjit; Sen, Priyabrata; Modi, Mahendra Kumar; Choudhury, Manabendra Dutta; Barooah, Madhumita

    2013-09-01

    The disease resistance gene Hm1 of maize encodes a NADPH-dependent reductase enzyme, HC-toxin reductase (HCTR) that detoxifies the HC toxin secreted by the race specific fungus Cochliobolus carbonum race 1. HCTR enzyme shares 29.6% sequence identity with dihydroflavonol reductase (DFR) of grape, a key enzyme involved in flavonoid biosynthesis. Here we report the comparative modelling, molecular dynamics simulation and docking studies to explain the structure-function relationship and the mode of cofactor (NADPH) binding in HCTR enzyme at the molecular level. The nucleotide binding domain of modelled HCTR adopts a classic Rossmann fold and possesses a consensus glycine rich GxGxxG motif. Molecular simulation studies suggested that HCTR model retained stability throughout the simulation in aqueous solution. HCTR model showed considerable structural identities with the cofactor binding site of DFR, but significant difference in the catalytic site might be the reason of functional divergence between these families of proteins. Similarly electrostatic surface potential analysis of both HCTR and DFR revealed profound variations in the charge distribution over the substrate binding site, which can be correlated with the sequence variability and may suggest distinct substrate-binding patterns and differences in the catalytic mechanism. Docking results indicated Phe19, Gly21, Arg40, Thr90, Gly208, Arg218, Glu221 and Thr222 are important residues for cofactor (NADPH) binding through strong hydrogen bonding and electrostatic interactions. Alanine scanning and analysis of docking energies of mutant proteins suggested that Phe19, and Arg40 are two critical residues for the cofactor binding. The result from the present study is expected to pave the way for exploration of similar genes in other economically important crop varieties. PMID:24004829

  6. UC Merced Center for Computational Biology Final Report

    SciTech Connect

    Colvin, Michael; Watanabe, Masakatsu

    2010-11-30

    Final report for the UC Merced Center for Computational Biology. The Center for Computational Biology (CCB) was established to support multidisciplinary scientific research and academic programs in computational biology at the new University of California campus in Merced. In 2003, the growing gap between biology research and education was documented in a report from the National Academy of Sciences, Bio2010 Transforming Undergraduate Education for Future Research Biologists. We believed that a new type of biological sciences undergraduate and graduate programs that emphasized biological concepts and considered biology as an information science would have a dramatic impact in enabling the transformation of biology. UC Merced as newest UC campus and the first new U.S. research university of the 21st century was ideally suited to adopt an alternate strategy - to create a new Biological Sciences majors and graduate group that incorporated the strong computational and mathematical vision articulated in the Bio2010 report. CCB aimed to leverage this strong commitment at UC Merced to develop a new educational program based on the principle of biology as a quantitative, model-driven science. Also we expected that the center would be enable the dissemination of computational biology course materials to other university and feeder institutions, and foster research projects that exemplify a mathematical and computations-based approach to the life sciences. As this report describes, the CCB has been successful in achieving these goals, and multidisciplinary computational biology is now an integral part of UC Merced undergraduate, graduate and research programs in the life sciences. The CCB began in fall 2004 with the aid of an award from U.S. Department of Energy (DOE), under its Genomes to Life program of support for the development of research and educational infrastructure in the modern biological sciences. This report to DOE describes the research and academic programs

  7. Modelling, abstraction, and computation in systems biology: A view from computer science.

    PubMed

    Melham, Tom

    2013-04-01

    Systems biology is centrally engaged with computational modelling across multiple scales and at many levels of abstraction. Formal modelling, precise and formalised abstraction relationships, and computation also lie at the heart of computer science--and over the past decade a growing number of computer scientists have been bringing their discipline's core intellectual and computational tools to bear on biology in fascinating new ways. This paper explores some of the apparent points of contact between the two fields, in the context of a multi-disciplinary discussion on conceptual foundations of systems biology.

  8. Excellence in Computational Biology and Informatics — EDRN Public Portal

    Cancer.gov

    9th Early Detection Research Network (EDRN) Scientific Workshop. Excellence in Computational Biology and Informatics: Sponsored by the EDRN Data Sharing Subcommittee Moderator: Daniel Crichton, M.S., NASA Jet Propulsion Laboratory

  9. Computational Modeling, Formal Analysis, and Tools for Systems Biology

    PubMed Central

    Bartocci, Ezio; Lió, Pietro

    2016-01-01

    As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science. PMID:26795950

  10. Computational Modeling, Formal Analysis, and Tools for Systems Biology.

    PubMed

    Bartocci, Ezio; Lió, Pietro

    2016-01-01

    As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.

  11. [Biological functions of tin and disease].

    PubMed

    Arakawa, Yasuaki; Tomiyama, Kenichi

    2016-07-01

    Tin generates a wide variety of biological functions due to its chemical character. In this article, the modes of the biological functions of tin(especially organotin compounds) are reviewed, with special emphasis on the connection with the immune system, brain nervous system and endocrine system, on the basis of our data. To sum up this article, the biological functions of organotin compounds appear to be due to the following several processes: (1) their incorporation into the cells in vesicle form through fusion or in a similar manner to their incorporation in cationic form; (2) transport to and accumulation in the regions of the Golgi apparatus and endoplasmic reticulum (ER), but not to or in the plasma membrane or nucleus because of their hydrophobicity; (3) inhibition of intracellular phospholipid transport between organelles due to impairment of the structures and functions of the Golgi apparatus and ER; (4) inhibition of the membrane-mediated signal transduction system leading to DNA synthesis via phospholipid turnover and Ca2+ mobilization, as in cell proliferation systems; (5) disturbance of the trace element balance and the localization of certain elements; (6) disorders of membrane-mediated Ca2+ homeostasis via various channel functions including Zn modulation on the plasma and organelle membranes, and protein phosphorylation, as in the signal transduction systems of memory and olfaction; (7) necrosis or apoptosis in vivo or toxic cell death in vitro. PMID:27455812

  12. Insights into the functional biology of schistosomes

    PubMed Central

    2011-01-01

    The need to discover new treatments for human schistosomiasis has been an important driver for molecular research on schistosomes, a major breakthrough being the publication of the Schistosoma mansoni and Schistosoma japonicum genomes in 2009. This 'Primer' considers recent advances in the understanding of schistosome biology by providing a snapshot of selected areas of contemporary functional schistosome research, including that on the genome, the tegument, cell signalling and developmental biology, offering biologists a valuable insight into the life of these fascinating parasites at the basic and molecular level. PMID:22013990

  13. Data-intensive computing laying foundation for biological breakthroughs

    SciTech Connect

    Straatsma, TP

    2007-06-18

    Biological breakthroughs critical to solving society’s most challenging problems require new and innovative tools and a “different way” to analyze the enormous amounts of data being generated. This article for the Breakthroughs magazine focuses on the Data-Intensive Computing for Complex Biological Systems (Biopilot) project—a joint research effort between the Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory funded by the U.S. Department of Energy’s Office of Advanced Scientific Computing Research. The two national laboratories, both of whom are world leaders in computing and computational sciences, are teaming to support areas of biological research in urgent need of data-intensive computing capabilities.

  14. Computer Modelling of Biological Molecules: Free Resources on the Internet.

    ERIC Educational Resources Information Center

    Millar, Neil

    1996-01-01

    Describes a three-dimensional computer modeling system for biological molecules which is suitable for sixth-form teaching. Consists of the modeling program "RasMol" together with structure files of proteins, DNA, and small biological molecules. Describes how the whole system can be downloaded from various sites on the Internet. (Author/JRH)

  15. The computational linguistics of biological sequences

    SciTech Connect

    Searls, D.

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Protein sequences are analogous in many respects, particularly their folding behavior. Proteins have a much richer variety of interactions, but in theory the same linguistic principles could come to bear in describing dependencies between distant residues that arise by virtue of three-dimensional structure. This tutorial will concentrate on nucleic acid sequences.

  16. Computer display and manipulation of biological molecules

    NASA Technical Reports Server (NTRS)

    Coeckelenbergh, Y.; Macelroy, R. D.; Hart, J.; Rein, R.

    1978-01-01

    This paper describes a computer model that was designed to investigate the conformation of molecules, macromolecules and subsequent complexes. Utilizing an advanced 3-D dynamic computer display system, the model is sufficiently versatile to accommodate a large variety of molecular input and to generate data for multiple purposes such as visual representation of conformational changes, and calculation of conformation and interaction energy. Molecules can be built on the basis of several levels of information. These include the specification of atomic coordinates and connectivities and the grouping of building blocks and duplicated substructures using symmetry rules found in crystals and polymers such as proteins and nucleic acids. Called AIMS (Ames Interactive Molecular modeling System), the model is now being used to study pre-biotic molecular evolution toward life.

  17. Using a Computer Animation to Teach High School Molecular Biology

    ERIC Educational Resources Information Center

    Rotbain, Yosi; Marbach-Ad, Gili; Stavy, Ruth

    2008-01-01

    We present an active way to use a computer animation in secondary molecular genetics class. For this purpose we developed an activity booklet that helps students to work interactively with a computer animation which deals with abstract concepts and processes in molecular biology. The achievements of the experimental group were compared with those…

  18. DNA computing, computation complexity and problem of biological evolution rate.

    PubMed

    Melkikh, Alexey V

    2008-12-01

    An analogy between the evolution of organisms and some complex computational problems (cryptosystem cracking, determination of the shortest path in a graph) is considered. It is shown that in the absence of a priori information about possible species of organisms such a problem is complex (is rated in the class NP) and cannot be solved in a polynomial number of steps. This conclusion suggests the need for re-examination of evolution mechanisms. Ideas of a deterministic approach to the evolution are discussed.

  19. Computing functions by approximating the input

    NASA Astrophysics Data System (ADS)

    Goldberg, Mayer

    2012-12-01

    In computing real-valued functions, it is ordinarily assumed that the input to the function is known, and it is the output that we need to approximate. In this work, we take the opposite approach: we show how to compute the values of some transcendental functions by approximating the input to these functions, and obtaining exact answers for their output. Our approach assumes only the most rudimentary knowledge of algebra and trigonometry, and makes no use of calculus.

  20. Deducing protein function by forensic integrative cell biology.

    PubMed

    Earnshaw, William C

    2013-12-01

    Our ability to sequence genomes has provided us with near-complete lists of the proteins that compose cells, tissues, and organisms, but this is only the beginning of the process to discover the functions of cellular components. In the future, it's going to be crucial to develop computational analyses that can predict the biological functions of uncharacterised proteins. At the same time, we must not forget those fundamental experimental skills needed to confirm the predictions or send the analysts back to the drawing board to devise new ones.

  1. 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

  2. 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

  3. Stochastic Effects in Computational Biology of Space Radiation Cancer Risk

    NASA Technical Reports Server (NTRS)

    Cucinotta, Francis A.; Pluth, Janis; Harper, Jane; O'Neill, Peter

    2007-01-01

    Estimating risk from space radiation poses important questions on the radiobiology of protons and heavy ions. We are considering systems biology models to study radiation induced repair foci (RIRF) at low doses, in which less than one-track on average transverses the cell, and the subsequent DNA damage processing and signal transduction events. Computational approaches for describing protein regulatory networks coupled to DNA and oxidative damage sites include systems of differential equations, stochastic equations, and Monte-Carlo simulations. We review recent developments in the mathematical description of protein regulatory networks and possible approaches to radiation effects simulation. These include robustness, which states that regulatory networks maintain their functions against external and internal perturbations due to compensating properties of redundancy and molecular feedback controls, and modularity, which leads to general theorems for considering molecules that interact through a regulatory mechanism without exchange of matter leading to a block diagonal reduction of the connecting pathways. Identifying rate-limiting steps, robustness, and modularity in pathways perturbed by radiation damage are shown to be valid techniques for reducing large molecular systems to realistic computer simulations. Other techniques studied are the use of steady-state analysis, and the introduction of composite molecules or rate-constants to represent small collections of reactants. Applications of these techniques to describe spatial and temporal distributions of RIRF and cell populations following low dose irradiation are described.

  4. Signal-to-Noise Ratio Measures Efficacy of Biological Computing Devices and Circuits

    PubMed Central

    Beal, Jacob

    2015-01-01

    Engineering biological cells to perform computations has a broad range of important potential applications, including precision medical therapies, biosynthesis process control, and environmental sensing. Implementing predictable and effective computation, however, has been extremely difficult to date, due to a combination of poor composability of available parts and of insufficient characterization of parts and their interactions with the complex environment in which they operate. In this paper, the author argues that this situation can be improved by quantitative signal-to-noise analysis of the relationship between computational abstractions and the variation and uncertainty endemic in biological organisms. This analysis takes the form of a ΔSNRdB function for each computational device, which can be computed from measurements of a device’s input/output curve and expression noise. These functions can then be combined to predict how well a circuit will implement an intended computation, as well as evaluating the general suitability of biological devices for engineering computational circuits. Applying signal-to-noise analysis to current repressor libraries shows that no library is currently sufficient for general circuit engineering, but also indicates key targets to remedy this situation and vastly improve the range of computations that can be used effectively in the implementation of biological applications. PMID:26177070

  5. Signal-to-Noise Ratio Measures Efficacy of Biological Computing Devices and Circuits.

    PubMed

    Beal, Jacob

    2015-01-01

    Engineering biological cells to perform computations has a broad range of important potential applications, including precision medical therapies, biosynthesis process control, and environmental sensing. Implementing predictable and effective computation, however, has been extremely difficult to date, due to a combination of poor composability of available parts and of insufficient characterization of parts and their interactions with the complex environment in which they operate. In this paper, the author argues that this situation can be improved by quantitative signal-to-noise analysis of the relationship between computational abstractions and the variation and uncertainty endemic in biological organisms. This analysis takes the form of a ΔSNRdB function for each computational device, which can be computed from measurements of a device's input/output curve and expression noise. These functions can then be combined to predict how well a circuit will implement an intended computation, as well as evaluating the general suitability of biological devices for engineering computational circuits. Applying signal-to-noise analysis to current repressor libraries shows that no library is currently sufficient for general circuit engineering, but also indicates key targets to remedy this situation and vastly improve the range of computations that can be used effectively in the implementation of biological applications. PMID:26177070

  6. Computational Modeling of Mitochondrial Function

    PubMed Central

    Cortassa, Sonia; Aon, Miguel A.

    2012-01-01

    The advent of techniques with the ability to scan massive changes in cellular makeup (genomics, proteomics, etc.) has revealed the compelling need for analytical methods to interpret and make sense of those changes. Computational models built on sound physico-chemical mechanistic basis are unavoidable at the time of integrating, interpreting, and simulating high-throughput experimental data. Another powerful role of computational models is predicting new behavior provided they are adequately validated. Mitochondrial energy transduction has been traditionally studied with thermodynamic models. More recently, kinetic or thermo-kinetic models have been proposed, leading the path toward an understanding of the control and regulation of mitochondrial energy metabolism and its interaction with cytoplasmic and other compartments. In this work, we outline the methods, step-by-step, that should be followed to build a computational model of mitochondrial energetics in isolation or integrated to a network of cellular processes. Depending on the question addressed by the modeler, the methodology explained herein can be applied with different levels of detail, from the mitochondrial energy producing machinery in a network of cellular processes to the dynamics of a single enzyme during its catalytic cycle. PMID:22057575

  7. Aegerolysins: Structure, function, and putative biological role

    PubMed Central

    Berne, Sabina; Lah, Ljerka; Sepčić, Kristina

    2009-01-01

    Aegerolysins, discovered in fungi, bacteria and plants, are highly similar proteins with interesting biological properties. Certain aegerolysins possess antitumoral, antiproliferative, and antibacterial activities. Further possible medicinal applications include their use in the prevention of atherosclerosis, or as vaccines. Additional biotechnological value of fungal aegerolysins lies in their involvement in development, which could improve cultivation of commercially important edible mushrooms. Besides, new insights on microheterogeneity of raft-like membrane domains could be gained by using aegerolysins as specific markers in cell and molecular biology. Although the exact function of aegerolysins in their producing organisms remains to be explained, they are biochemically well characterized all-β structured proteins sharing the following common features: low isoelectric points, similar molecular weights (15–17 kDa), and stability in a wide pH range. PMID:19309687

  8. Marine carotenoids: biological functions and commercial applications.

    PubMed

    Vílchez, Carlos; Forján, Eduardo; Cuaresma, María; Bédmar, Francisco; Garbayo, Inés; Vega, José M

    2011-03-03

    Carotenoids are the most common pigments in nature and are synthesized by all photosynthetic organisms and fungi. Carotenoids are considered key molecules for life. Light capture, photosynthesis photoprotection, excess light dissipation and quenching of singlet oxygen are among key biological functions of carotenoids relevant for life on earth. Biological properties of carotenoids allow for a wide range of commercial applications. Indeed, recent interest in the carotenoids has been mainly for their nutraceutical properties. A large number of scientific studies have confirmed the benefits of carotenoids to health and their use for this purpose is growing rapidly. In addition, carotenoids have traditionally been used in food and animal feed for their color properties. Carotenoids are also known to improve consumer perception of quality; an example is the addition of carotenoids to fish feed to impart color to farmed salmon.

  9. Towards molecular computers that operate in a biological environment

    NASA Astrophysics Data System (ADS)

    Kahan, Maya; Gil, Binyamin; Adar, Rivka; Shapiro, Ehud

    2008-07-01

    important consequences when performed in a proper context. We envision that molecular computers that operate in a biological environment can be the basis of “smart drugs”, which are potent drugs that activate only if certain environmental conditions hold. These conditions could include abnormalities in the molecular composition of the biological environment that are indicative of a particular disease. Here we review the research direction that set this vision and attempts to realize it.

  10. Catalyzing Inquiry at the Interface of Computing and Biology

    SciTech Connect

    John Wooley; Herbert S. Lin

    2005-10-30

    This study is the first comprehensive NRC study that suggests a high-level intellectual structure for Federal agencies for supporting work at the biology/computing interface. The report seeks to establish the intellectual legitimacy of a fundamentally cross-disciplinary collaboration between biologists and computer scientists. That is, while some universities are increasingly favorable to research at the intersection, life science researchers at other universities are strongly impeded in their efforts to collaborate. This report addresses these impediments and describes proven strategies for overcoming them. An important feature of the report is the use of well-documented examples that describe clearly to individuals not trained in computer science the value and usage of computing across the biological sciences, from genes and proteins to networks and pathways, from organelles to cells, and from individual organisms to populations and ecosystems. It is hoped that these examples will be useful to students in the life sciences to motivate (continued) study in computer science that will enable them to be more facile users of computing in their future biological studies.

  11. 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.

  12. Filling the gap between biology and computer science.

    PubMed

    Aguilar-Ruiz, Jesús S; Moore, Jason H; Ritchie, Marylyn D

    2008-07-17

    This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community.

  13. Filling the gap between biology and computer science

    PubMed Central

    Aguilar-Ruiz, Jesús S; Moore, Jason H; Ritchie, Marylyn D

    2008-01-01

    This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community. PMID:18822148

  14. Biology Students Building Computer Simulations Using StarLogo TNG

    ERIC Educational Resources Information Center

    Smith, V. Anne; Duncan, Ishbel

    2011-01-01

    Confidence is an important issue for biology students in handling computational concepts. This paper describes a practical in which honours-level bioscience students simulate complex animal behaviour using StarLogo TNG, a freely-available graphical programming environment. The practical consists of two sessions, the first of which guides students…

  15. Integrating Computer Interfaced Videodisc Systems in Introductory College Biology.

    ERIC Educational Resources Information Center

    Ebert-Zawasky, Kathleen; Abegg, Gerald L.

    This study was designed as a systematic investigation of the feasibility and effectiveness of student authored videodisc presentations in a non-major introductory level college biology course. Students (n=66) used a quick-learn authoring system, the Macintosh computer, and videodisc player with color monitor. Results included: (1) students managed…

  16. On computation of Hough functions

    NASA Astrophysics Data System (ADS)

    Wang, Houjun; Boyd, John P.; Akmaev, Rashid A.

    2016-04-01

    Hough functions are the eigenfunctions of the Laplace tidal equation governing fluid motion on a rotating sphere with a resting basic state. Several numerical methods have been used in the past. In this paper, we compare two of those methods: normalized associated Legendre polynomial expansion and Chebyshev collocation. Both methods are not widely used, but both have some advantages over the commonly used unnormalized associated Legendre polynomial expansion method. Comparable results are obtained using both methods. For the first method we note some details on numerical implementation. The Chebyshev collocation method was first used for the Laplace tidal problem by Boyd (1976) and is relatively easy to use. A compact MATLAB code is provided for this method. We also illustrate the importance and effect of including a parity factor in Chebyshev polynomial expansions for modes with odd zonal wave numbers.

  17. Genomic Functionalization: The Next Revolution In Biology

    SciTech Connect

    Anderson, Peter; Schoeniger, Joseph S.; Imbro, Paula M.

    2014-07-01

    We have implemented a ligand-alignment algorithm into our developed computational pipeline for identifying specificity-determining features (SDFs) in protein-ligand complexes. Given a set of protein-ligand complex structures, the algorithm aligns the complexes by ligand rather than by the C -RMSD or standard approach, providing a single reference frame for extracting SDFs. We anticipate that this ligand-alignment capability will be highly useful for protein function prediction. We already have a database containing > 20 K ligand-protein complex crystal structures taken from the Protein Data Bank. By aligning these proteins to single reference frames using ligand alignment, we can submit the complexes to our pipeline for SDF extraction. The SDFs derived from this training procedure can be used as thumbprints that are hallmarks of individual enzyme classes. These SDF thumbprints may then serve as guides to the prediction of function of new unknown proteins.

  18. Computational intelligence techniques for biological data mining: An overview

    NASA Astrophysics Data System (ADS)

    Faye, Ibrahima; Iqbal, Muhammad Javed; Said, Abas Md; Samir, Brahim Belhaouari

    2014-10-01

    Computational techniques have been successfully utilized for a highly accurate analysis and modeling of multifaceted and raw biological data gathered from various genome sequencing projects. These techniques are proving much more effective to overcome the limitations of the traditional in-vitro experiments on the constantly increasing sequence data. However, most critical problems that caught the attention of the researchers may include, but not limited to these: accurate structure and function prediction of unknown proteins, protein subcellular localization prediction, finding protein-protein interactions, protein fold recognition, analysis of microarray gene expression data, etc. To solve these problems, various classification and clustering techniques using machine learning have been extensively used in the published literature. These techniques include neural network algorithms, genetic algorithms, fuzzy ARTMAP, K-Means, K-NN, SVM, Rough set classifiers, decision tree and HMM based algorithms. Major difficulties in applying the above algorithms include the limitations found in the previous feature encoding and selection methods while extracting the best features, increasing classification accuracy and decreasing the running time overheads of the learning algorithms. The application of this research would be potentially useful in the drug design and in the diagnosis of some diseases. This paper presents a concise overview of the well-known protein classification techniques.

  19. 10 years for the Journal of Bioinformatics and Computational Biology (2003-2013) -- a retrospective.

    PubMed

    Eisenhaber, Frank; Sherman, Westley Arthur

    2014-06-01

    The Journal of Bioinformatics and Computational Biology (JBCB) started publishing scientific articles in 2003. It has established itself as home for solid research articles in the field (~ 60 per year) that are surprisingly well cited. JBCB has an important function as alternative publishing channel in addition to other, bigger journals.

  20. Supporting Representational Competence in High School Biology with Computer-Based Biomolecular Visualizations

    ERIC Educational Resources Information Center

    Wilder, Anna; Brinkerhoff, Jonathan

    2007-01-01

    This study assessed the effectiveness of computer-based biomolecular visualization activities on the development of high school biology students' representational competence as a means of understanding and visualizing protein structure/function relationships. Also assessed were students' attitudes toward these activities. Sixty-nine students…

  1. Computational design of receptor and sensor proteins with novel functions

    NASA Astrophysics Data System (ADS)

    Looger, Loren L.; Dwyer, Mary A.; Smith, James J.; Hellinga, Homme W.

    2003-05-01

    The formation of complexes between proteins and ligands is fundamental to biological processes at the molecular level. Manipulation of molecular recognition between ligands and proteins is therefore important for basic biological studies and has many biotechnological applications, including the construction of enzymes, biosensors, genetic circuits, signal transduction pathways and chiral separations. The systematic manipulation of binding sites remains a major challenge. Computational design offers enormous generality for engineering protein structure and function. Here we present a structure-based computational method that can drastically redesign protein ligand-binding specificities. This method was used to construct soluble receptors that bind trinitrotoluene, L-lactate or serotonin with high selectivity and affinity. These engineered receptors can function as biosensors for their new ligands; we also incorporated them into synthetic bacterial signal transduction pathways, regulating gene expression in response to extracellular trinitrotoluene or L-lactate. The use of various ligands and proteins shows that a high degree of control over biomolecular recognition has been established computationally. The biological and biosensing activities of the designed receptors illustrate potential applications of computational design.

  2. Computational Proteomics: High-throughput Analysis for Systems Biology

    SciTech Connect

    Cannon, William R.; Webb-Robertson, Bobbie-Jo M.

    2007-01-03

    High-throughput (HTP) proteomics is a rapidly developing field that offers the global profiling of proteins from a biological system. The HTP technological advances are fueling a revolution in biology, enabling analyses at the scales of entire systems (e.g., whole cells, tumors, or environmental communities). However, simply identifying the proteins in a cell is insufficient for understanding the underlying complexity and operating mechanisms of the overall system. Systems level investigations are relying more and more on computational analyses, especially in the field of proteomics generating large-scale global data.

  3. Perspectives on an education in computational biology and medicine.

    PubMed

    Rubinstein, Jill C

    2012-09-01

    The mainstream application of massively parallel, high-throughput assays in biomedical research has created a demand for scientists educated in Computational Biology and Bioinformatics (CBB). In response, formalized graduate programs have rapidly evolved over the past decade. Concurrently, there is increasing need for clinicians trained to oversee the responsible translation of CBB research into clinical tools. Physician-scientists with dedicated CBB training can facilitate such translation, positioning themselves at the intersection between computational biomedical research and medicine. This perspective explores key elements of the educational path to such a position, specifically addressing: 1) evolving perceptions of the role of the computational biologist and the impact on training and career opportunities; 2) challenges in and strategies for obtaining the core skill set required of a biomedical researcher in a computational world; and 3) how the combination of CBB with medical training provides a logical foundation for a career in academic medicine and/or biomedical research.

  4. Perspectives on an Education in Computational Biology and Medicine

    PubMed Central

    Rubinstein, Jill C.

    2012-01-01

    The mainstream application of massively parallel, high-throughput assays in biomedical research has created a demand for scientists educated in Computational Biology and Bioinformatics (CBB). In response, formalized graduate programs have rapidly evolved over the past decade. Concurrently, there is increasing need for clinicians trained to oversee the responsible translation of CBB research into clinical tools. Physician-scientists with dedicated CBB training can facilitate such translation, positioning themselves at the intersection between computational biomedical research and medicine. This perspective explores key elements of the educational path to such a position, specifically addressing: 1) evolving perceptions of the role of the computational biologist and the impact on training and career opportunities; 2) challenges in and strategies for obtaining the core skill set required of a biomedical researcher in a computational world; and 3) how the combination of CBB with medical training provides a logical foundation for a career in academic medicine and/or biomedical research. PMID:23012581

  5. Computational approaches for rational design of proteins with novel functionalities.

    PubMed

    Tiwari, Manish Kumar; Singh, Ranjitha; Singh, Raushan Kumar; Kim, In-Won; Lee, Jung-Kul

    2012-01-01

    Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes.

  6. Computational Neuroscience: Modeling the Systems Biology of Synaptic Plasticity

    PubMed Central

    Kotaleski, Jeanette Hellgren; Blackwell, Kim T.

    2016-01-01

    Preface Synaptic plasticity is a mechanism proposed to underlie learning and memory. The complexity of the interactions between ion channels, enzymes, and genes involved in synaptic plasticity impedes a deep understanding of this phenomenon. Computer modeling is an approach to investigate the information processing that is performed by signaling pathways underlying synaptic plasticity. In the past few years, new software developments that blend computational neuroscience techniques with systems biology techniques have allowed large-scale, quantitative modeling of synaptic plasticity in neurons. We highlight significant advancements produced by these modeling efforts and introduce promising approaches that utilize advancements in live cell imaging. PMID:20300102

  7. Computational approaches in modeling spectra of biological chromophores

    NASA Astrophysics Data System (ADS)

    Nemukhin, Alexander V.; Grigorenko, Bella L.; Bochenkova, Anastasia V.; Bravaya, Ksenia B.; Savitsky, Alexander P.

    2008-02-01

    Computational approaches to describe optical spectra of biological chromophores in proteins, in solutions and in the gas phase are discussed. Recently, accurate measurements of spectral properties for the series of chromophores in different media allowed the authors to estimate the positions of the bands with a high accuracy and to challenge theoreticians by stating that the measured S 0-S I transition wavelengths may be used as new benchmark values for the theory. The novel computational approaches based on the multiconfigurational quasidegenerate perturbation theory present the practical means how to adapt the high level methodology for calculations of accurate excitation energies in large biological chromophores. The theory is illustrated for a series of model compounds for which experimental data are available: the retinal molecule in the protonated Shiff-base form, the chromophores from the Green Fluorescent Protein family including the kindling protein asFP595, and the chromophore from the BLUF domain containing photoreceptor proteins.

  8. 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.

  9. Approximate Bayesian computation with functional statistics.

    PubMed

    Soubeyrand, Samuel; Carpentier, Florence; Guiton, François; Klein, Etienne K

    2013-03-26

    Functional statistics are commonly used to characterize spatial patterns in general and spatial genetic structures in population genetics in particular. Such functional statistics also enable the estimation of parameters of spatially explicit (and genetic) models. Recently, Approximate Bayesian Computation (ABC) has been proposed to estimate model parameters from functional statistics. However, applying ABC with functional statistics may be cumbersome because of the high dimension of the set of statistics and the dependences among them. To tackle this difficulty, we propose an ABC procedure which relies on an optimized weighted distance between observed and simulated functional statistics. We applied this procedure to a simple step model, a spatial point process characterized by its pair correlation function and a pollen dispersal model characterized by genetic differentiation as a function of distance. These applications showed how the optimized weighted distance improved estimation accuracy. In the discussion, we consider the application of the proposed ABC procedure to functional statistics characterizing non-spatial processes.

  10. Exploiting graphics processing units for computational biology and bioinformatics.

    PubMed

    Payne, Joshua L; Sinnott-Armstrong, Nicholas A; Moore, Jason H

    2010-09-01

    Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the standard workhorses of scientific computing. With the recent release of generalpurpose GPUs and NVIDIA's GPU programming language, CUDA, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. The goal of this article is to concisely present an introduction to GPU hardware and programming, aimed at the computational biologist or bioinformaticist. To this end, we discuss the primary differences between GPU and CPU architecture, introduce the basics of the CUDA programming language, and discuss important CUDA programming practices, such as the proper use of coalesced reads, data types, and memory hierarchies. We highlight each of these topics in the context of computing the all-pairs distance between instances in a dataset, a common procedure in numerous disciplines of scientific computing. We conclude with a runtime analysis of the GPU and CPU implementations of the all-pairs distance calculation. We show our final GPU implementation to outperform the CPU implementation by a factor of 1700.

  11. Bioconductor: open software development for computational biology and bioinformatics

    PubMed Central

    Gentleman, Robert C; Carey, Vincent J; Bates, Douglas M; Bolstad, Ben; Dettling, Marcel; Dudoit, Sandrine; Ellis, Byron; Gautier, Laurent; Ge, Yongchao; Gentry, Jeff; Hornik, Kurt; Hothorn, Torsten; Huber, Wolfgang; Iacus, Stefano; Irizarry, Rafael; Leisch, Friedrich; Li, Cheng; Maechler, Martin; Rossini, Anthony J; Sawitzki, Gunther; Smith, Colin; Smyth, Gordon; Tierney, Luke; Yang, Jean YH; Zhang, Jianhua

    2004-01-01

    The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples. PMID:15461798

  12. Topological properties of robust biological and computational networks

    PubMed Central

    Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

    2014-01-01

    Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

  13. Computational design of proteins with novel structure and functions

    NASA Astrophysics Data System (ADS)

    Wei, Yang; Lu-Hua, Lai

    2016-01-01

    Computational design of proteins is a relatively new field, where scientists search the enormous sequence space for sequences that can fold into desired structure and perform desired functions. With the computational approach, proteins can be designed, for example, as regulators of biological processes, novel enzymes, or as biotherapeutics. These approaches not only provide valuable information for understanding of sequence-structure-function relations in proteins, but also hold promise for applications to protein engineering and biomedical research. In this review, we briefly introduce the rationale for computational protein design, then summarize the recent progress in this field, including de novo protein design, enzyme design, and design of protein-protein interactions. Challenges and future prospects of this field are also discussed. Project supported by the National Basic Research Program of China (Grant No. 2015CB910300), the National High Technology Research and Development Program of China (Grant No. 2012AA020308), and the National Natural Science Foundation of China (Grant No. 11021463).

  14. Computational immuno-biology for organ transplantation and regenerative medicine.

    PubMed

    Vásquez-Montoya, Gustavo A; Danobeitia, Juan S; Fernández, Luis A; Hernández-Ortiz, Juan P

    2016-10-01

    Organ transplantation and regenerative medicine are adopted platforms that provide replacement tissues and organs from natural or engineered sources. Acceptance, tolerance and rejection depend greatly on the proper control of the immune response against graft antigens, motivating the development of immunological and genetical therapies that prevent organ failure. They rely on a complete, or partial, understanding of the immune system. Ultimately, they are innovative technologies that ensure permanent graft tolerance and indefinite graft survival through the modulation of the immune system. Computational immunology has arisen as a tool towards a mechanistic understanding of the biological and physicochemical processes surrounding an immune response. It comprehends theoretical and computational frameworks that simulate immuno-biological systems. The challenge is centered on the multi-scale character of the immune system that spans from atomistic scales, during peptide-epitope and protein interactions, to macroscopic scales, for lymph transport and organ-organ reactions. In this paper, we discuss, from an engineering perspective, the biological processes that are involved during the immune response of organ transplantation. Previous computational efforts, including their characteristics and visible limitations, are described. Finally, future perspectives and challenges are listed to motivate further developments.

  15. Dynamics and computation in functional shifts

    NASA Astrophysics Data System (ADS)

    Namikawa, Jun; Hashimoto, Takashi

    2004-07-01

    We introduce a new type of shift dynamics as an extended model of symbolic dynamics, and investigate the characteristics of shift spaces from the viewpoints of both dynamics and computation. This shift dynamics is called a functional shift, which is defined by a set of bi-infinite sequences of some functions on a set of symbols. To analyse the complexity of functional shifts, we measure them in terms of topological entropy, and locate their languages in the Chomsky hierarchy. Through this study, we argue that considering functional shifts from the viewpoints of both dynamics and computation gives us opposite results about the complexity of systems. We also describe a new class of shift spaces whose languages are not recursively enumerable.

  16. Mathematical and Computational Challenges in Population Biology and Ecosystems Science

    NASA Technical Reports Server (NTRS)

    Levin, Simon A.; Grenfell, Bryan; Hastings, Alan; Perelson, Alan S.

    1997-01-01

    Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhanced potential of high-speed computation, have opened up new vistas and presented new challenges. Key challenges involve ways to deal with the collective dynamics of heterogeneous ensembles of individuals, and to scale from small spatial regions to large ones. The central issues-understanding how detail at one scale makes its signature felt at other scales, and how to relate phenomena across scales-cut across scientific disciplines and go to the heart of algorithmic development of approaches to high-speed computation. Examples are given from ecology, genetics, epidemiology, and immunology.

  17. Computer Games Functioning as Motivation Stimulants

    ERIC Educational Resources Information Center

    Lin, Grace Hui Chin; Tsai, Tony Kung Wan; Chien, Paul Shih Chieh

    2011-01-01

    Numerous scholars have recommended computer games can function as influential motivation stimulants of English learning, showing benefits as learning tools (Clarke and Dede, 2007; Dede, 2009; Klopfer and Squire, 2009; Liu and Chu, 2010; Mitchell, Dede & Dunleavy, 2009). This study aimed to further test and verify the above suggestion,…

  18. Computationally efficient method to construct scar functions

    NASA Astrophysics Data System (ADS)

    Revuelta, F.; Vergini, E. G.; Benito, R. M.; Borondo, F.

    2012-02-01

    The performance of a simple method [E. L. Sibert III, E. Vergini, R. M. Benito, and F. Borondo, New J. Phys.NJOPFM1367-263010.1088/1367-2630/10/5/053016 10, 053016 (2008)] to efficiently compute scar functions along unstable periodic orbits with complicated trajectories in configuration space is discussed, using a classically chaotic two-dimensional quartic oscillator as an illustration.

  19. The Structure and Function of Biological Networks

    ERIC Educational Resources Information Center

    Wu, Daniel Duanqing

    2010-01-01

    Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding…

  20. Computational Fluid Dynamics Framework for Turbine Biological Performance Assessment

    SciTech Connect

    Richmond, Marshall C.; Serkowski, John A.; Carlson, Thomas J.; Ebner, Laurie L.; Sick, Mirjam; Cada, G. F.

    2011-05-04

    In this paper, a method for turbine biological performance assessment is introduced to bridge the gap between field and laboratory studies on fish injury and turbine design. Using this method, a suite of biological performance indicators is computed based on simulated data from a computational fluid dynamics (CFD) model of a proposed turbine design. Each performance indicator is a measure of the probability of exposure to a certain dose of an injury mechanism. If the relationship between the dose of an injury mechanism and frequency of injury (dose-response) is known from laboratory or field studies, the likelihood of fish injury for a turbine design can be computed from the performance indicator. By comparing the values of the indicators from various turbine designs, the engineer can identify the more-promising designs. Discussion here is focused on Kaplan-type turbines, although the method could be extended to other designs. Following the description of the general methodology, we will present sample risk assessment calculations based on CFD data from a model of the John Day Dam on the Columbia River in the USA.

  1. 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

  2. Computational biology in the cloud: methods and new insights from computing at scale.

    PubMed

    Kasson, Peter M

    2013-01-01

    The past few years have seen both explosions in the size of biological data sets and the proliferation of new, highly flexible on-demand computing capabilities. The sheer amount of information available from genomic and metagenomic sequencing, high-throughput proteomics, experimental and simulation datasets on molecular structure and dynamics affords an opportunity for greatly expanded insight, but it creates new challenges of scale for computation, storage, and interpretation of petascale data. Cloud computing resources have the potential to help solve these problems by offering a utility model of computing and storage: near-unlimited capacity, the ability to burst usage, and cheap and flexible payment models. Effective use of cloud computing on large biological datasets requires dealing with non-trivial problems of scale and robustness, since performance-limiting factors can change substantially when a dataset grows by a factor of 10,000 or more. New computing paradigms are thus often needed. The use of cloud platforms also creates new opportunities to share data, reduce duplication, and to provide easy reproducibility by making the datasets and computational methods easily available.

  3. Representing and analysing molecular and cellular function using the computer.

    PubMed

    van Helden, J; Naim, A; Mancuso, R; Eldridge, M; Wernisch, L; Gilbert, D; Wodak, S J

    2000-01-01

    Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model.

  4. Advances and computational tools towards predictable design in biological engineering.

    PubMed

    Pasotti, Lorenzo; Zucca, Susanna

    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.

  5. Twenty years of protein interaction studies for biological function deciphering.

    PubMed

    Legrain, Pierre; Rain, Jean-Christophe

    2014-07-31

    Intensive methodological developments and technology innovation have been devoted to protein-protein interaction studies over 20years. Genetic indirect assays and sophisticated large scale biochemical analyses have jointly contributed to the elucidation of protein-protein interactions, still with a lot of drawbacks despite heavy investment in human resources and technologies. With the most recent developments in mass spectrometry and computational tools for studying protein content of complex samples, the initial goal of deciphering molecular bases of biological functions is now within reach. Here, we described the various steps of this process and gave examples of key milestones in this scientific story line. This article is part of a Special Issue entitled: 20years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

  6. Progressive inquiry in a computer-supported biology class

    NASA Astrophysics Data System (ADS)

    Hakkarainen, Kai

    2003-12-01

    The problem addressed in the study was whether 10- and 11-year-old children, collaborating within a computer-supported classroom, could engage in progressive inquiry that exhibits an essential principal feature of mature scientific inquiry: namely, engagement in increasingly deep levels of explanation. Technical infrastructure for the study was provided by the Computer-Supported Intentional Learning Environment (CSILE). The study was carried out by qualitatively analyzing written notes logged by 28 Grade 5/6 students to CSILE's database. Results of the study indicated that with teacher guidance, students were able to produce meaningful intuitive explanations about biological phenomena, guide this process by pursuing their own research questions, and engage in constructive peer interaction that helped them go beyond their intuitive explanations and toward theoretical scientific explanations. Expert evaluations by three widely recognized philosophers of science confirmed the progressive nature of students' inquiry.

  7. Computations and algorithms in physical and biological problems

    NASA Astrophysics Data System (ADS)

    Qin, Yu

    This dissertation presents the applications of state-of-the-art computation techniques and data analysis algorithms in three physical and biological problems: assembling DNA pieces, optimizing self-assembly yield, and identifying correlations from large multivariate datasets. In the first topic, in-depth analysis of using Sequencing by Hybridization (SBH) to reconstruct target DNA sequences shows that a modified reconstruction algorithm can overcome the theoretical boundary without the need for different types of biochemical assays and is robust to error. In the second topic, consistent with theoretical predictions, simulations using Graphics Processing Unit (GPU) demonstrate how controlling the short-ranged interactions between particles and controlling the concentrations optimize the self-assembly yield of a desired structure, and nonequilibrium behavior when optimizing concentrations is also unveiled by leveraging the computation capacity of GPUs. In the last topic, a methodology to incorporate existing categorization information into the search process to efficiently reconstruct the optimal true correlation matrix for multivariate datasets is introduced. Simulations on both synthetic and real financial datasets show that the algorithm is able to detect signals below the Random Matrix Theory (RMT) threshold. These three problems are representatives of using massive computation techniques and data analysis algorithms to tackle optimization problems, and outperform theoretical boundary when incorporating prior information into the computation.

  8. Functional quantum computing: An optical approach

    NASA Astrophysics Data System (ADS)

    Rambo, Timothy M.; Altepeter, Joseph B.; Kumar, Prem; D'Ariano, G. Mauro

    2016-05-01

    Recent theoretical investigations treat quantum computations as functions, quantum processes which operate on other quantum processes, rather than circuits. Much attention has been given to the N -switch function which takes N black-box quantum operators as input, coherently permutes their ordering, and applies the result to a target quantum state. This is something which cannot be equivalently done using a quantum circuit. Here, we propose an all-optical system design which implements coherent operator permutation for an arbitrary number of input operators.

  9. Analysis of Ventricular Function by Computed Tomography

    PubMed Central

    Rizvi, Asim; Deaño, Roderick C.; Bachman, Daniel P.; Xiong, Guanglei; Min, James K.; Truong, Quynh A.

    2014-01-01

    The assessment of ventricular function, cardiac chamber dimensions and ventricular mass is fundamental for clinical diagnosis, risk assessment, therapeutic decisions, and prognosis in patients with cardiac disease. Although cardiac computed tomography (CT) is a noninvasive imaging technique often used for the assessment of coronary artery disease, it can also be utilized to obtain important data about left and right ventricular function and morphology. In this review, we will discuss the clinical indications for the use of cardiac CT for ventricular analysis, review the evidence on the assessment of ventricular function compared to existing imaging modalities such cardiac MRI and echocardiography, provide a typical cardiac CT protocol for image acquisition and post-processing for ventricular analysis, and provide step-by-step instructions to acquire multiplanar cardiac views for ventricular assessment from the standard axial, coronal, and sagittal planes. Furthermore, both qualitative and quantitative assessments of ventricular function as well as sample reporting are detailed. PMID:25576407

  10. New Computer Simulations of Macular Neural Functioning

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

    1994-01-01

    We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

  11. A method of biological pathway similarity search using high performance computing.

    PubMed

    Jiang, Keyuan; Huang, Yingmeng; Robertson, Joseph

    2009-01-01

    Comparative study of biological pathway structures and composition can aid us in elucidating the functions of newly discovered pathways, understanding evolutionary traits, and determining missing pathway elements. A method has been developed to perform pair-wise comparison and similarity search of biological pathways. The comparison determines the differences of each pair of pathways represented in the XML format. The similarity search uses a scoring mechanism to rank the similarities of the pathway in question against those in the pathway repository. To achieve a reasonably good performance, the method is being implemented using the Condor high performance computing environment. PMID:19963871

  12. Computer network defense through radial wave functions

    NASA Astrophysics Data System (ADS)

    Malloy, Ian J.

    The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.

  13. Biology Teacher and Expert Opinions about Computer Assisted Biology Instruction Materials: A Software Entitled Nucleic Acids and Protein Synthesis

    ERIC Educational Resources Information Center

    Hasenekoglu, Ismet; Timucin, Melih

    2007-01-01

    The aim of this study is to collect and evaluate opinions of CAI experts and biology teachers about a high school level Computer Assisted Biology Instruction Material presenting computer-made modelling and simulations. It is a case study. A material covering "Nucleic Acids and Protein Synthesis" topic was developed as the "case". The goal of the…

  14. Understanding regulatory networks requires more than computing a multitude of graph statistics. Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O.C. Martin et al.

    NASA Astrophysics Data System (ADS)

    Tkačik, Gašper

    2016-07-01

    The article by O. Martin and colleagues provides a much needed systematic review of a body of work that relates the topological structure of genetic regulatory networks to evolutionary selection for function. This connection is very important. Using the current wealth of genomic data, statistical features of regulatory networks (e.g., degree distributions, motif composition, etc.) can be quantified rather easily; it is, however, often unclear how to interpret the results. On a graph theoretic level the statistical significance of the results can be evaluated by comparing observed graphs to "randomized" ones (bravely ignoring the issue of how precisely to randomize!) and comparing the frequency of appearance of a particular network structure relative to a randomized null expectation. While this is a convenient operational test for statistical significance, its biological meaning is questionable. In contrast, an in-silico genotype-to-phenotype model makes explicit the assumptions about the network function, and thus clearly defines the expected network structures that can be compared to the case of no selection for function and, ultimately, to data.

  15. Fibroblast Growth Factors: Biology, Function, and Application for Tissue Regeneration

    PubMed Central

    Yun, Ye-Rang; Won, Jong Eun; Jeon, Eunyi; Lee, Sujin; Kang, Wonmo; Jo, Hyejin; Jang, Jun-Hyeog; Shin, Ueon Sang; Kim, Hae-Won

    2010-01-01

    Fibroblast growth factors (FGFs) that signal through FGF receptors (FGFRs) regulate a broad spectrum of biological functions, including cellular proliferation, survival, migration, and differentiation. The FGF signal pathways are the RAS/MAP kinase pathway, PI3 kinase/AKT pathway, and PLCγ pathway, among which the RAS/MAP kinase pathway is known to be predominant. Several studies have recently implicated the in vitro biological functions of FGFs for tissue regeneration. However, to obtain optimal outcomes in vivo, it is important to enhance the half-life of FGFs and their biological stability. Future applications of FGFs are expected when the biological functions of FGFs are potentiated through the appropriate use of delivery systems and scaffolds. This review will introduce the biology and cellular functions of FGFs and deal with the biomaterials based delivery systems and their current applications for the regeneration of tissues, including skin, blood vessel, muscle, adipose, tendon/ligament, cartilage, bone, tooth, and nerve tissues. PMID:21350642

  16. Functionalized apertures for the detection of chemical and biological materials

    DOEpatents

    Letant, Sonia E.; van Buuren, Anthony W.; Terminello, Louis J.; Thelen, Michael P.; Hope-Weeks, Louisa J.; Hart, Bradley R.

    2010-12-14

    Disclosed are nanometer to micron scale functionalized apertures constructed on a substrate made of glass, carbon, semiconductors or polymeric materials that allow for the real time detection of biological materials or chemical moieties. Many apertures can exist on one substrate allowing for the simultaneous detection of numerous chemical and biological molecules. One embodiment features a macrocyclic ring attached to cross-linkers, wherein the macrocyclic ring has a biological or chemical probe extending through the aperture. Another embodiment achieves functionalization by attaching chemical or biological anchors directly to the walls of the apertures via cross-linkers.

  17. Algorithms in nature: the convergence of systems biology and computational thinking.

    PubMed

    Navlakha, Saket; Bar-Joseph, Ziv

    2011-11-08

    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.

  18. Computational functions in biochemical reaction networks.

    PubMed Central

    Arkin, A; Ross, J

    1994-01-01

    In prior work we demonstrated the implementation of logic gates, sequential computers (universal Turing machines), and parallel computers by means of the kinetics of chemical reaction mechanisms. In the present article we develop this subject further by first investigating the computational properties of several enzymatic (single and multiple) reaction mechanisms: we show their steady states are analogous to either Boolean or fuzzy logic gates. Nearly perfect digital function is obtained only in the regime in which the enzymes are saturated with their substrates. With these enzymatic gates, we construct combinational chemical networks that execute a given truth-table. The dynamic range of a network's output is strongly affected by "input/output matching" conditions among the internal gate elements. We find a simple mechanism, similar to the interconversion of fructose-6-phosphate between its two bisphosphate forms (fructose-1,6-bisphosphate and fructose-2,6-bisphosphate), that functions analogously to an AND gate. When the simple model is supplanted with one in which the enzyme rate laws are derived from experimental data, the steady state of the mechanism functions as an asymmetric fuzzy aggregation operator with properties akin to a fuzzy AND gate. The qualitative behavior of the mechanism does not change when situated within a large model of glycolysis/gluconeogenesis and the TCA cycle. The mechanism, in this case, switches the pathway's mode from glycolysis to gluconeogenesis in response to chemical signals of low blood glucose (cAMP) and abundant fuel for the TCA cycle (acetyl coenzyme A). Images FIGURE 3 FIGURE 4 FIGURE 5 FIGURE 7 FIGURE 10 FIGURE 12 FIGURE 13 FIGURE 14 FIGURE 15 FIGURE 16 PMID:7948674

  19. Biological insertion of computationally designed short transmembrane segments.

    PubMed

    Baeza-Delgado, Carlos; von Heijne, Gunnar; Marti-Renom, Marc A; Mingarro, Ismael

    2016-01-01

    The great majority of helical membrane proteins are inserted co-translationally into the ER membrane through a continuous ribosome-translocon channel. The efficiency of membrane insertion depends on transmembrane (TM) helix amino acid composition, the helix length and the position of the amino acids within the helix. In this work, we conducted a computational analysis of the composition and location of amino acids in transmembrane helices found in membrane proteins of known structure to obtain an extensive set of designed polypeptide segments with naturally occurring amino acid distributions. Then, using an in vitro translation system in the presence of biological membranes, we experimentally validated our predictions by analyzing its membrane integration capacity. Coupled with known strategies to control membrane protein topology, these findings may pave the way to de novo membrane protein design. PMID:26987712

  20. Biological insertion of computationally designed short transmembrane segments

    PubMed Central

    Baeza-Delgado, Carlos; von Heijne, Gunnar; Marti-Renom, Marc A.; Mingarro, Ismael

    2016-01-01

    The great majority of helical membrane proteins are inserted co-translationally into the ER membrane through a continuous ribosome-translocon channel. The efficiency of membrane insertion depends on transmembrane (TM) helix amino acid composition, the helix length and the position of the amino acids within the helix. In this work, we conducted a computational analysis of the composition and location of amino acids in transmembrane helices found in membrane proteins of known structure to obtain an extensive set of designed polypeptide segments with naturally occurring amino acid distributions. Then, using an in vitro translation system in the presence of biological membranes, we experimentally validated our predictions by analyzing its membrane integration capacity. Coupled with known strategies to control membrane protein topology, these findings may pave the way to de novo membrane protein design. PMID:26987712

  1. Computational modeling structure and spectra of biological chromophores

    NASA Astrophysics Data System (ADS)

    Collins, Jack R.; Topol, Igor A.; Nemukhin, Alexander V.; Savitsky, Alexander P.

    2009-02-01

    Modern computational approaches based on quantum mechanical methods to characterize structures and optical spectra of biological chromophores in the gas phase, in solutions and proteins are discussed. Primary attention is paid to the chromophores from the family of the green fluorescent protein (GFP) widely used as a biomarker in living cells. Beyond GFP, photophysical properties of the monomeric teal fluorescent protein (mTFPI) and the kindling fluorescent protein asFP595 are simulated. We apply modern quantum chemical approaches for high level calculations of the structures of the chromophore binding pockets and to estimate spectral bands corresponding to the S0-S1 optical transitions. A special attention is paid to evaluate effects of point mutations in the vicinity of the chromophore group. Theoretical data provide important information on the chromophore properties aiming to interpret the results of experimental studies of fluorescent proteins.

  2. Tunable ultrasensitivity: functional decoupling and biological insights

    PubMed Central

    Wang, Guanyu; Zhang, Mengshi

    2016-01-01

    Sensitivity has become a basic concept in biology, but much less is known about its tuning, probably because allosteric cooperativity, the best known mechanism of sensitivity, is determined by rigid conformations of interacting molecules and is thus difficult to tune. Reversible covalent modification (RCM), owing to its systems-level ingenuity, can generate concentration based, tunable sensitivity. Using a mathematical model of regulated RCM, we find sensitivity tuning can be decomposed into two orthogonal modes, which provide great insights into vital biological processes such as tissue development and cell cycle progression. We find that decoupling of the two modes of sensitivity tuning is critical to fidelity of cell fate decision; the decoupling is thus important in development. The decomposition also allows us to solve the ‘wasteful degradation conundrum’ in budding yeast cell cycle checkpoint, which further leads to discovery of a subtle but essential difference between positive feedback and double negative feedback. The latter guarantees revocability of stress-induced cell cycle arrest; while the former does not. By studying concentration conditions in the system, we extend applicability of ultrasensitivity and explain the ubiquity of reversible covalent modification. PMID:26847155

  3. Tunable ultrasensitivity: functional decoupling and biological insights.

    PubMed

    Wang, Guanyu; Zhang, Mengshi

    2016-01-01

    Sensitivity has become a basic concept in biology, but much less is known about its tuning, probably because allosteric cooperativity, the best known mechanism of sensitivity, is determined by rigid conformations of interacting molecules and is thus difficult to tune. Reversible covalent modification (RCM), owing to its systems-level ingenuity, can generate concentration based, tunable sensitivity. Using a mathematical model of regulated RCM, we find sensitivity tuning can be decomposed into two orthogonal modes, which provide great insights into vital biological processes such as tissue development and cell cycle progression. We find that decoupling of the two modes of sensitivity tuning is critical to fidelity of cell fate decision; the decoupling is thus important in development. The decomposition also allows us to solve the 'wasteful degradation conundrum' in budding yeast cell cycle checkpoint, which further leads to discovery of a subtle but essential difference between positive feedback and double negative feedback. The latter guarantees revocability of stress-induced cell cycle arrest; while the former does not. By studying concentration conditions in the system, we extend applicability of ultrasensitivity and explain the ubiquity of reversible covalent modification. PMID:26847155

  4. Complex network problems in physics, computer science and biology

    NASA Astrophysics Data System (ADS)

    Cojocaru, Radu Ionut

    There is a close relation between physics and mathematics and the exchange of ideas between these two sciences are well established. However until few years ago there was no such a close relation between physics and computer science. Even more, only recently biologists started to use methods and tools from statistical physics in order to study the behavior of complex system. In this thesis we concentrate on applying and analyzing several methods borrowed from computer science to biology and also we use methods from statistical physics in solving hard problems from computer science. In recent years physicists have been interested in studying the behavior of complex networks. Physics is an experimental science in which theoretical predictions are compared to experiments. In this definition, the term prediction plays a very important role: although the system is complex, it is still possible to get predictions for its behavior, but these predictions are of a probabilistic nature. Spin glasses, lattice gases or the Potts model are a few examples of complex systems in physics. Spin glasses and many frustrated antiferromagnets map exactly to computer science problems in the NP-hard class defined in Chapter 1. In Chapter 1 we discuss a common result from artificial intelligence (AI) which shows that there are some problems which are NP-complete, with the implication that these problems are difficult to solve. We introduce a few well known hard problems from computer science (Satisfiability, Coloring, Vertex Cover together with Maximum Independent Set and Number Partitioning) and then discuss their mapping to problems from physics. In Chapter 2 we provide a short review of combinatorial optimization algorithms and their applications to ground state problems in disordered systems. We discuss the cavity method initially developed for studying the Sherrington-Kirkpatrick model of spin glasses. We extend this model to the study of a specific case of spin glass on the Bethe

  5. Using computer algebra and SMT solvers in algebraic biology

    NASA Astrophysics Data System (ADS)

    Pineda Osorio, Mateo

    2014-05-01

    Biologic processes are represented as Boolean networks, in a discrete time. The dynamics within these networks are approached with the help of SMT Solvers and the use of computer algebra. Software such as Maple and Z3 was used in this case. The number of stationary states for each network was calculated. The network studied here corresponds to the immune system under the effects of drastic mood changes. Mood is considered as a Boolean variable that affects the entire dynamics of the immune system, changing the Boolean satisfiability and the number of stationary states of the immune network. Results obtained show Z3's great potential as a SMT Solver. Some of these results were verified in Maple, even though it showed not to be as suitable for the problem approach. The solving code was constructed using Z3-Python and Z3-SMT-LiB. Results obtained are important in biology systems and are expected to help in the design of immune therapies. As a future line of research, more complex Boolean network representations of the immune system as well as the whole psychological apparatus are suggested.

  6. Enzymatic Halogenases and Haloperoxidases: Computational Studies on Mechanism and Function.

    PubMed

    Timmins, Amy; de Visser, Sam P

    2015-01-01

    Despite the fact that halogenated compounds are rare in biology, a number of organisms have developed processes to utilize halogens and in recent years, a string of enzymes have been identified that selectively insert halogen atoms into, for instance, a CH aliphatic bond. Thus, a number of natural products, including antibiotics, contain halogenated functional groups. This unusual process has great relevance to the chemical industry for stereoselective and regiospecific synthesis of haloalkanes. Currently, however, industry utilizes few applications of biological haloperoxidases and halogenases, but efforts are being worked on to understand their catalytic mechanism, so that their catalytic function can be upscaled. In this review, we summarize experimental and computational studies on the catalytic mechanism of a range of haloperoxidases and halogenases with structurally very different catalytic features and cofactors. This chapter gives an overview of heme-dependent haloperoxidases, nonheme vanadium-dependent haloperoxidases, and flavin adenine dinucleotide-dependent haloperoxidases. In addition, we discuss the S-adenosyl-l-methionine fluoridase and nonheme iron/α-ketoglutarate-dependent halogenases. In particular, computational efforts have been applied extensively for several of these haloperoxidases and halogenases and have given insight into the essential structural features that enable these enzymes to perform the unusual halogen atom transfer to substrates. PMID:26415843

  7. Enzymatic Halogenases and Haloperoxidases: Computational Studies on Mechanism and Function.

    PubMed

    Timmins, Amy; de Visser, Sam P

    2015-01-01

    Despite the fact that halogenated compounds are rare in biology, a number of organisms have developed processes to utilize halogens and in recent years, a string of enzymes have been identified that selectively insert halogen atoms into, for instance, a CH aliphatic bond. Thus, a number of natural products, including antibiotics, contain halogenated functional groups. This unusual process has great relevance to the chemical industry for stereoselective and regiospecific synthesis of haloalkanes. Currently, however, industry utilizes few applications of biological haloperoxidases and halogenases, but efforts are being worked on to understand their catalytic mechanism, so that their catalytic function can be upscaled. In this review, we summarize experimental and computational studies on the catalytic mechanism of a range of haloperoxidases and halogenases with structurally very different catalytic features and cofactors. This chapter gives an overview of heme-dependent haloperoxidases, nonheme vanadium-dependent haloperoxidases, and flavin adenine dinucleotide-dependent haloperoxidases. In addition, we discuss the S-adenosyl-l-methionine fluoridase and nonheme iron/α-ketoglutarate-dependent halogenases. In particular, computational efforts have been applied extensively for several of these haloperoxidases and halogenases and have given insight into the essential structural features that enable these enzymes to perform the unusual halogen atom transfer to substrates.

  8. Printable Bioelectronics To Investigate Functional Biological Interfaces.

    PubMed

    Manoli, Kyriaki; Magliulo, Maria; Mulla, Mohammad Yusuf; Singh, Mandeep; Sabbatini, Luigia; Palazzo, Gerardo; Torsi, Luisa

    2015-10-19

    Thin-film transistors can be used as high-performance bioelectronic devices to accomplish tasks such as sensing or controlling the release of biological species as well as transducing the electrical activity of cells or even organs, such as the brain. Organic, graphene, or zinc oxide are used as convenient printable semiconducting layers and can lead to high-performance low-cost bioelectronic sensing devices that are potentially very useful for point-of-care applications. Among others, electrolyte-gated transistors are of interest as they can be operated as capacitance-modulated devices, because of the high capacitance of their charge double layers. Specifically, it is the capacitance of the biolayer, being lowest in a series of capacitors, which controls the output current of the device. Such an occurrence allows for extremely high sensitivity towards very weak interactions. All the aspects governing these processes are reviewed here. PMID:26420480

  9. Chemical Biology for Understanding Matrix Metalloproteinase Function

    PubMed Central

    Knapinska, Anna; Fields, Gregg B.

    2013-01-01

    The matrix metalloproteinase (MMP) family has long been associated with normal physiological processes such as embryonic implantation, tissue remodeling, organ development, and wound healing, as well as multiple aspects of cancer initiation and progression, osteoarthritis, inflammatory and vascular diseases, and neurodegenerative diseases. The development of chemically designed MMP probes has advanced our understanding of the roles of MMPs in disease in addition to shedding considerable light on the mechanisms of MMP action. The first generation of protease-activated agents has demonstrated proof of principle as well as providing impetus for in vivo applications. One common problem has been a lack of agent stability at nontargeted tissues and organs due to activation by multiple proteases. The present review considers how chemical biology has impacted the progress made in understanding the roles of MMPs in disease and the basic mechanisms of MMP action. PMID:22933318

  10. Energy and time determine scaling in biological and computer designs.

    PubMed

    Moses, Melanie; Bezerra, George; Edwards, Benjamin; Brown, James; Forrest, Stephanie

    2016-08-19

    Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy-time minimization principle may govern the design of many complex systems that process energy, materials and information.This article is part of the themed issue 'The major synthetic evolutionary transitions'. PMID:27431524

  11. Computational biology: plus c'est la même chose, plus ça change.

    PubMed

    Huttenhower, Curtis

    2011-08-23

    A report on the joint 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB)/10th Annual European Conference on Computational Biology (ECCB) meetings and the 7th International Society for Computational Biology Student Council Symposium, Vienna, Austria, 15-19 July 2011.

  12. Computational approaches for inferring the functions of intrinsically disordered proteins

    PubMed Central

    Varadi, Mihaly; Vranken, Wim; Guharoy, Mainak; Tompa, Peter

    2015-01-01

    Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder. PMID:26301226

  13. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

    PubMed

    Naudé, Jérémie; Cessac, Bruno; Berry, Hugues; Delord, Bruno

    2013-09-18

    Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks.

  14. Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

    PubMed

    Naudé, Jérémie; Cessac, Bruno; Berry, Hugues; Delord, Bruno

    2013-09-18

    Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks. PMID:24048833

  15. COMPUTATIONAL METHODS FOR STUDYING THE INTERACTION BETWEEN POLYCYCLIC AROMATIC HYDROCARBONS AND BIOLOGICAL MACROMOLECULES

    EPA Science Inventory

    Computational Methods for Studying the Interaction between Polycyclic Aromatic Hydrocarbons and Biological Macromolecules .

    The mechanisms for the processes that result in significant biological activity of PAHs depend on the interaction of these molecules or their metabol...

  16. Chaste: using agile programming techniques to develop computational biology software.

    PubMed

    Pitt-Francis, Joe; Bernabeu, Miguel O; Cooper, Jonathan; Garny, Alan; Momtahan, Lee; Osborne, James; Pathmanathan, Pras; Rodriguez, Blanca; Whiteley, Jonathan P; Gavaghan, David J

    2008-09-13

    Cardiac modelling is the area of physiome modelling where the available simulation software is perhaps most mature, and it therefore provides an excellent starting point for considering the software requirements for the wider physiome community. In this paper, we will begin by introducing some of the most advanced existing software packages for simulating cardiac electrical activity. We consider the software development methods used in producing codes of this type, and discuss their use of numerical algorithms, relative computational efficiency, usability, robustness and extensibility. We then go on to describe a class of software development methodologies known as test-driven agile methods and argue that such methods are more suitable for scientific software development than the traditional academic approaches. A case study is a project of our own, Cancer, Heart and Soft Tissue Environment, which is a library of computational biology software that began as an experiment in the use of agile programming methods. We present our experiences with a review of our progress thus far, focusing on the advantages and disadvantages of this new approach compared with the development methods used in some existing packages. We conclude by considering whether the likely wider needs of the cardiac modelling community are currently being met and suggest that, in order to respond effectively to changing requirements, it is essential that these codes should be more malleable. Such codes will allow for reliable extensions to include both detailed mathematical models--of the heart and other organs--and more efficient numerical techniques that are currently being developed by many research groups worldwide. PMID:18565813

  17. Chaste: using agile programming techniques to develop computational biology software.

    PubMed

    Pitt-Francis, Joe; Bernabeu, Miguel O; Cooper, Jonathan; Garny, Alan; Momtahan, Lee; Osborne, James; Pathmanathan, Pras; Rodriguez, Blanca; Whiteley, Jonathan P; Gavaghan, David J

    2008-09-13

    Cardiac modelling is the area of physiome modelling where the available simulation software is perhaps most mature, and it therefore provides an excellent starting point for considering the software requirements for the wider physiome community. In this paper, we will begin by introducing some of the most advanced existing software packages for simulating cardiac electrical activity. We consider the software development methods used in producing codes of this type, and discuss their use of numerical algorithms, relative computational efficiency, usability, robustness and extensibility. We then go on to describe a class of software development methodologies known as test-driven agile methods and argue that such methods are more suitable for scientific software development than the traditional academic approaches. A case study is a project of our own, Cancer, Heart and Soft Tissue Environment, which is a library of computational biology software that began as an experiment in the use of agile programming methods. We present our experiences with a review of our progress thus far, focusing on the advantages and disadvantages of this new approach compared with the development methods used in some existing packages. We conclude by considering whether the likely wider needs of the cardiac modelling community are currently being met and suggest that, in order to respond effectively to changing requirements, it is essential that these codes should be more malleable. Such codes will allow for reliable extensions to include both detailed mathematical models--of the heart and other organs--and more efficient numerical techniques that are currently being developed by many research groups worldwide.

  18. Connecting leptin signaling to biological function

    PubMed Central

    Allison, Margaret B.; Myers, Martin G.

    2014-01-01

    Hypothalamic leptin action promotes negative energy balance and modulates glucose homeostasis, as well as serving as a permissive signal to the neuroendocrine axes that control growth and reproduction. Since the initial discovery of leptin 20 years ago, we have learned a great deal about the molecular mechanisms of leptin action. An important aspect of this has been the dissection of the cellular mechanisms of leptin signaling, and how specific leptin signals influence physiology. Leptin acts via the long form of the leptin receptor, LepRb. LepRb activation and subsequent tyrosine phosphorylation recruits and activates multiple signaling pathways, including STAT transcription factors, SHP2 and ERK signaling, the IRS-protein/PI3Kinase pathway, and SH2B1. Each of these pathways controls specific aspects of leptin action and physiology. Important inhibitory pathways mediated by suppressor of cytokine signaling (SOCS) proteins and protein tyrosine phosphatases (PTPases) also limit physiologic leptin action. This review summarizes the signaling pathways engaged by LepRb and their effects on energy balance, glucose homeostasis, and reproduction. Particular emphasis is given to the multiple mouse models which have been used to elucidate these functions in vivo. PMID:25232147

  19. [Biological experiments in microgravity: equilibrium function].

    PubMed

    Gorgiladze, G I; Shipov, A A; Horn, E

    2012-01-01

    The review deals with the investigations of structural and functional modifications in the equilibrium organ (EO) in invertebrates (coelenterates, shells, crustaceans and insects) and vertebrates (fishes, amphibians, rats, primates) on different ontogenetic stages in the condition of microgravity and during readaptation to the Earth's gravity. Results of the investigations detail the adaptive strategy of terrestrial organism in the environment lacking the gravitational components that leads to the discrepancy of an inner model of the body-environment schema constructed by the central nervous system at 1 g and the novel reality. It is manifested by ataxic behavior and increased graviceptors' afferentation against efferent system inactivation. The new condition is defined as a sensibilization phase ensued by the eluding phase: behavior obeys the innate motion strategy, whereas graviceptors' afferentation decreases due to activation of the efferent system. Readaptation to 1 G takes several to 50 days and proceeds as a sequence of slow in motion behavior, ataxia and vestibular sensitization. Reactivity of the gravitosensory system to microgravity was found to be age-dependent. Gain in the EO inertial mass in microgravity and reduction with return to 1 g indicates gravity relevance to EO genesis. PMID:23402139

  20. Complete RNA inverse folding: computational design of functional hammerhead ribozymes

    PubMed Central

    Dotu, Ivan; Garcia-Martin, Juan Antonio; Slinger, Betty L.; Mechery, Vinodh; Meyer, Michelle M.; Clote, Peter

    2014-01-01

    Nanotechnology and synthetic biology currently constitute one of the most innovative, interdisciplinary fields of research, poised to radically transform society in the 21st century. This paper concerns the synthetic design of ribonucleic acid molecules, using our recent algorithm, RNAiFold, which can determine all RNA sequences whose minimum free energy secondary structure is a user-specified target structure. Using RNAiFold, we design ten cis-cleaving hammerhead ribozymes, all of which are shown to be functional by a cleavage assay. We additionally use RNAiFold to design a functional cis-cleaving hammerhead as a modular unit of a synthetic larger RNA. Analysis of kinetics on this small set of hammerheads suggests that cleavage rate of computationally designed ribozymes may be correlated with positional entropy, ensemble defect, structural flexibility/rigidity and related measures. Artificial ribozymes have been designed in the past either manually or by SELEX (Systematic Evolution of Ligands by Exponential Enrichment); however, this appears to be the first purely computational design and experimental validation of novel functional ribozymes. RNAiFold is available at http://bioinformatics.bc.edu/clotelab/RNAiFold/. PMID:25209235

  1. Reduction of dynamical biochemical reactions networks in computational biology

    PubMed Central

    Radulescu, O.; Gorban, A. N.; Zinovyev, A.; Noel, V.

    2012-01-01

    Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler models, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state (QSS) and quasi-equilibrium approximations (QE), and provide practical recipes for model reduction of linear and non-linear networks. We also discuss the application of model reduction to the problem of parameter identification, via backward pruning machine learning techniques. PMID:22833754

  2. Computational Biology Methods for Characterization of Pluripotent Cells.

    PubMed

    Araúzo-Bravo, Marcos J

    2016-01-01

    Pluripotent cells are a powerful tool for regenerative medicine and drug discovery. Several techniques have been developed to induce pluripotency, or to extract pluripotent cells from different tissues and biological fluids. However, the characterization of pluripotency requires tedious, expensive, time-consuming, and not always reliable wet-lab experiments; thus, an easy, standard quality-control protocol of pluripotency assessment remains to be established. Here to help comes the use of high-throughput techniques, and in particular, the employment of gene expression microarrays, which has become a complementary technique for cellular characterization. Research has shown that the transcriptomics comparison with an Embryonic Stem Cell (ESC) of reference is a good approach to assess the pluripotency. Under the premise that the best protocol is a computer software source code, here I propose and explain line by line a software protocol coded in R-Bioconductor for pluripotency assessment based on the comparison of transcriptomics data of pluripotent cells with an ESC of reference. I provide advice for experimental design, warning about possible pitfalls, and guides for results interpretation.

  3. Can computational biology improve the phylogenetic analysis of insulin?

    PubMed

    Chakraborty, Chiranjib; Roy, Sanjiban S; Hsu, Minna J; Agoramoorthy, Govindasamy

    2012-11-01

    Using computational biology, we have depicted the insulin phylogenetics. We have also analyzed the sequence alignment and sequence logos formation for both the insulin chain A and B for three groups namely, the mammalian group, vertebrates group and fish group. We have also analyzed cladograms of insulin for the mammalian group. In accordance with that path lengths, matrix for distance analysis, matching representation of nodes of the cladogram and dissimilarity between two nodes have been performed for both of the A and B chains of the mammalian group. Our results show that 12 amino acid residues (GlyA1, IleA2, ValA3, TyrA19, CysA20, AsnA21, LeuB6, GlyB8, LeuB11, ValB12, GlyB23 and PheB24) are highly conserved for all groups and among them some (GlyA1, IleA2, ValA3);(TyrA19, CysA20, AsnA21) are continuous. This study shows a rapid method to calculate the amino acid sequences in terms of evolutionary conservation rates as well as molecular phylogenetics. PMID:22265574

  4. Evidence for a Role of Executive Functions in Learning Biology

    ERIC Educational Resources Information Center

    Rhodes, Sinéad M.; Booth, Josephine N.; Campbell, Lorna Elise; Blythe, Richard A.; Wheate, Nial J.; Delibegovic, Mirela

    2014-01-01

    Research examining cognition and science learning has focused on working memory, but evidence implicates a broader set of executive functions. The current study examined executive functions and learning of biology in young adolescents. Fifty-six participants, aged 12-13?years, completed tasks of working memory (Spatial Working Memory), inhibition…

  5. Tip110: Physical properties, primary structure, and biological functions.

    PubMed

    Whitmill, Amanda; Timani, Khalid Amine; Liu, Ying; He, Johnny J

    2016-03-15

    HIV-1 Tat-interacting protein of 110kDa (Tip110), also referred to as squamous cell carcinoma antigen recognized by T cells 3 (Sart3), p110 or p110(nrb), was initially identified as a cDNA clone (KIAA0156) without annotated functions. Over the past twenty years, several functions have been attributed to this protein. The proposed biological functions include roles for Tip110 in pre-mRNA splicing, gene transcription, stem cell biology, and development. Dysregulation of Tip110 is also a contributing factor in the development of cancer and other human diseases. It is clear that our understanding of this protein is rapidly evolving. In this review, we aimed to provide a summary of all the existing literature on this gene/protein and its proposed biological functions. PMID:26896687

  6. Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition.

    PubMed

    Fitch, W Tecumseh

    2014-09-01

    Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a

  7. Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition.

    PubMed

    Fitch, W Tecumseh

    2014-09-01

    Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a

  8. Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition

    NASA Astrophysics Data System (ADS)

    Fitch, W. Tecumseh

    2014-09-01

    Progress in understanding cognition requires a quantitative, theoretical framework, grounded in the other natural sciences and able to bridge between implementational, algorithmic and computational levels of explanation. I review recent results in neuroscience and cognitive biology that, when combined, provide key components of such an improved conceptual framework for contemporary cognitive science. Starting at the neuronal level, I first discuss the contemporary realization that single neurons are powerful tree-shaped computers, which implies a reorientation of computational models of learning and plasticity to a lower, cellular, level. I then turn to predictive systems theory (predictive coding and prediction-based learning) which provides a powerful formal framework for understanding brain function at a more global level. Although most formal models concerning predictive coding are framed in associationist terms, I argue that modern data necessitate a reinterpretation of such models in cognitive terms: as model-based predictive systems. Finally, I review the role of the theory of computation and formal language theory in the recent explosion of comparative biological research attempting to isolate and explore how different species differ in their cognitive capacities. Experiments to date strongly suggest that there is an important difference between humans and most other species, best characterized cognitively as a propensity by our species to infer tree structures from sequential data. Computationally, this capacity entails generative capacities above the regular (finite-state) level; implementationally, it requires some neural equivalent of a push-down stack. I dub this unusual human propensity "dendrophilia", and make a number of concrete suggestions about how such a system may be implemented in the human brain, about how and why it evolved, and what this implies for models of language acquisition. I conclude that, although much remains to be done, a

  9. A versatile computer-controlled biological stimulus sequencer.

    PubMed

    Boyechko, G; Bose, D

    1984-08-01

    A computer-controlled stimulus sequencer has been developed. This device can be controlled by several commonly available, inexpensive 8-bit microcomputers in which the address, data, and control lines are externally accessible. Although the Apple implementation has been described, a similar interface has also been devised for the Radio Shack Color Computer. The hardware relies on the Rockwell Versatile Interface Adapter (VIA) chip (which has two 16-bit timers capable of functioning as frequency dividers, event counters, or one-shots) and a 12-bit digital-to-analog converter (DAC) chip. This hardware combination, along with software written in Basic and machine language (stored in an EPROM for turn-key operation), allows creation of a large number of unique trains that can be chained together in any sequence. This is aided by storing the train characteristics economically in the computer memory and chaining different train parameters in a link-list. Several train parameters can be incremented or decremented manually by a pair of keys. The amplitude of the train can be changed manually or under program control. Once created, the trains can be edited on the run or deleted from the sequence. The device also generates a trigger pulse that can be referenced to any pulse in a train and can be used to pretrigger an oscilloscope. The software has provision for detecting an external signal, and this information can be used to modify train parameters. The interface is interrupt driven and therefore does not require continuous use of the Basic interpreter.(ABSTRACT TRUNCATED AT 250 WORDS)

  10. 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.

  11. Towards a behavioral-matching based compilation of synthetic biology functions.

    PubMed

    Basso-Blandin, Adrien; Delaplace, Franck

    2015-09-01

    The field of synthetic biology is looking forward engineering framework for safely designing reliable de-novo biological functions. In this undertaking, Computer-Aided-Design (CAD) environments should play a central role for facilitating the design. Although, CAD environment is widely used to engineer artificial systems the application in synthetic biology is still in its infancy. In this article we address the problem of the design of a high level language which at the core of CAD environment. More specifically the Gubs (Genomic Unified Behavioural Specification) language is a specification language used to describe the observations of the expected behaviour. The compiler appropriately selects components such that the observation of the synthetic biological function resulting to their assembly complies to the programmed behaviour. PMID:26141968

  12. Towards a behavioral-matching based compilation of synthetic biology functions.

    PubMed

    Basso-Blandin, Adrien; Delaplace, Franck

    2015-09-01

    The field of synthetic biology is looking forward engineering framework for safely designing reliable de-novo biological functions. In this undertaking, Computer-Aided-Design (CAD) environments should play a central role for facilitating the design. Although, CAD environment is widely used to engineer artificial systems the application in synthetic biology is still in its infancy. In this article we address the problem of the design of a high level language which at the core of CAD environment. More specifically the Gubs (Genomic Unified Behavioural Specification) language is a specification language used to describe the observations of the expected behaviour. The compiler appropriately selects components such that the observation of the synthetic biological function resulting to their assembly complies to the programmed behaviour.

  13. Potential biological functions emerging from the different estrogen receptors.

    PubMed

    Carpenter, Karen D; Korach, Kenneth S

    2006-12-01

    Technological advances and new tools have brought about tremendous advances in elucidating the roles of estradiol and the estrogen receptors (ERs) in biological processes, especially within the female reproductive system. Development and analysis of multiple genetic models have provided insight into the particular functions of each of the ERs. This article reviews the insights into ER biology in female reproduction gained from the development and use of new types of experimental models.

  14. 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

  15. 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

  16. 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

  17. Quantum computation in brain microtubules: Decoherence and biological feasibility

    NASA Astrophysics Data System (ADS)

    Hagan, S.; Hameroff, S. R.; Tuszyński, J. A.

    2002-06-01

    The Penrose-Hameroff orchestrated objective reduction (orch. OR) model assigns a cognitive role to quantum computations in microtubules within the neurons of the brain. Despite an apparently ``warm, wet, and noisy'' intracellular milieu, the proposal suggests that microtubules avoid environmental decoherence long enough to reach threshold for ``self-collapse'' (objective reduction) by a quantum gravity mechanism put forth by Penrose. The model has been criticized as regards the issue of environmental decoherence, and a recent report by Tegmark finds that microtubules can maintain quantum coherence for only 10-13 s, far too short to be neurophysiologically relevant. Here, we critically examine the decoherence mechanisms likely to dominate in a biological setting and find that (1) Tegmark's commentary is not aimed at an existing model in the literature but rather at a hybrid that replaces the superposed protein conformations of the orch. OR theory with a soliton in superposition along the microtubule; (2) recalculation after correcting for differences between the model on which Tegmark bases his calculations and the orch. OR model (superposition separation, charge vs dipole, dielectric constant) lengthens the decoherence time to 10-5-10-4 s (3) decoherence times on this order invalidate the assumptions of the derivation and determine the approximation regime considered by Tegmark to be inappropriate to the orch. OR superposition; (4) Tegmark's formulation yields decoherence times that increase with temperature contrary to well-established physical intuitions and the observed behavior of quantum coherent states; (5) incoherent metabolic energy supplied to the collective dynamics ordering water in the vicinity of microtubules at a rate exceeding that of decoherence can counter decoherence effects (in the same way that lasers avoid decoherence at room temperature); (6) microtubules are surrounded by a Debye layer of counterions, which can screen thermal fluctuations

  18. Interactomes to Biological Phase Space: a call to begin thinking at a new level in computational biology.

    SciTech Connect

    Davidson, George S.; Brown, William Michael

    2007-09-01

    Techniques for high throughput determinations of interactomes, together with high resolution protein collocalizations maps within organelles and through membranes will soon create a vast resource. With these data, biological descriptions, akin to the high dimensional phase spaces familiar to physicists, will become possible. These descriptions will capture sufficient information to make possible realistic, system-level models of cells. The descriptions and the computational models they enable will require powerful computing techniques. This report is offered as a call to the computational biology community to begin thinking at this scale and as a challenge to develop the required algorithms and codes to make use of the new data.3

  19. Can Simple Biophysical Principles Yield Complicated Biological Functions?

    NASA Astrophysics Data System (ADS)

    Liphardt, Jan

    2011-03-01

    About once a year, a new regulatory paradigm is discovered in cell biology. As of last count, eukaryotic cells have more than 40 distinct ways of regulating protein concentration and function. Regulatory possibilities include site-specific phosphorylation, epigenetics, alternative splicing, mRNA (re)localization, and modulation of nucleo-cytoplasmic transport. This raises a simple question. Do all the remarkable things cells do, require an intricately choreographed supporting cast of hundreds of molecular machines and associated signaling networks? Alternatively, are there a few simple biophysical principles that can generate apparently very complicated cellular behaviors and functions? I'll discuss two problems, spatial organization of the bacterial chemotaxis system and nucleo-cytoplasmic transport, where the latter might be true. In both cases, the ability to precisely quantify biological organization and function, at the single-molecule level, helped to find signatures of basic biological organizing principles.

  20. Infrared Structural Biology: Detect Functionally Important Structural Motions of Proteins

    NASA Astrophysics Data System (ADS)

    Xie, Aihua

    Proteins are dynamic. Lack of dynamic structures of proteins hampers our understanding of protein functions. Infrared structural biology (IRSB) is an emerging technology. There are several advantages of IRSB for mechanistic studies of proteins: (1) its excellent dynamic range (detecting structural motions from picoseconds to >= seconds); (2) its high structural sensitivity (detect tiny but functionally important structural motions such as proton transfer and changes in hydrogen bonding interaction); (3) its ability to detect different structural motions simultaneously. Successful development of infrared structural biology demands not only new experimental techniques (from infrared technologies to chemical synthesis and cell biology), but also new data processing (how to translate infrared signals into quantitative structural information of proteins). These topics will be discussed as well as examples of how to use IRSB to study structure-function relationship of proteins. This work was supported by NSF DBI1338097 and OCAST HR10-078.

  1. Theoretical and computational models of biological ion channels

    NASA Astrophysics Data System (ADS)

    Roux, Benoit

    2004-03-01

    A theoretical framework for describing ion conduction through biological molecular pores is established and explored. The framework is based on a statistical mechanical formulation of the transmembrane potential (1) and of the equilibrium multi-ion potential of mean forces through selective ion channels (2). On the basis of these developments, it is possible to define computational schemes to address questions about the non-equilibrium flow of ions through ion channels. In the case of narrow channels (gramicidin or KcsA), it is possible to characterize the ion conduction in terms of the potential of mean force of the ions along the channel axis (i.e., integrating out the off-axis motions). This has been used for gramicidin (3) and for KcsA (4,5). In the case of wide pores (i.e., OmpF porin), this is no longer a good idea, but it is possible to use a continuum solvent approximations. In this case, a grand canonical monte carlo brownian dynamics algorithm was constructed for simulating the non-equilibrium flow of ions through wide pores. The results were compared with those from the Poisson-Nernst-Planck mean-field electrodiffusion theory (6-8). References; 1. B. Roux, Biophys. J. 73:2980-2989 (1997); 2. B. Roux, Biophys. J. 77, 139-153 (1999); 3. Allen, Andersen and Roux, PNAS (2004, in press); 4. Berneche and Roux. Nature, 414:73-77 (2001); 5. Berneche and Roux. PNAS, 100:8644-8648 (2003); 6. W. Im and S. Seefeld and B. Roux, Biophys. J. 79:788-801 (2000); 7. W. Im and B. Roux, J. Chem. Phys. 115:4850-4861 (2001); 8. W. Im and B. Roux, J. Mol. Biol. 322:851-869 (2002).

  2. BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature

    PubMed Central

    Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S.; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

    2011-01-01

    With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php. PMID:21558175

  3. BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

    PubMed

    Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

    2011-07-01

    With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php.

  4. Highlights from the Third International Society for Computational Biology (ISCB) European Student Council Symposium 2014.

    PubMed

    Francescatto, Margherita; Hermans, Susanne M A; Babaei, Sepideh; Vicedo, Esmeralda; Borrel, Alexandre; Meysman, Pieter

    2015-01-01

    In this meeting report, we give an overview of the talks, presentations and posters presented at the third European Symposium of the International Society for Computational Biology (ISCB) Student Council. The event was organized as a satellite meeting of the 13th European Conference for Computational Biology (ECCB) and took place in Strasbourg, France on September 6th, 2014.

  5. On the possible biological relevance of HSNO isomers: a computational investigation.

    PubMed

    Ivanova, Lena V; Anton, Becka J; Timerghazin, Qadir K

    2014-05-14

    Thionitrous acid (HSNO), the smallest S-nitrosothiol, has been identified as a potential biologically active molecule that connects the biochemistries of two important gasotransmitters, nitric oxide (NO) and hydrogen sulfide (H2S). Here, we computationally explore possible isomerization reactions of HSNO that may occur under physiological conditions using high-level coupled-cluster as well as density functional theory and composite CBS-QB3 methodology calculations. Gas-phase calculations show that the formation of the tautomeric form HONS and the Y-isomer SN(H)O is thermodynamically feasible, as they are energetically close, within ∼6 kcal mol(-1), to HSNO, while the recently proposed three-membered ring isomer is not thermodynamically or kinetically accessible. The gas-phase intramolecular proton-transfer reactions required for HSNO isomerization into HONS and SN(H)O are predicted to have prohibitively high reaction barriers, 30-50 kcal mol(-1). However, the polar aqueous environment and water-assisted proton shuttle should decrease these barriers to ∼9 kcal mol(-1), which makes these two isomers kinetically accessible under physiological conditions. Our calculations also support the possibility of an aqueous reaction between the Y-isomer SN(H)O and H2S leading to biologically active nitroxyl HNO. These results suggest that the formation of HSNO in biological milieu can lead to various derivative species with their own, possibly biologically relevant, activity.

  6. Bioconductor: an open source framework for bioinformatics and computational biology.

    PubMed

    Reimers, Mark; Carey, Vincent J

    2006-01-01

    This chapter describes the Bioconductor project and details of its open source facilities for analysis of microarray and other high-throughput biological experiments. Particular attention is paid to concepts of container and workflow design, connections of biological metadata to statistical analysis products, support for statistical quality assessment, and calibration of inference uncertainty measures when tens of thousands of simultaneous statistical tests are performed.

  7. Exploratory Analysis of Biological Networks through Visualization, Clustering, and Functional Annotation in Cytoscape.

    PubMed

    Baryshnikova, Anastasia

    2016-01-01

    Biological networks define how genes, proteins, and other cellular components interact with one another to carry out specific functions, providing a scaffold for understanding cellular organization. Although in-depth network analysis requires advanced mathematical and computational knowledge, a preliminary visual exploration of biological networks is accessible to anyone with basic computer skills. Visualization of biological networks is used primarily to examine network topology, identify functional modules, and predict gene functions based on gene connectivity within the network. Networks are excellent at providing a bird's-eye view of data sets and have the power of illustrating complex ideas in simple and intuitive terms. In addition, they enable exploratory analysis and generation of new hypotheses, which can then be tested using rigorous statistical and experimental tools. This protocol describes a simple procedure for visualizing a biological network using the genetic interaction similarity network for Saccharomyces cerevisiae as an example. The visualization procedure described here relies on the open-source network visualization software Cytoscape and includes detailed instructions on formatting and loading the data, clustering networks, and overlaying functional annotations. PMID:26988373

  8. Experimental and Computational Characterization of Biological Liquid Crystals: A Review of Single-Molecule Bioassays

    PubMed Central

    Eom, Kilho; Yang, Jaemoon; Park, Jinsung; Yoon, Gwonchan; Soo Sohn, Young; Park, Shinsuk; Yoon, Dae Sung; Na, Sungsoo; Kwon, Taeyun

    2009-01-01

    Quantitative understanding of the mechanical behavior of biological liquid crystals such as proteins is essential for gaining insight into their biological functions, since some proteins perform notable mechanical functions. Recently, single-molecule experiments have allowed not only the quantitative characterization of the mechanical behavior of proteins such as protein unfolding mechanics, but also the exploration of the free energy landscape for protein folding. In this work, we have reviewed the current state-of-art in single-molecule bioassays that enable quantitative studies on protein unfolding mechanics and/or various molecular interactions. Specifically, single-molecule pulling experiments based on atomic force microscopy (AFM) have been overviewed. In addition, the computational simulations on single-molecule pulling experiments have been reviewed. We have also reviewed the AFM cantilever-based bioassay that provides insight into various molecular interactions. Our review highlights the AFM-based single-molecule bioassay for quantitative characterization of biological liquid crystals such as proteins. PMID:19865530

  9. Computer-Based Screening of Functional Conformers of Proteins

    PubMed Central

    Montiel Molina, Héctor Marlosti; Millán-Pacheco, César; Pastor, Nina; del Rio, Gabriel

    2008-01-01

    A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest. PMID:18463705

  10. Multilevel functional genomics data integration as a tool for understanding physiology: a network biology perspective.

    PubMed

    Davidsen, Peter K; Turan, Nil; Egginton, Stuart; Falciani, Francesco

    2016-02-01

    The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. PMID:26542523

  11. Optimizing high performance computing workflow for protein functional annotation.

    PubMed

    Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

    2014-09-10

    Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data. PMID:25313296

  12. Optimizing high performance computing workflow for protein functional annotation.

    PubMed

    Stanberry, Larissa; Rekepalli, Bhanu; Liu, Yuan; Giblock, Paul; Higdon, Roger; Montague, Elizabeth; Broomall, William; Kolker, Natali; Kolker, Eugene

    2014-09-10

    Functional annotation of newly sequenced genomes is one of the major challenges in modern biology. With modern sequencing technologies, the protein sequence universe is rapidly expanding. Newly sequenced bacterial genomes alone contain over 7.5 million proteins. The rate of data generation has far surpassed that of protein annotation. The volume of protein data makes manual curation infeasible, whereas a high compute cost limits the utility of existing automated approaches. In this work, we present an improved and optmized automated workflow to enable large-scale protein annotation. The workflow uses high performance computing architectures and a low complexity classification algorithm to assign proteins into existing clusters of orthologous groups of proteins. On the basis of the Position-Specific Iterative Basic Local Alignment Search Tool the algorithm ensures at least 80% specificity and sensitivity of the resulting classifications. The workflow utilizes highly scalable parallel applications for classification and sequence alignment. Using Extreme Science and Engineering Discovery Environment supercomputers, the workflow processed 1,200,000 newly sequenced bacterial proteins. With the rapid expansion of the protein sequence universe, the proposed workflow will enable scientists to annotate big genome data.

  13. Computational approaches to stochastic systems in physics and biology

    NASA Astrophysics Data System (ADS)

    Jeraldo Maldonado, Patricio Rodrigo

    calculation of the corresponding scaling laws. In Part II, I investigate the evolutionary dynamics of communities of microbes living in the gastrointestinal tracts of vertebrates, and ask to what degree their evolution is niche-driven, where organisms fitter to their environment become dominant, or if it is neutral, where the organisms evolve stochastically and are otherwise functionally equivalent within their communities. To that end, a series of computational tools were developed to pre-process, curate and reduce the data sets. In Chapter 4, I analyze the raw data for this research, namely short reads of 16S ribosomal RNA, and quantify how much of phylogenetic information is lost by using these short reads instead of full-length reads, and show that for lengths spanning 300 to 400 base pairs, we can recover some meaningful phylogenetic information. In Chapter 5, I introduce a pipeline for pre-processing, alignment and curation of libraries of short reads of rRNA. We show that this pipeline significantly reduces the artifacts usually associated with these sequences, resulting in better clustering of the sequences, and better phylogenetic trees representing their organismal relationships. In Chapter 6 I use the data processed with the above tools to analyze communities of microbes living in gastrointestinal tracts of vertebrates, and we ask to what extent the evolutionary dynamics of these communities is dominated by niche-based evolution, or if the communities behave neutrally, where evolution is random and all organisms are functionally equivalent. We conclude that there is evidence for strong niche-based dynamics, though we cannot fully discard some degree of neutral evolution. Finally, in Chapter 7 I propose a method to quantify the balance present in phylogenetic trees to compare a large-scale molecular phylogeny to full organismal taxonomies. I show that there is considerable structure in all of them, but direct comparison of both types of trees is difficult at the

  14. Chemical master equation closure for computer-aided synthetic biology.

    PubMed

    Smadbeck, Patrick; Kaznessis, Yiannis N

    2015-01-01

    With inexpensive DNA synthesis technologies, we can now construct biological systems by quickly piecing together DNA sequences. Synthetic biology is the promising discipline that focuses on the construction of these new biological systems. Synthetic biology is an engineering discipline, and as such, it can benefit from mathematical modeling. This chapter focuses on mathematical models of biological systems. These models take the form of chemical reaction networks. The importance of stochasticity is discussed and methods to simulate stochastic reaction networks are reviewed. A closure scheme solution is also presented for the master equation of chemical reaction networks. The master equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks for over 70 years. With the first complete solution of chemical master equations, a wide range of experimental observations of biomolecular interactions may be mathematically conceptualized. We anticipate that models based on the closure scheme described herein may assist in rationally designing synthetic biological systems.

  15. Chemical Master Equation Closure for Computer-Aided Synthetic Biology

    PubMed Central

    Smadbeck, Patrick; Kaznessis, Yiannis N.

    2016-01-01

    SUMMARY With inexpensive DNA synthesis technologies, we can now construct biological systems by quickly piecing together DNA sequences. Synthetic biology is the promising discipline that focuses on the construction of these new biological systems. Synthetic biology is an engineering discipline, and as such, it can benefit from mathematical modeling. This chapter focuses on mathematical models of biological systems. These models take the form of chemical reaction networks. The importance of stochasticity is discussed and methods to simulate stochastic reaction networks are reviewed. A closure scheme solution is also presented for the master equation of chemical reaction networks. The master equation is a complete model of randomly evolving molecular populations. Because of its ambitious character, the master equation remained unsolved for all but the simplest of molecular interaction networks for over seventy years. With the first complete solution of chemical master equations, a wide range of experimental observations of biomolecular interactions may be mathematically conceptualized. We anticipate that models based on the closure scheme described herein may assist in rationally designing synthetic biological systems. PMID:25487098

  16. Venom Proteins from Parasitoid Wasps and Their Biological Functions

    PubMed Central

    Moreau, Sébastien J. M.; Asgari, Sassan

    2015-01-01

    Parasitoid wasps are valuable biological control agents that suppress their host populations. Factors introduced by the female wasp at parasitization play significant roles in facilitating successful development of the parasitoid larva either inside (endoparasitoid) or outside (ectoparasitoid) the host. Wasp venoms consist of a complex cocktail of proteinacious and non-proteinacious components that may offer agrichemicals as well as pharmaceutical components to improve pest management or health related disorders. Undesirably, the constituents of only a small number of wasp venoms are known. In this article, we review the latest research on venom from parasitoid wasps with an emphasis on their biological function, applications and new approaches used in venom studies. PMID:26131769

  17. Venom Proteins from Parasitoid Wasps and Their Biological Functions.

    PubMed

    Moreau, Sébastien J M; Asgari, Sassan

    2015-06-26

    Parasitoid wasps are valuable biological control agents that suppress their host populations. Factors introduced by the female wasp at parasitization play significant roles in facilitating successful development of the parasitoid larva either inside (endoparasitoid) or outside (ectoparasitoid) the host. Wasp venoms consist of a complex cocktail of proteinacious and non-proteinacious components that may offer agrichemicals as well as pharmaceutical components to improve pest management or health related disorders. Undesirably, the constituents of only a small number of wasp venoms are known. In this article, we review the latest research on venom from parasitoid wasps with an emphasis on their biological function, applications and new approaches used in venom studies.

  18. 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

  19. A structural biology perspective on NMDA receptor pharmacology and function.

    PubMed

    Regan, Michael C; Romero-Hernandez, Annabel; Furukawa, Hiro

    2015-08-01

    N-methyld-aspartate receptors (NMDARs) belong to the large family of ionotropic glutamate receptors (iGluRs), which are critically involved in basic brain functions as well as multiple neurological diseases and disorders. The NMDARs are large heterotetrameric membrane protein complexes. The extensive extracellular domains recognize neurotransmitter ligands and allosteric compounds and translate the binding information to regulate activity of the transmembrane ion channel. Here, we review recent advances in the structural biology of NMDARs with a focus on pharmacology and function. Structural analysis of the isolated extracellular domains in combination with the intact heterotetrameric NMDAR structure provides important insights into how this sophisticated ligand-gated ion channel may function.

  20. Accomplishment Summary 1968-1969. Biological Computer Laboratory.

    ERIC Educational Resources Information Center

    Von Foerster, Heinz; And Others

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

  1. Computer method for identification of boiler transfer functions

    NASA Technical Reports Server (NTRS)

    Miles, J. H.

    1972-01-01

    Iterative computer aided procedure was developed which provides for identification of boiler transfer functions using frequency response data. Method uses frequency response data to obtain satisfactory transfer function for both high and low vapor exit quality data.

  2. Functional Agents to Biologically Control Deoxynivalenol Contamination in Cereal Grains

    PubMed Central

    Tian, Ye; Tan, Yanglan; Liu, Na; Liao, Yucai; Sun, Changpo; Wang, Shuangxia; Wu, Aibo

    2016-01-01

    Mycotoxins, as microbial secondary metabolites, frequently contaminate cereal grains and pose a serious threat to human and animal health around the globe. Deoxynivalenol (DON), a commonly detected Fusarium mycotoxin, has drawn utmost attention due to high exposure levels and contamination frequency in the food chain. Biological control is emerging as a promising technology for the management of DON contamination. Functional biological control agents (BCAs), which include antagonistic microbes, natural fungicides derived from plants and detoxification enzymes, can be used to control DON contamination at different stages of grain production. In this review, studies regarding different biological agents for DON control in recent years are summarized for the first time. Furthermore, this article highlights the significance of BCAs for controlling DON contamination, as well as the need for more practical and efficient BCAs concerning food safety. PMID:27064760

  3. From Structure and Function of Proteins Toward in Silico Biology

    NASA Astrophysics Data System (ADS)

    Yamato, Ichiro

    2013-01-01

    Researches of biology are targeted on three major flows, materials (or chemicals), energy, and information. I have been mainly concerned with the studies on bioenergy transducing mechanisms. I have studied the mechanism of secondary active transport systems and proposed an affinity change mechanism as a general hypothesis, then tried to confirm that it is applicable to other kinds of bioenergy transducing systems. Choosing Na+-translocating V-type ATPase from Enterococcus hirae as target, I hypothesized the affinity change mechanism for the energy transduction of this ATPase. Here I describe several three dimensional structures of parts of the ATPase supporting my hypothesis. From such detailed and extensive researches on protein structure/function relationship, we can proceed toward the in silico biology, which I described previously in 2007 ([1] "Toward in silico biology").

  4. Morpho-chemistry and functionality of diseased biological tissues

    NASA Astrophysics Data System (ADS)

    Lange, Marta; Cicchi, Riccardo; Pavone, Francesco

    2014-09-01

    Heart and cardiovascular diseases are one of the most common in the world, in particular - arthrosclerosis. The aim of the research is to distinguish pathological and healthy tissue regions in biological samples, in this case - to distinguish collagen and lipid rich regions within the arterial wall. In the work a specific combination of such methods are used: FLIM and SHG in order to evaluate the biological tissue morphology and functionality, so that this research could give a contribution for creating a new biological tissue imaging standard in the closest future. During the study the most appropriate parameter for fluorescence lifetime decay was chosen in order to evaluate lifetime decay parameters and the isotropy of the arterial wall and deposition, using statistical methods FFT and GLCM. The research gives a contribution or the future investigations for evaluating lipid properties when it can de-attach from the arterial wall and cause clotting in the blood vessel or even a stroke.

  5. Biological Functionalization of Carbon Nanotubes Using Cell Surface Mucin Mimics

    NASA Astrophysics Data System (ADS)

    Chen, Xing; Lee, Goo Soo; Zettl, Alex; Bertozzi, Carolyn

    2004-03-01

    Carbon Nanotubes (CNTs) are molecular wires with remarkable structural, electrical, and mechanical properties. Their potential applications in biology include sensing, imaging, and scaffolding for cell growth, but are presently limited by chemical incompatibility of the CNT surface with biological components and their aqueous milieu. Here we describe a biomimetic surface modification of CNTs using glycosylated polymers designed to mimic natural cell surface mucins. The polymers were end-functionalized with lipid tails for self-assembly on the CNT surface through hydrophobic interactions. Mucin mimic-coated CNTs were soluble in water, resisted non-specific protein binding and bound specifically to biomolecules via receptor-ligand interactions. This strategy for biomimetic surface engineering provides a means to bridge nanomaterials and biological systems.

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

    PubMed Central

    2011-01-01

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

  7. Computational biology for target discovery and characterization: a feasibility study in protein-protein interaction detection

    SciTech Connect

    Zhou, C; Zemla, A

    2009-02-25

    In this work we developed new code for detecting putative multi-domain protein-protein interactions for a small network of bacterial pathogen proteins, and determined how structure-driven domain-fusion (DF) methods should be scaled up for whole-proteome analysis. Protein-protein interactions are of great interest in structural biology and are important for understanding the biology of pathogens. The ability to predict protein-protein interactions provides a means for development of anti-microbials that may interfer with key processes in pathogenicity. The function of a protein-protein complex can be elucidated through knowledge of its structure. The overall goal of this project was to determine the feasibility of extending current LLNL capabilities to produce a high-throughput systems bio-informatics capability for identification and characterization of putative interacting protein partners within known or suspected small protein networks. We extended an existing LLNL methodology for identification of putative protein-protein interacting partners (Chakicherla et al (in review)) by writing a new code to identify multi-domain-fusion linkages (3 or more per complex). We applied these codes to the proteins in the Yersinia pestis quorum sensing network, known as the lsr operon, which comprises a virulence mechanism in this pathogen. We determined that efficient application of our computational algorithms in high-throughput for detection of putative protein-protein complexes genome wide would require pre-computation of PDB domains and construction of a domain-domain association database.

  8. Constructing biological pathway models with hybrid functional Petri nets.

    PubMed

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

    2004-01-01

    In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism. PMID:15724280

  9. Topological network alignment uncovers biological function and phylogeny

    PubMed Central

    Kuchaiev, Oleksii; Milenković, Tijana; Memišević, Vesna; Hayes, Wayne; Pržulj, Nataša

    2010-01-01

    Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology and disease. Comparison and alignment of biological networks will probably have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein–protein interaction networks of two very different species—yeast and human—indicate that even distant species share a surprising amount of network topology, suggesting broad similarities in internal cellular wiring across all life on Earth. PMID:20236959

  10. Computational local stiffness analysis of biological cell: High aspect ratio single wall carbon nanotube tip.

    PubMed

    TermehYousefi, Amin; Bagheri, Samira; Shahnazar, Sheida; Rahman, Md Habibur; Kadri, Nahrizul Adib

    2016-02-01

    Carbon nanotubes (CNTs) are potentially ideal tips for atomic force microscopy (AFM) due to the robust mechanical properties, nanoscale diameter and also their ability to be functionalized by chemical and biological components at the tip ends. This contribution develops the idea of using CNTs as an AFM tip in computational analysis of the biological cells. The proposed software was ABAQUS 6.13 CAE/CEL provided by Dassault Systems, which is a powerful finite element (FE) tool to perform the numerical analysis and visualize the interactions between proposed tip and membrane of the cell. Finite element analysis employed for each section and displacement of the nodes located in the contact area was monitored by using an output database (ODB). Mooney-Rivlin hyperelastic model of the cell allows the simulation to obtain a new method for estimating the stiffness and spring constant of the cell. Stress and strain curve indicates the yield stress point which defines as a vertical stress and plan stress. Spring constant of the cell and the local stiffness was measured as well as the applied force of CNT-AFM tip on the contact area of the cell. This reliable integration of CNT-AFM tip process provides a new class of high performance nanoprobes for single biological cell analysis. PMID:26652417

  11. [Biological Function of The Small G Protein Rap].

    PubMed

    Li, Shan-Shan; Guo, Xiao-Xi; An, Shu; Yang, Yang; Liu, Ying; Xu, Tian-Rui

    2016-02-01

    Rap has different biological functions on intracellular signaling pathways, such as regulating cell polarity, cell proliferation, cell differentiation, cell adhesion and cell movement. Furthermore, at tissue and organ level, Rap controls the establishment of neural polarity, synaptic growth, synaptic plasticity, neuronal migration and so on. Rap belongs to Ras family which contains two subtypes, Rap1 and Rap2. By binding GTP or GDP Rap transform between active or inactive state, and plays an important role as a molecular switch. Moreover, in the signal pathway of tumor, Rap inhibits cell transformation induced by the oncogene Ras, therefore inhibits the proliferation, invasion and migration of certain cancer cells by interacting with its downstream target molecules. In this review, we summarized the biological functions of Rap and discussed It's significance in cancer therapy and drug treatment of neurological diseases.

  12. [Biological Function of The Small G Protein Rap].

    PubMed

    Li, Shan-Shan; Guo, Xiao-Xi; An, Shu; Yang, Yang; Liu, Ying; Xu, Tian-Rui

    2016-02-01

    Rap has different biological functions on intracellular signaling pathways, such as regulating cell polarity, cell proliferation, cell differentiation, cell adhesion and cell movement. Furthermore, at tissue and organ level, Rap controls the establishment of neural polarity, synaptic growth, synaptic plasticity, neuronal migration and so on. Rap belongs to Ras family which contains two subtypes, Rap1 and Rap2. By binding GTP or GDP Rap transform between active or inactive state, and plays an important role as a molecular switch. Moreover, in the signal pathway of tumor, Rap inhibits cell transformation induced by the oncogene Ras, therefore inhibits the proliferation, invasion and migration of certain cancer cells by interacting with its downstream target molecules. In this review, we summarized the biological functions of Rap and discussed It's significance in cancer therapy and drug treatment of neurological diseases. PMID:27424400

  13. The Human Genome Project: Biology, Computers, and Privacy.

    ERIC Educational Resources Information Center

    Cutter, Mary Ann G.; Drexler, Edward; Gottesman, Kay S.; Goulding, Philip G.; McCullough, Laurence B.; McInerney, Joseph D.; Micikas, Lynda B.; Mural, Richard J.; Murray, Jeffrey C.; Zola, John

    This module, for high school teachers, is the second of two modules about the Human Genome Project (HGP) produced by the Biological Sciences Curriculum Study (BSCS). The first section of this module provides background information for teachers about the structure and objectives of the HGP, aspects of the science and technology that underlie the…

  14. Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design.

    PubMed

    Cheng, Gong; Qian, Bin; Samudrala, Ram; Baker, David

    2005-01-01

    The prediction of functional sites in newly solved protein structures is a challenge for computational structural biology. Most methods for approaching this problem use evolutionary conservation as the primary indicator of the location of functional sites. However, sequence conservation reflects not only evolutionary selection at functional sites to maintain protein function, but also selection throughout the protein to maintain the stability of the folded state. To disentangle sequence conservation due to protein functional constraints from sequence conservation due to protein structural constraints, we use all atom computational protein design methodology to predict sequence profiles expected under solely structural constraints, and to compute the free energy difference between the naturally occurring amino acid and the lowest free energy amino acid at each position. We show that functional sites are more likely than non-functional sites to have computed sequence profiles which differ significantly from the naturally occurring sequence profiles and to have residues with sub-optimal free energies, and that incorporation of these two measures improves sequence based prediction of protein functional sites. The combined sequence and structure based functional site prediction method has been implemented in a publicly available web server.

  15. SU-E-T-54: Benefits of Biological Cost Functions

    SciTech Connect

    Demirag, N

    2014-06-01

    Purpose: To verify the benefits of the biological cost functions. Methods: TG166 patients were used for the test case scenarios. Patients were planned using Monaco V5.0 (CMS/Elekta, St.Louis, MO) Monaco has 3 biological and 8 physical CFs. In this study the plans were optimized using 3 different scenarios. 1- Biological CFs only 2-Physical CFs only 3- Combination of Physical and Biological CFsMonaco has 3 biological CFs. Target EUD used for the targets, derived from the poisson cell kill model, has an α value that controls the cold spots inside the target. α values used in the optimization were 0.5 and 0.8. if cold spots needs to be penalized α value increased. Serial CF: it's called serial to mimic the behaviour of the serial organs, if a high k value like 12 or 14 is used it controls the maximum dose. Serial CF has a k parameter that is used to shape the whole dvh curve. K value ranges between 1–20. k:1 is used to control the mean dose, lower k value controls the mean dose, higher k value controls the higher dose, using 2 serial CFs with different k values controls the whole DVH. Paralel CF controls the percentage of the volume that tolerates higher doses than the reference dose to mimic the behaviour of the paralel organs. Results: It was possible to achive clinically accepted plans in all 3 scenarios. The benefit of the biological cost functions were to control the mean dose for target and OAR, to shape the DVH curve using one EUD value and one k value simplifies the optimization process. Using the biological CFs alone, it was hard to control the dose at a point. Conclusion: Biological CFs in Monaco doesn't require the ntcp/tcp values from the labs and useful to shape the whole dvh curve. I work as an applications support specialist for Elekta and I am a Ph.D. Student in Istanbul University for radiation therapy physics.

  16. Pair correlation function integrals: Computation and use

    NASA Astrophysics Data System (ADS)

    Wedberg, Rasmus; O'Connell, John P.; Peters, Günther H.; Abildskov, Jens

    2011-08-01

    We describe a method for extending radial distribution functions obtained from molecular simulations of pure and mixed molecular fluids to arbitrary distances. The method allows total correlation function integrals to be reliably calculated from simulations of relatively small systems. The long-distance behavior of radial distribution functions is determined by requiring that the corresponding direct correlation functions follow certain approximations at long distances. We have briefly described the method and tested its performance in previous communications [R. Wedberg, J. P. O'Connell, G. H. Peters, and J. Abildskov, Mol. Simul. 36, 1243 (2010);, 10.1080/08927020903536366 Fluid Phase Equilib. 302, 32 (2011)], 10.1016/j.fluid.2010.10.004, but describe here its theoretical basis more thoroughly and derive long-distance approximations for the direct correlation functions. We describe the numerical implementation of the method in detail, and report numerical tests complementing previous results. Pure molecular fluids are here studied in the isothermal-isobaric ensemble with isothermal compressibilities evaluated from the total correlation function integrals and compared with values derived from volume fluctuations. For systems where the radial distribution function has structure beyond the sampling limit imposed by the system size, the integration is more reliable, and usually more accurate, than simple integral truncation.

  17. Biological framework for soil aggregation: Implications for ecological functions.

    NASA Astrophysics Data System (ADS)

    Ghezzehei, Teamrat; Or, Dani

    2016-04-01

    Soil aggregation is heuristically understood as agglomeration of primary particles bound together by biotic and abiotic cementing agents. The organization of aggregates is believed to be hierarchical in nature; whereby primary particles bond together to form secondary particles and subsequently merge to form larger aggregates. Soil aggregates are not permanent structures, they continuously change in response to internal and external forces and other drivers, including moisture, capillary pressure, temperature, biological activity, and human disturbances. Soil aggregation processes and the resulting functionality span multiple spatial and temporal scales. The intertwined biological and physical nature of soil aggregation, and the time scales involved precluded a universally applicable and quantifiable framework for characterizing the nature and function of soil aggregation. We introduce a biophysical framework of soil aggregation that considers the various modes and factors of the genesis, maturation and degradation of soil aggregates including wetting/drying cycles, soil mechanical processes, biological activity and the nature of primary soil particles. The framework attempts to disentangle mechanical (compaction and soil fragmentation) from in-situ biophysical aggregation and provides a consistent description of aggregate size, hierarchical organization, and life time. It also enables quantitative description of biotic and abiotic functions of soil aggregates including diffusion and storage of mass and energy as well as role of aggregates as hot spots of nutrient accumulation, biodiversity, and biogeochemical cycles.

  18. Bioclojure: a functional library for the manipulation of biological sequences

    PubMed Central

    Plieskatt, Jordan; Rinaldi, Gabriel; Brindley, Paul J.; Jia, Xinying; Potriquet, Jeremy; Bethony, Jeffrey; Mulvenna, Jason

    2014-01-01

    Motivation: BioClojure is an open-source library for the manipulation of biological sequence data written in the language Clojure. BioClojure aims to provide a functional framework for the processing of biological sequence data that provides simple mechanisms for concurrency and lazy evaluation of large datasets. Results: BioClojure provides parsers and accessors for a range of biological sequence formats, including UniProtXML, Genbank XML, FASTA and FASTQ. In addition, it provides wrappers for key analysis programs, including BLAST, SignalP, TMHMM and InterProScan, and parsers for analyzing their output. All interfaces leverage Clojure’s functional style and emphasize laziness and composability, so that BioClojure, and user-defined, functions can be chained into simple pipelines that are thread-safe and seamlessly integrate lazy evaluation. Availability and implementation: BioClojure is distributed under the Lesser GPL, and the source code is freely available from GitHub (https://github.com/s312569/clj-biosequence). Contact: jason.mulvenna@qimrberghofer.edu.au or jason.mulvenna@qimr.edu.au PMID:24794932

  19. Research advances on structure and biological functions of integrins.

    PubMed

    Pan, Li; Zhao, Yuan; Yuan, Zhijie; Qin, Guixin

    2016-01-01

    Integrins are an important family of adhesion molecules that were first discovered two decades ago. Integrins are transmembrane heterodimeric glycoprotein receptors consisting of α and β subunits, and are comprised of an extracellular domain, a transmembrane domain, and a cytoplasmic tail. Therein, integrin cytoplasmic domains may associate directly with numerous cytoskeletal proteins and intracellular signaling molecules, which are crucial for modulating fundamental cell processes and functions including cell adhesion, proliferation, migration, and survival. The purpose of this review is to describe the unique structure of each integrin subunit, primary cytoplasmic association proteins, and transduction signaling pathway of integrins, with an emphasis on their biological functions. PMID:27468395

  20. The biology, structure, and function of eyebrow hair.

    PubMed

    Nguyen, Jennifer V

    2014-01-01

    Eyebrow hair serves many important biologic and aesthetic functions. This article reviews the structure and function of the hair follicle, as well as hair follicle morphogenesis and cycling. Eyebrow hair follicles share the same basic structure as hair follicles elsewhere on the body, but are distinguished by their shorter anagen (growing) phase. Knowledge of the hair follicle structure and cycle is important for understanding the pathophysiology of alopecia, as diseases affecting the stem cell portion of the hair follicle in the bulge region may cause permanent hair loss. Furthermore, therapeutic agents that target distinct phases and hormones involved in the hair cycle may be useful for promoting hair growth.

  1. The Computer and Its Functions; How to Communicate with the Computer.

    ERIC Educational Resources Information Center

    Ward, Peggy M.

    A brief discussion of why it is important for students to be familiar with computers and their functions and a list of some practical applications introduce this two-part paper. Focusing on how the computer works, the first part explains the various components of the computer, different kinds of memory storage devices, disk operating systems, and…

  2. Functional structure of biological communities predicts ecosystem multifunctionality.

    PubMed

    Mouillot, David; Villéger, Sébastien; Scherer-Lorenzen, Michael; Mason, Norman W H

    2011-01-01

    The accelerating rate of change in biodiversity patterns, mediated by ever increasing human pressures and global warming, demands a better understanding of the relationship between the structure of biological communities and ecosystem functioning (BEF). Recent investigations suggest that the functional structure of communities, i.e. the composition and diversity of functional traits, is the main driver of ecological processes. However, the predictive power of BEF research is still low, the integration of all components of functional community structure as predictors is still lacking, and the multifunctionality of ecosystems (i.e. rates of multiple processes) must be considered. Here, using a multiple-processes framework from grassland biodiversity experiments, we show that functional identity of species and functional divergence among species, rather than species diversity per se, together promote the level of ecosystem multifunctionality with a predictive power of 80%. Our results suggest that primary productivity and decomposition rates, two key ecosystem processes upon which the global carbon cycle depends, are primarily sustained by specialist species, i.e. those that hold specialized combinations of traits and perform particular functions. Contrary to studies focusing on single ecosystem functions and considering species richness as the sole measure of biodiversity, we found a linear and non-saturating effect of the functional structure of communities on ecosystem multifunctionality. Thus, sustaining multiple ecological processes would require focusing on trait dominance and on the degree of community specialization, even in species-rich assemblages.

  3. Virtual Cell: computational tools for modeling in cell biology

    PubMed Central

    Resasco, Diana C.; Gao, Fei; Morgan, Frank; Novak, Igor L.; Schaff, James C.; Slepchenko, Boris M.

    2011-01-01

    The Virtual Cell (VCell) is a general computational framework for modeling physico-chemical and electrophysiological processes in living cells. Developed by the National Resource for Cell Analysis and Modeling at the University of Connecticut Health Center, it provides automated tools for simulating a wide range of cellular phenomena in space and time, both deterministically and stochastically. These computational tools allow one to couple electrophysiology and reaction kinetics with transport mechanisms, such as diffusion and directed transport, and map them onto spatial domains of various shapes, including irregular three-dimensional geometries derived from experimental images. In this article, we review new robust computational tools recently deployed in VCell for treating spatially resolved models. PMID:22139996

  4. Workshop in computational molecular biology, April 15, 1991--April 14, 1994

    SciTech Connect

    Tavare, S.

    1995-04-12

    Funds from this award were used to the Workshop in Computational Molecular Biology, `91 Symposium entitled Interface: Computing Science and Statistics, Seattle, Washington, April 21, 1991; the Workshop in Statistical Issues in Molecular Biology held at Stanford, California, August 8, 1993; and the Session on Population Genetics a part of the 56th Annual Meeting, Institute of Mathematical Statistics, San Francisco, California, August 9, 1993.

  5. Biological and computational evaluation of resveratrol inhibitors against Alzheimer's disease.

    PubMed

    Koukoulitsa, Catherine; Villalonga-Barber, Caroline; Csonka, Robert; Alexi, Xanthippi; Leonis, Georgios; Dellis, Dimitris; Hamelink, Elizabeth; Belda, Oscar; Steele, Barry R; Micha-Screttas, Maria; Alexis, Michael N; Papadopoulos, Manthos G; Mavromoustakos, Thomas

    2016-01-01

    It has been reported that beta amyloid induces production of radical oxygen species and oxidative stress in neuronal cells, which in turn upregulates β-secretase (BACE-1) expression and beta amyloid levels, thereby propagating oxidative stress and increasing neuronal injury. A series of resveratrol derivatives, known to be inhibitors of oxidative stress-induced neuronal cell death (oxytosis) were biologically evaluated against BACE-1 using homogeneous time-resolved fluorescence (TRF) assay. Correlation between oxytosis inhibitory and BACE-1 inhibitory activity of resveratrol derivatives was statistically significant, supporting the notion that BACE-1 may act as pivotal mediator of neuronal cell oxytosis. Four of the biologically evaluated resveratrol analogs demonstrated considerably higher activity than resveratrol in either assay. The discovery of some "hits" led us to initiate detailed docking studies associated with Molecular Dynamics in order to provide a plausible explanation for the experimental results and understand their molecular basis of action.

  6. ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus.

    PubMed

    Karp, Peter D; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

    2015-01-01

    Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida).

  7. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

    PubMed Central

    Huang, Da Wei; Sherman, Brad T; Tan, Qina; Collins, Jack R; Alvord, W Gregory; Roayaei, Jean; Stephens, Robert; Baseler, Michael W; Lane, H Clifford; Lempicki, Richard A

    2007-01-01

    The DAVID Gene Functional Classification Tool uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules. This organization is accomplished by mining the complex biological co-occurrences found in multiple sources of functional annotation. It is a powerful method to group functionally related genes and terms into a manageable number of biological modules for efficient interpretation of gene lists in a network context. PMID:17784955

  8. The Effects of 3D Computer Simulation on Biology Students' Achievement and Memory Retention

    ERIC Educational Resources Information Center

    Elangovan, Tavasuria; Ismail, Zurida

    2014-01-01

    A quasi experimental study was conducted for six weeks to determine the effectiveness of two different 3D computer simulation based teaching methods, that is, realistic simulation and non-realistic simulation on Form Four Biology students' achievement and memory retention in Perak, Malaysia. A sample of 136 Form Four Biology students in Perak,…

  9. From raw data to biological discoveries: a computational analysis pipeline for mass spectrometry-based proteomics.

    PubMed

    Lavallée-Adam, Mathieu; Park, Sung Kyu Robin; Martínez-Bartolomé, Salvador; He, Lin; Yates, John R

    2015-11-01

    In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development. Graphical Abstract ᅟ.

  10. From Raw Data to Biological Discoveries: A Computational Analysis Pipeline for Mass Spectrometry-Based Proteomics

    NASA Astrophysics Data System (ADS)

    Lavallée-Adam, Mathieu; Park, Sung Kyu Robin; Martínez-Bartolomé, Salvador; He, Lin; Yates, John R.

    2015-11-01

    In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development.

  11. An Introduction to Computer Assisted Analysis in the Biological Sciences.

    ERIC Educational Resources Information Center

    Banaugh, R. P.

    This set of notes is designed to introduce the student to the development and use of computer-based models, and to analyze quantitative phenomena in the life sciences. Only BASIC programming language is used. The ten chapter titles are: The Growth of a Single Species; The Association of Two Species; Parameter Determination; Automated Parameter…

  12. Basic mathematical function libraries for scientific computation

    NASA Technical Reports Server (NTRS)

    Galant, David C.

    1989-01-01

    Ada packages implementing selected mathematical functions for the support of scientific and engineering applications were written. The packages provide the Ada programmer with the mathematical function support found in the languages Pascal and FORTRAN as well as an extended precision arithmetic and a complete complex arithmetic. The algorithms used are fully described and analyzed. Implementation assumes that the Ada type FLOAT objects fully conform to the IEEE 754-1985 standard for single binary floating-point arithmetic, and that INTEGER objects are 32-bit entities. Codes for the Ada packages are included as appendixes.

  13. Computing Partial Transposes and Related Entanglement Functions

    NASA Astrophysics Data System (ADS)

    Maziero, Jonas

    2016-10-01

    The partial transpose (PT) is an important function for entanglement testing and quantification and also for the study of geometrical aspects of the quantum state space. In this article, considering general bipartite and multipartite discrete systems, explicit formulas ready for the numerical implementation of the PT and of related entanglement functions are presented and the Fortran code produced for that purpose is described. What is more, we obtain an analytical expression for the Hilbert-Schmidt entanglement of two-qudit systems and for the associated closest separable state. In contrast to previous works on this matter, we only use the properties of the PT, not applying Lagrange multipliers.

  14. Biological properties of extracellular vesicles and their physiological functions.

    PubMed

    Yáñez-Mó, María; Siljander, Pia R-M; Andreu, Zoraida; Zavec, Apolonija Bedina; Borràs, Francesc E; Buzas, Edit I; Buzas, Krisztina; Casal, Enriqueta; Cappello, Francesco; Carvalho, Joana; Colás, Eva; Cordeiro-da Silva, Anabela; Fais, Stefano; Falcon-Perez, Juan M; Ghobrial, Irene M; Giebel, Bernd; Gimona, Mario; Graner, Michael; Gursel, Ihsan; Gursel, Mayda; Heegaard, Niels H H; Hendrix, An; Kierulf, Peter; Kokubun, Katsutoshi; Kosanovic, Maja; Kralj-Iglic, Veronika; Krämer-Albers, Eva-Maria; Laitinen, Saara; Lässer, Cecilia; Lener, Thomas; Ligeti, Erzsébet; Linē, Aija; Lipps, Georg; Llorente, Alicia; Lötvall, Jan; Manček-Keber, Mateja; Marcilla, Antonio; Mittelbrunn, Maria; Nazarenko, Irina; Nolte-'t Hoen, Esther N M; Nyman, Tuula A; O'Driscoll, Lorraine; Olivan, Mireia; Oliveira, Carla; Pállinger, Éva; Del Portillo, Hernando A; Reventós, Jaume; Rigau, Marina; Rohde, Eva; Sammar, Marei; Sánchez-Madrid, Francisco; Santarém, N; Schallmoser, Katharina; Ostenfeld, Marie Stampe; Stoorvogel, Willem; Stukelj, Roman; Van der Grein, Susanne G; Vasconcelos, M Helena; Wauben, Marca H M; De Wever, Olivier

    2015-01-01

    In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system. PMID:25979354

  15. Systems analysis of biological networks in skeletal muscle function

    PubMed Central

    Smith, Lucas R.; Meyer, Gretchen; Lieber, Richard L.

    2014-01-01

    Skeletal muscle function depends on the efficient coordination among subcellular systems. These systems are composed of proteins encoded by a subset of genes, all of which are tightly regulated. In the cases where regulation is altered because of disease or injury, dysfunction occurs. To enable objective analysis of muscle gene expression profiles, we have defined nine biological networks whose coordination is critical to muscle function. We begin by describing the expression of proteins necessary for optimal neuromuscular junction function that results in the muscle cell action potential. That action potential is transmitted to proteins involved in excitation–contraction coupling enabling Ca2+ release. Ca2+ then activates contractile proteins supporting actin and myosin cross-bridge cycling. Force generated by cross-bridges is transmitted via cytoskeletal proteins through the sarcolemma and out to critical proteins that support the muscle extracellular matrix. Muscle contraction is fueled through many proteins that regulate energy metabolism. Inflammation is a common response to injury that can result in alteration of many pathways within muscle. Muscle also has multiple pathways that regulate size through atrophy or hypertrophy. Finally, the isoforms associated with fast muscle fibers and their corresponding isoforms in slow muscle fibers are delineated. These nine networks represent important biological systems that affect skeletal muscle function. Combining high-throughput systems analysis with advanced networking software will allow researchers to use these networks to objectively study skeletal muscle systems. PMID:23188744

  16. Advances in cell surface glycoengineering reveal biological function.

    PubMed

    Nischan, Nicole; Kohler, Jennifer J

    2016-08-01

    Cell surface glycans are critical mediators of cell-cell, cell-ligand, and cell-pathogen interactions. By controlling the set of glycans displayed on the surface of a cell, it is possible to gain insight into the biological functions of glycans. Moreover, control of glycan expression can be used to direct cellular behavior. While genetic approaches to manipulate glycosyltransferase gene expression are available, their utility in glycan engineering has limitations due to the combinatorial nature of glycan biosynthesis and the functional redundancy of glycosyltransferase genes. Biochemical and chemical strategies offer valuable complements to these genetic approaches, notably by enabling introduction of unnatural functionalities, such as fluorophores, into cell surface glycans. Here, we describe some of the most recent developments in glycoengineering of cell surfaces, with an emphasis on strategies that employ novel chemical reagents. We highlight key examples of how these advances in cell surface glycan engineering enable study of cell surface glycans and their function. Exciting new technologies include synthetic lipid-glycans, new chemical reporters for metabolic oligosaccharide engineering to allow tandem and in vivo labeling of glycans, improved chemical and enzymatic methods for glycoproteomics, and metabolic glycosyltransferase inhibitors. Many chemical and biochemical reagents for glycan engineering are commercially available, facilitating their adoption by the biological community.

  17. Biological properties of extracellular vesicles and their physiological functions

    PubMed Central

    Yáñez-Mó, María; Siljander, Pia R.-M.; Andreu, Zoraida; Zavec, Apolonija Bedina; Borràs, Francesc E.; Buzas, Edit I.; Buzas, Krisztina; Casal, Enriqueta; Cappello, Francesco; Carvalho, Joana; Colás, Eva; Silva, Anabela Cordeiro-da; Fais, Stefano; Falcon-Perez, Juan M.; Ghobrial, Irene M.; Giebel, Bernd; Gimona, Mario; Graner, Michael; Gursel, Ihsan; Gursel, Mayda; Heegaard, Niels H. H.; Hendrix, An; Kierulf, Peter; Kokubun, Katsutoshi; Kosanovic, Maja; Kralj-Iglic, Veronika; Krämer-Albers, Eva-Maria; Laitinen, Saara; Lässer, Cecilia; Lener, Thomas; Ligeti, Erzsébet; Linē, Aija; Lipps, Georg; Llorente, Alicia; Lötvall, Jan; Manček-Keber, Mateja; Marcilla, Antonio; Mittelbrunn, Maria; Nazarenko, Irina; Hoen, Esther N.M. Nolte-‘t; Nyman, Tuula A.; O'Driscoll, Lorraine; Olivan, Mireia; Oliveira, Carla; Pállinger, Éva; del Portillo, Hernando A.; Reventós, Jaume; Rigau, Marina; Rohde, Eva; Sammar, Marei; Sánchez-Madrid, Francisco; Santarém, N.; Schallmoser, Katharina; Ostenfeld, Marie Stampe; Stoorvogel, Willem; Stukelj, Roman; Van der Grein, Susanne G.; Vasconcelos, M. Helena; Wauben, Marca H. M.; De Wever, Olivier

    2015-01-01

    In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system. PMID:25979354

  18. Biological properties of extracellular vesicles and their physiological functions.

    PubMed

    Yáñez-Mó, María; Siljander, Pia R-M; Andreu, Zoraida; Zavec, Apolonija Bedina; Borràs, Francesc E; Buzas, Edit I; Buzas, Krisztina; Casal, Enriqueta; Cappello, Francesco; Carvalho, Joana; Colás, Eva; Cordeiro-da Silva, Anabela; Fais, Stefano; Falcon-Perez, Juan M; Ghobrial, Irene M; Giebel, Bernd; Gimona, Mario; Graner, Michael; Gursel, Ihsan; Gursel, Mayda; Heegaard, Niels H H; Hendrix, An; Kierulf, Peter; Kokubun, Katsutoshi; Kosanovic, Maja; Kralj-Iglic, Veronika; Krämer-Albers, Eva-Maria; Laitinen, Saara; Lässer, Cecilia; Lener, Thomas; Ligeti, Erzsébet; Linē, Aija; Lipps, Georg; Llorente, Alicia; Lötvall, Jan; Manček-Keber, Mateja; Marcilla, Antonio; Mittelbrunn, Maria; Nazarenko, Irina; Nolte-'t Hoen, Esther N M; Nyman, Tuula A; O'Driscoll, Lorraine; Olivan, Mireia; Oliveira, Carla; Pállinger, Éva; Del Portillo, Hernando A; Reventós, Jaume; Rigau, Marina; Rohde, Eva; Sammar, Marei; Sánchez-Madrid, Francisco; Santarém, N; Schallmoser, Katharina; Ostenfeld, Marie Stampe; Stoorvogel, Willem; Stukelj, Roman; Van der Grein, Susanne G; Vasconcelos, M Helena; Wauben, Marca H M; De Wever, Olivier

    2015-01-01

    In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.

  19. The Capable ABL: What Is Its Biological Function?

    PubMed Central

    2014-01-01

    The mammalian ABL1 gene encodes the ubiquitously expressed nonreceptor tyrosine kinase ABL. In response to growth factors, cytokines, cell adhesion, DNA damage, oxidative stress, and other signals, ABL is activated to stimulate cell proliferation or differentiation, survival or death, retraction, or migration. ABL also regulates specialized functions such as antigen receptor signaling in lymphocytes, synapse formation in neurons, and bacterial adhesion to intestinal epithelial cells. Although discovered as the proto-oncogene from which the Abelson leukemia virus derived its Gag–v-Abl oncogene, recent results have linked ABL kinase activation to neuronal degeneration. This body of knowledge on ABL seems confusing because it does not fit the one-gene-one-function paradigm. Without question, ABL capabilities are encoded by its gene sequence and that molecular blueprint designs this kinase to be regulated by subcellular location-dependent interactions with inhibitors and substrate activators. Furthermore, ABL shuttles between the nucleus and the cytoplasm where it binds DNA and actin—two biopolymers with fundamental roles in almost all biological processes. Taken together, the cumulated results from analyses of ABL structure-function, ABL mutant mouse phenotypes, and ABL substrates suggest that this tyrosine kinase does not have its own agenda but that, instead, it has evolved to serve a variety of tissue-specific and context-dependent biological functions. PMID:24421390

  20. [Genotoxic modification of nucleic acid bases and biological consequences of it. Review and prospects of experimental and computational investigations

    NASA Technical Reports Server (NTRS)

    Poltev, V. I.; Bruskov, V. I.; Shuliupina, N. V.; Rein, R.; Shibata, M.; Ornstein, R.; Miller, J.

    1993-01-01

    The review is presented of experimental and computational data on the influence of genotoxic modification of bases (deamination, alkylation, oxidation) on the structure and biological functioning of nucleic acids. Pathways are discussed for the influence of modification on coding properties of bases, on possible errors of nucleic acid biosynthesis, and on configurations of nucleotide mispairs. The atomic structure of nucleic acid fragments with modified bases and the role of base damages in mutagenesis and carcinogenesis are considered.

  1. Computer Center: 2 HyperCard Stacks for Biology.

    ERIC Educational Resources Information Center

    Duhrkopf, Richard, Ed.

    1989-01-01

    Two Hypercard stacks are reviewed including "Amino Acids," created to help students associate amino acid names with their structures, and "DNA Teacher," a tutorial on the structure and function of DNA. Availability, functions, hardware requirements, and general comments on these stacks are provided. (CW)

  2. Gold nanoparticles in model biological membranes: A computational perspective.

    PubMed

    Rossi, Giulia; Monticelli, Luca

    2016-10-01

    The electronic, optical, catalytic, and magnetic properties of metal nanoparticles (NPs) make them extremely interesting for biomedical applications. In this rapidly moving field, monolayer-protected gold nanoparticles emerge both as a reference system and as promising candidates for drug and gene delivery, photothermal treatment, and imaging applications. Despite the technological relevance, there is still poor understanding of the molecular processes driving the interactions of metal nanoparticles with cells, and with cell membranes in particular. In this paper we review molecular-level computational studies of the interaction between monolayer-protected gold NPs and model lipid membranes. Our review comprises a brief description of the most relevant experimental results in this field and of the questions they raised, followed by a description of the computational achievements reported so far. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg.

  3. Functional identification of biological neural networks using reservoir adaptation for point processes.

    PubMed

    Gürel, Tayfun; Rotter, Stefan; Egert, Ulrich

    2010-08-01

    The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.

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

    PubMed Central

    2013-01-01

    Background Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. Results In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user

  5. Click Chemistry Mediated Functionalization of Vertical Nanowires for Biological Applications.

    PubMed

    Vutti, Surendra; Schoffelen, Sanne; Bolinsson, Jessica; Buch-Månson, Nina; Bovet, Nicolas; Nygård, Jesper; Martinez, Karen L; Meldal, Morten

    2016-01-11

    Semiconductor nanowires (NWs) are gaining significant importance in various biological applications, such as biosensing and drug delivery. Efficient and controlled immobilization of biomolecules on the NW surface is crucial for many of these applications. Here, we present for the first time the use of the Cu(I) -catalyzed alkyne-azide cycloaddition and its strain-promoted variant for the covalent functionalization of vertical NWs with peptides and proteins. The potential of the approach was demonstrated in two complementary applications of measuring enzyme activity and protein binding, which is of general interest for biological studies. The attachment of a peptide substrate provided NW arrays for the detection of protease activity. In addition, green fluorescent protein was immobilized in a site-specific manner and recognized by antibody binding to demonstrate the proof-of-concept for the use of covalently modified NWs for diagnostic purposes using minute amounts of material.

  6. Phenological response of a key ecosystem function to biological invasion.

    PubMed

    Alp, Maria; Cucherousset, Julien; Buoro, Mathieu; Lecerf, Antoine

    2016-05-01

    Although climate warming has been widely demonstrated to induce shifts in the timing of many biological events, the phenological consequences of other prominent global change drivers remain largely unknown. Here, we investigated the effects of biological invasions on the seasonality of leaf litter decomposition, a crucial freshwater ecosystem function. Decomposition rates were quantified in 18 temperate shallow lakes distributed along a gradient of crayfish invasion and a temperature-based model was constructed to predict yearly patterns of decomposition. We found that, through direct detritus consumption, omnivorous invasive crayfish accelerated decomposition rates up to fivefold in spring, enhancing temperature dependence of the process and shortening the period of major detritus availability in the ecosystem by up to 39 days (95% CI: 15-61). The fact that our estimates are an order of magnitude higher than any previously reported climate-driven phenological shifts indicates that some powerful drivers of phenological change have been largely overlooked. PMID:26931804

  7. Dynamically repairing and replacing neural networks: using hybrid computational and biological tools.

    PubMed

    Sanchez, Justin; Lytton, William; Carmena, Jose; Principe, Jose; Fortes, Jose; Barbour, Randall; Francis, Joseph

    2012-01-01

    The debilitating effects of injury to the nervous system can have a profound effect on daily life activities of the injured person. In this article, we present a project overview in which we are utilizing computational and biological principles, along with simulation and experimentation, to create a realistic computational model of natural and injured sensorimotor control systems. Through the development of hybrid in silico/biological coadaptive symbiotic systems, the goal is to create new technologies that yield transformative neuroprosthetic rehabilitative solutions and a new test bed for the development of integrative medical devices for the repair and enhancement of biological systems. PMID:22344954

  8. Chemical and biological flocculation process to treat municipal sewage and analysis of biological function.

    PubMed

    Xia, Si-qing; Yang, Dian-hai; Xu, Bin; Zhao, Jian-fu

    2005-01-01

    The pilot-scale experimental apparatus and the procedure of the chemical and biological flocculation process to verify the feasibility in treating Shanghai municipal sewage were introduced in this paper. In addition, the biological function of the process was discussed. The results of optimal running showed that in the reaction tank, the concentration of mixed liquor suspended solid(MLSS) was 2 g/L, hydraulic retention time(HRT) was 35 min, dosage of liquid polyaluminium chloride(PAC) was 60 mg/L, and the concentration of polyacrylamide(PAM) was 0.5 mg/L. The effluent average concentrations of COD(Cr), TP, SS and BOD5 were 50 mg/L, 0.62 mg/L, 18 mg/L, and 17 mg/L, respectively. These were better than the designed demand. In addition, the existence of biological degradation in this system was proven by several methods. The removal efficiencies of the chemical and biological flocculation process were 20% higher than that of the chemical flocculation process above at the same coagulant dosage. The treatment process under different situations was evaluated on a pilot-scale experiment, and the results provided magnificent parameters and optimal condition for future operation of the plant.

  9. Modeling Mendel's Laws on Inheritance in Computational Biology and Medical Sciences

    ERIC Educational Resources Information Center

    Singh, Gurmukh; Siddiqui, Khalid; Singh, Mankiran; Singh, Satpal

    2011-01-01

    The current research article is based on a simple and practical way of employing the computational power of widely available, versatile software MS Excel 2007 to perform interactive computer simulations for undergraduate/graduate students in biology, biochemistry, biophysics, microbiology, medicine in college and university classroom setting. To…

  10. A Descriptive Analysis of Computer-Assisted Teaching and Learning in Molecular Biological Education

    ERIC Educational Resources Information Center

    Li, Guangxing; Yin, Jiechao; Ren, Yudong; Wang, Binjie; Ren, Xiaofeng

    2006-01-01

    The role and importance of computer-assisted teaching and learning in molecular biological-related education and research has been emphasized and pinpointed. In this study, some benefit viewpoints and discussion are provided for applying the computer-assisted teaching and learning more efficiently in the process of knowledge acquisition and…

  11. Using parallel evolutionary development for a biologically-inspired computer vision system for mobile robots.

    PubMed

    Wright, Cameron H G; Barrett, Steven F; Pack, Daniel J

    2005-01-01

    We describe a new approach to attacking the problem of robust computer vision for mobile robots. The overall strategy is to mimic the biological evolution of animal vision systems. Our basic imaging sensor is based upon the eye of the common house fly, Musca domestica. The computational algorithms are a mix of traditional image processing, subspace techniques, and multilayer neural networks.

  12. Computer Literacy for Life Sciences: Helping the Digital-Era Biology Undergraduates Face Today's Research

    ERIC Educational Resources Information Center

    Smolinski, Tomasz G.

    2010-01-01

    Computer literacy plays a critical role in today's life sciences research. Without the ability to use computers to efficiently manipulate and analyze large amounts of data resulting from biological experiments and simulations, many of the pressing questions in the life sciences could not be answered. Today's undergraduates, despite the ubiquity of…

  13. A Computer-Aided Self-Testing System for Biological Psychology.

    ERIC Educational Resources Information Center

    Leiblum, M. D.; And Others

    1994-01-01

    Describes the production of a computer-aided, self-testing system for university students enrolled in a first-year course in biological psychology. Project aspects described include selection, acquisition and description of software; question banks and test structures; modes of use (computer or printed version); evaluation; and future plans. (11…

  14. Using Mathematics to Bridge the Gap between Biology and Computer Science

    ERIC Educational Resources Information Center

    Hammerman, Natalie; Tolvo, Anthony; Goldberg, Robert

    2004-01-01

    The rapid rate of expansion of the disciplines of biotechnology, genomics, and bioinformatics emphasizes the increased interdependency between computer science and biology, with mathematics serving as the bridge between these disciplines. This paper demonstrates this inter-relationship within the context of a computational model for a biological…

  15. Considerations to improve functional annotations in biological databases.

    PubMed

    Benítez-Páez, Alfonso

    2009-12-01

    Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community. PMID:20050264

  16. Machine learning in cell biology - teaching computers to recognize phenotypes.

    PubMed

    Sommer, Christoph; Gerlich, Daniel W

    2013-12-15

    Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline. PMID:24259662

  17. Machine learning in cell biology - teaching computers to recognize phenotypes.

    PubMed

    Sommer, Christoph; Gerlich, Daniel W

    2013-12-15

    Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

  18. Biomarkers of Aging: From Function to Molecular Biology

    PubMed Central

    Wagner, Karl-Heinz; Cameron-Smith, David; Wessner, Barbara; Franzke, Bernhard

    2016-01-01

    Aging is a major risk factor for most chronic diseases and functional impairments. Within a homogeneous age sample there is a considerable variation in the extent of disease and functional impairment risk, revealing a need for valid biomarkers to aid in characterizing the complex aging processes. The identification of biomarkers is further complicated by the diversity of biological living situations, lifestyle activities and medical treatments. Thus, there has been no identification of a single biomarker or gold standard tool that can monitor successful or healthy aging. Within this short review the current knowledge of putative biomarkers is presented, focusing on their application to the major physiological mechanisms affected by the aging process including physical capability, nutritional status, body composition, endocrine and immune function. This review emphasizes molecular and DNA-based biomarkers, as well as recent advances in other biomarkers such as microRNAs, bilirubin or advanced glycation end products. PMID:27271660

  19. Systemic Modeling of Biological Functions in Consideration of Physiome Project

    NASA Astrophysics Data System (ADS)

    Minamitani, Haruyuki

    Emerging of the physiome project provides various influences on the medical, biological and pharmaceutical development. In this paper, as an example of physiome research, neural network model analysis providing the conduction mechanisms of pain and tactile sensations was presented, and the functional relations between neural activities of the network cells and stimulus intensity applied on the peripheral receptive fields were described. The modeling presented here is based on the various assumptions made by the results of physiological and anatomical studies reported in the literature. The functional activities of spinothalamic and thalamocortical cells show a good agreement with the physiological and psychophysical functions of somatosensory system that are very instructive for covering the gap between physiologically and psychophysically aspects of pain and tactile sensation.

  20. Density functional theory across chemistry, physics and biology.

    PubMed

    van Mourik, Tanja; Bühl, Michael; Gaigeot, Marie-Pierre

    2014-03-13

    The past decades have seen density functional theory (DFT) evolve from a rising star in computational quantum chemistry to one of its major players. This Theme Issue, which comes half a century after the publication of the Hohenberg-Kohn theorems that laid the foundations of modern DFT, reviews progress and challenges in present-day DFT research. Rather than trying to be comprehensive, this Theme Issue attempts to give a flavour of selected aspects of DFT.

  1. Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

    PubMed Central

    Waltemath, Dagmar; Bergmann, Frank T.; Chaouiya, Claudine; Czauderna, Tobias; Gleeson, Padraig; Goble, Carole; Golebiewski, Martin; Hucka, Michael; Juty, Nick; Krebs, Olga; Le Novère, Nicolas; Mi, Huaiyu; Moraru, Ion I.; Myers, Chris J.; Nickerson, David; Olivier, Brett G.; Rodriguez, Nicolas; Schreiber, Falk; Smith, Lucian; Zhang, Fengkai; Bonnet, Eric

    2014-01-01

    The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.

  2. Develop Infrared Structural Biology for Probing Structural Dynamics of Protein Functions

    NASA Astrophysics Data System (ADS)

    Xie, Aihua; Kang, Zhouyang; Causey, Oliver; Liu, Charle

    2015-03-01

    Protein functions are carried out through a series of structural transitions. Lack of knowledge on functionally important structural motions of proteins impedes our understanding of protein functions. Infrared structural biology is an emerging technology with powerful applications for protein structural dynamics. One key element of infrared structural biology is the development of vibrational structural marker (VSM) database library that translates infrared spectroscopic signals into specific structural information. We report the development of VSM for probing the type, geometry and strength of hydrogen bonding interactions of buried COO- side chains of Asp and Glu in proteins. Quantum theory based first principle computational studies combined with bioinformatic hydrogen bond analysis are employed in this study. We will discuss the applications of VSM in mechanistic studies of protein functions. Infrared structural biology is expected to emerge as a powerful technique for elucidating the functional mechanism of a broad range of proteins, including water soluble and membrane proteins. This work is supported by OCAST HR10-078 and NSF DBI1338097.

  3. Tubulin acetylation: responsible enzymes, biological functions and human diseases.

    PubMed

    Li, Lin; Yang, Xiang-Jiao

    2015-11-01

    Microtubules have important functions ranging from maintenance of cell morphology to subcellular transport, cellular signaling, cell migration, and formation of cell polarity. At the organismal level, microtubules are crucial for various biological processes, such as viral entry, inflammation, immunity, learning and memory in mammals. Microtubules are subject to various covalent modifications. One such modification is tubulin acetylation, which is associated with stable microtubules and conserved from protists to humans. In the past three decades, this reversible modification has been studied extensively. In mammals, its level is mainly governed by opposing actions of α-tubulin acetyltransferase 1 (ATAT1) and histone deacetylase 6 (HDAC6). Knockout studies of the mouse enzymes have yielded new insights into biological functions of tubulin acetylation. Abnormal levels of this modification are linked to neurological disorders, cancer, heart diseases and other pathological conditions, thereby yielding important therapeutic implications. This review summarizes related studies and concludes that tubulin acetylation is important for regulating microtubule architecture and maintaining microtubule integrity. Together with detyrosination, glutamylation and other modifications, tubulin acetylation may form a unique 'language' to regulate microtubule structure and function.

  4. mRNA capping: biological functions and applications.

    PubMed

    Ramanathan, Anand; Robb, G Brett; Chan, Siu-Hong

    2016-09-19

    The 5' m7G cap is an evolutionarily conserved modification of eukaryotic mRNA. Decades of research have established that the m7G cap serves as a unique molecular module that recruits cellular proteins and mediates cap-related biological functions such as pre-mRNA processing, nuclear export and cap-dependent protein synthesis. Only recently has the role of the cap 2'O methylation as an identifier of self RNA in the innate immune system against foreign RNA has become clear. The discovery of the cytoplasmic capping machinery suggests a novel level of control network. These new findings underscore the importance of a proper cap structure in the synthesis of functional messenger RNA. In this review, we will summarize the current knowledge of the biological roles of mRNA caps in eukaryotic cells. We will also discuss different means that viruses and their host cells use to cap their RNA and the application of these capping machineries to synthesize functional mRNA. Novel applications of RNA capping enzymes in the discovery of new RNA species and sequencing the microbiome transcriptome will also be discussed. We will end with a summary of novel findings in RNA capping and the questions these findings pose. PMID:27317694

  5. mRNA capping: biological functions and applications.

    PubMed

    Ramanathan, Anand; Robb, G Brett; Chan, Siu-Hong

    2016-09-19

    The 5' m7G cap is an evolutionarily conserved modification of eukaryotic mRNA. Decades of research have established that the m7G cap serves as a unique molecular module that recruits cellular proteins and mediates cap-related biological functions such as pre-mRNA processing, nuclear export and cap-dependent protein synthesis. Only recently has the role of the cap 2'O methylation as an identifier of self RNA in the innate immune system against foreign RNA has become clear. The discovery of the cytoplasmic capping machinery suggests a novel level of control network. These new findings underscore the importance of a proper cap structure in the synthesis of functional messenger RNA. In this review, we will summarize the current knowledge of the biological roles of mRNA caps in eukaryotic cells. We will also discuss different means that viruses and their host cells use to cap their RNA and the application of these capping machineries to synthesize functional mRNA. Novel applications of RNA capping enzymes in the discovery of new RNA species and sequencing the microbiome transcriptome will also be discussed. We will end with a summary of novel findings in RNA capping and the questions these findings pose.

  6. mRNA capping: biological functions and applications

    PubMed Central

    Ramanathan, Anand; Robb, G. Brett; Chan, Siu-Hong

    2016-01-01

    The 5′ m7G cap is an evolutionarily conserved modification of eukaryotic mRNA. Decades of research have established that the m7G cap serves as a unique molecular module that recruits cellular proteins and mediates cap-related biological functions such as pre-mRNA processing, nuclear export and cap-dependent protein synthesis. Only recently has the role of the cap 2′O methylation as an identifier of self RNA in the innate immune system against foreign RNA has become clear. The discovery of the cytoplasmic capping machinery suggests a novel level of control network. These new findings underscore the importance of a proper cap structure in the synthesis of functional messenger RNA. In this review, we will summarize the current knowledge of the biological roles of mRNA caps in eukaryotic cells. We will also discuss different means that viruses and their host cells use to cap their RNA and the application of these capping machineries to synthesize functional mRNA. Novel applications of RNA capping enzymes in the discovery of new RNA species and sequencing the microbiome transcriptome will also be discussed. We will end with a summary of novel findings in RNA capping and the questions these findings pose. PMID:27317694

  7. Computational systems biology approaches to anti-angiogenic cancer therapeutics.

    PubMed

    Finley, Stacey D; Chu, Liang-Hui; Popel, Aleksander S

    2015-02-01

    Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

  8. Hyaluronan: A Simple Polysaccharide with Diverse Biological Functions

    PubMed Central

    Dicker, Kevin T.; Gurski, Lisa A.; Pradhan-Bhatt, Swati; Witt, Robert L.; Farach-Carson, Mary C.; Jia, Xinqiao

    2014-01-01

    Hyaluronan (HA) is a linear polysaccharide with disaccharide repeats of D-glucuronic acid and N-acetyl-D-glucosamine. It is evolutionary conserved and abundantly expressed in the extracellular matrix (ECM), on the cell surface and even inside cells. Being a simple polysaccharide, HA exhibits an astonishing array of biological functions. HA interacts with various proteins or proteoglycans to organize the ECM and to maintain tissue homeostasis. The unique physical and mechanical properties of HA contribute to the maintenance of tissue hydration, the mediation of solute diffusion through the extracellular space and the lubrication of certain tissues. The diverse biological functions of HA are manifested through its complex interactions with matrix components and resident cells. Binding of HA with cell surface receptors activates various signaling pathways that regulate cell function, tissue development, inflammation, wound healing and tumor progression and metastasis. Taking advantage of the inherent biocompatibility and biodegradability of HA, as well as its susceptibility to chemical modification, researchers have developed various HA-based biomaterials and tissue constructs with promising and broad clinical potential. In this article, we illustrate the properties of HA from a matrix biology perspective by first introducing principles underlying the biosynthesis and biodegradation of HA, as well as the interactions of HA with various proteins and proteoglycans. We next highlight the roles of HA in physiological and pathological states, including morphogenesis, wound healing and tumor metastasis. A deeper understanding of the mechanisms underlying the roles of HA in various physiological processes can provide new insights and tools for the engineering of complex tissues and tissue models. PMID:24361428

  9. Activation of PPARδ: from computer modelling to biological effects

    PubMed Central

    Kahremany, Shirin; Livne, Ariela; Gruzman, Arie; Senderowitz, Hanoch; Sasson, Shlomo

    2015-01-01

    PPARδ is a ligand-activated receptor that dimerizes with another nuclear receptor of the retinoic acid receptor family. The dimers interact with other co-activator proteins and form active complexes that bind to PPAR response elements and promote transcription of genes involved in lipid metabolism. It appears that various natural fatty acids and their metabolites serve as endogenous activators of PPARδ; however, there is no consensus in the literature on the nature of the prime activators of the receptor. In vitro and cell-based assays of PPARδ activation by fatty acids and their derivatives often produce conflicting results. The search for synthetic and selective PPARδ agonists, which may be pharmacologically useful, is intense. Current rational modelling used to obtain such compounds relies mostly on crystal structures of synthetic PPARδ ligands with the recombinant ligand binding domain (LBD) of the receptor. Here, we introduce an original computational prediction model for ligand binding to PPARδ LBD. The model was built based on EC50 data of 16 ligands with available crystal structures and validated by calculating binding probabilities of 82 different natural and synthetic compounds from the literature. These compounds were independently tested in cell-free and cell-based assays for their capacity to bind or activate PPARδ, leading to prediction accuracy of between 70% and 93% (depending on ligand type). This new computational tool could therefore be used in the search for natural and synthetic agonists of the receptor. PMID:25255770

  10. How computational models can help unlock biological systems.

    PubMed

    Brodland, G Wayne

    2015-12-01

    With computation models playing an ever increasing role in the advancement of science, it is important that researchers understand what it means to model something; recognize the implications of the conceptual, mathematical and algorithmic steps of model construction; and comprehend what models can and cannot do. Here, we use examples to show that models can serve a wide variety of roles, including hypothesis testing, generating new insights, deepening understanding, suggesting and interpreting experiments, tracing chains of causation, doing sensitivity analyses, integrating knowledge, and inspiring new approaches. We show that models can bring together information of different kinds and do so across a range of length scales, as they do in multi-scale, multi-faceted embryogenesis models, some of which connect gene expression, the cytoskeleton, cell properties, tissue mechanics, morphogenetic movements and phenotypes. Models cannot replace experiments nor can they prove that particular mechanisms are at work in a given situation. But they can demonstrate whether or not a proposed mechanism is sufficient to produce an observed phenomenon. Although the examples in this article are taken primarily from the field of embryo mechanics, most of the arguments and discussion are applicable to any form of computational modelling.

  11. Functionalization of polydopamine coated magnetic nanoparticles with biological entities

    NASA Astrophysics Data System (ADS)

    Mǎgeruşan, Lidia; Mrówczyński, Radosław; Turcu, Rodica

    2015-12-01

    New hybrid materials, obtained through introduction of cysteine, lysine and folic acid as biological entities into polydopamine-coated magnetite nanoparticles, are reported. The syntheses are straight forward and various methods were applied for structural and morphological characterization of the resulting nanoparticles. XPS proved a very powerful tool for surface chemical analysis and it evidences the functionalization of polydopamine coated magnetite nanoparticles. The superparamagnetic behavior and the high values of saturation magnetization recommend all products for further application where magnetism is important for targeting, separation, or heating by alternative magnetic fields.

  12. Structure and Biological Functions of β-Hairpin Antimicrobial Peptides

    PubMed Central

    Panteleev, P. V.; Bolosov, I. A.; Balandin, S. V.; Ovchinnikova, T. V.

    2015-01-01

    Antimicrobial peptides (AMPs) are evolutionarily ancient factors of the innate immune system that serve as a crucial first line of defense for humans, animals, and plants against infection. This review focuses on the structural organization, biosynthesis, and biological functions of AMPs that possess a β-hairpin spatial structure. Representatives of this class of AMPs are among the most active antibiotic molecules of animal origin. Due to their wide spectrum of activity and resistance to internal environmental factors, natural β-hairpin AMPbased compounds might become the most promising drug candidates. PMID:25927000

  13. Really computing nonperturbative real time correlation functions

    NASA Astrophysics Data System (ADS)

    Bödeker, Dietrich; McLerran, Larry; Smilga, Andrei

    1995-10-01

    It has been argued by Grigoriev and Rubakov that one can simulate real time processes involving baryon number nonconservation at high temperature using real time evolution of classical equations, and summing over initial conditions with a classical thermal weight. It is known that such a naive algorithm is plagued by ultraviolet divergences. In quantum theory the divergences are regularized, but the corresponding graphs involve the contributions from the hard momentum region and also the new scale ~gT comes into play. We propose a modified algorithm which involves solving the classical equations of motion for the effective hard thermal loop Hamiltonian with an ultraviolet cutoff μ>>gT and integrating over initial conditions with a proper thermal weight. Such an algorithm should provide a determination of the infrared behavior of the real time correlation function T determining the baryon violation rate. Hopefully, the results obtained in this modified algorithm will be cutoff independent.

  14. Basis Function Sampling for Material Property Computations

    NASA Astrophysics Data System (ADS)

    Whitmer, Jonathan K.; Chiu, Chi-Cheng; Joshi, Abhijeet A.; de Pablo, Juan J.

    2014-03-01

    Wang-Landau sampling, and the associated class of flat histogram simulation methods, have been particularly successful for free energy calculations in a wide array of physical systems. Practically, the convergence of these calculations to a target free energy surface is hampered by reliance on parameters which are unknown a priori. We derive and implement a method based on orthogonal (basis) functions which is fast, parameter-free, and geometrically robust. An important feature of this method is its ability to achieve arbitrary levels of description for the free energy. It is thus ideally suited to in silico measurement of elastic moduli and other quantities related to free energy perturbations. We demonstrate the utility of such applications by applying our method to calculation of the Frank elastic constants of the Lebwohl-Lasher model.

  15. Computational biology approach to uncover hepatitis C virus helicase operation.

    PubMed

    Flechsig, Holger

    2014-04-01

    Hepatitis C virus (HCV) helicase is a molecular motor that splits nucleic acid duplex structures during viral replication, therefore representing a promising target for antiviral treatment. Hence, a detailed understanding of the mechanism by which it operates would facilitate the development of efficient drug-assisted therapies aiming to inhibit helicase activity. Despite extensive investigations performed in the past, a thorough understanding of the activity of this important protein was lacking since the underlying internal conformational motions could not be resolved. Here we review investigations that have been previously performed by us for HCV helicase. Using methods of structure-based computational modelling it became possible to follow entire operation cycles of this motor protein in structurally resolved simulations and uncover the mechanism by which it moves along the nucleic acid and accomplishes strand separation. We also discuss observations from that study in the light of recent experimental studies that confirm our findings.

  16. Computational biology approach to uncover hepatitis C virus helicase operation

    PubMed Central

    Flechsig, Holger

    2014-01-01

    Hepatitis C virus (HCV) helicase is a molecular motor that splits nucleic acid duplex structures during viral replication, therefore representing a promising target for antiviral treatment. Hence, a detailed understanding of the mechanism by which it operates would facilitate the development of efficient drug-assisted therapies aiming to inhibit helicase activity. Despite extensive investigations performed in the past, a thorough understanding of the activity of this important protein was lacking since the underlying internal conformational motions could not be resolved. Here we review investigations that have been previously performed by us for HCV helicase. Using methods of structure-based computational modelling it became possible to follow entire operation cycles of this motor protein in structurally resolved simulations and uncover the mechanism by which it moves along the nucleic acid and accomplishes strand separation. We also discuss observations from that study in the light of recent experimental studies that confirm our findings. PMID:24707123

  17. Current studies on physiological functions and biological production of lactosucrose.

    PubMed

    Mu, Wanmeng; Chen, Qiuming; Wang, Xiao; Zhang, Tao; Jiang, Bo

    2013-08-01

    Lactosucrose (O-β-D-galactopyranosyl-(1,4)-O-α-D-glucopyranosyl-(1,2)-β-D-fructofuranoside) is a trisaccharide formed from lactose and sucrose by enzymatic transglycosylation. This rare trisaccharide is a kind of indigestible carbohydrate, has good prebiotic effect, and promotes intestinal mineral absorption. It has been used as a functional ingredient in a range of food products which are approved as foods for specified health uses in Japan. Using lactose and sucrose as substrates, lactosucrose can be produced through transfructosylation by β-fructofuranosidase from Arthrobacter sp. K-1 or a range of levansucrases, or through transgalactosylation by β-galactosidase from Bacillus circulans. This article presented a review of recent studies on the physiological functions of lactosucrose and the biological production from lactose and sucrose by different enzymes. PMID:23828605

  18. Current studies on physiological functions and biological production of lactosucrose.

    PubMed

    Mu, Wanmeng; Chen, Qiuming; Wang, Xiao; Zhang, Tao; Jiang, Bo

    2013-08-01

    Lactosucrose (O-β-D-galactopyranosyl-(1,4)-O-α-D-glucopyranosyl-(1,2)-β-D-fructofuranoside) is a trisaccharide formed from lactose and sucrose by enzymatic transglycosylation. This rare trisaccharide is a kind of indigestible carbohydrate, has good prebiotic effect, and promotes intestinal mineral absorption. It has been used as a functional ingredient in a range of food products which are approved as foods for specified health uses in Japan. Using lactose and sucrose as substrates, lactosucrose can be produced through transfructosylation by β-fructofuranosidase from Arthrobacter sp. K-1 or a range of levansucrases, or through transgalactosylation by β-galactosidase from Bacillus circulans. This article presented a review of recent studies on the physiological functions of lactosucrose and the biological production from lactose and sucrose by different enzymes.

  19. Diffusion of innovations dynamics, biological growth and catenary function

    NASA Astrophysics Data System (ADS)

    Guseo, Renato

    2016-12-01

    The catenary function has a well-known role in determining the shape of chains and cables supported at their ends under the force of gravity. This enables design using a specific static equilibrium over space. Its reflected version, the catenary arch, allows the construction of bridges and arches exploiting the dual equilibrium property under uniform compression. In this paper, we emphasize a further connection with well-known aggregate biological growth models over time and the related diffusion of innovation key paradigms (e.g., logistic and Bass distributions over time) that determine self-sustaining evolutionary growth dynamics in naturalistic and socio-economic contexts. Moreover, we prove that the 'local entropy function', related to a logistic distribution, is a catenary and vice versa. This special invariance may be explained, at a deeper level, through the Verlinde's conjecture on the origin of gravity as an effect of the entropic force.

  20. STAT6: its role in interleukin 4-mediated biological functions.

    PubMed

    Takeda, K; Kishimoto, T; Akira, S

    1997-05-01

    Interleukin (IL) 4 is known to be a cytokine which plays a central role in the regulation of immune response. Studies on cytokine signal transduction have clarified the mechanism by which IL4 exerts its functions. Two cytoplasmic proteins, signal transducer and activator of transcription (STAT) 6 and IL4-induced phosphotyrosine substrate/insulin receptor substrate 2 (4PS/IRS2), are activated in IL4 signal transduction. Recent studies from STAT6-deficient mice have revealed the essential role of STAT6 in IL4-mediated biological actions. In addition, STAT6 has also been demonstrated to be important for the functions mediated by IL13, which is related to IL4. IL4 and IL13 have been shown to induce the production of IgE, which is a major mediator in an allergic response. These findings indicate that STAT6 activation is involved in IL4- and IL13-mediated disorders such as allergy.

  1. Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?

    PubMed

    Sevimoglu, Tuba; Arga, Kazim Yalcin

    2015-11-01

    Computational biology and 'omics' systems sciences are greatly impacting research on common diseases such as cancer. By contrast, dermatology covering an array of skin diseases with high prevalence in society, has received relatively less attention from 'omics' and computational biosciences. We are focusing on psoriasis, a common and debilitating autoimmune disease involving skin and joints. Using computational systems biology and reconstruction, topological, modular, and a novel correlational analyses (based on fold changes) of biological and transcriptional regulatory networks, we analyzed and integrated data from a total of twelve studies from the Gene Expression Omnibus (sample size = 534). Samples represented a comprehensive continuum from lesional and nonlesional skin, as well as bone marrow and dermal mesenchymal stem cells. We identified and propose here a JAK/STAT signaling pathway significant for psoriasis. Importantly, cytokines, interferon-stimulated genes, antimicrobial peptides, among other proteins, were involved in intrinsic parts of the proposed pathway. Several biomarker and therapeutic candidates such as SUB1 are discussed for future experimental studies. The integrative systems biology approach presented here illustrates a comprehensive perspective on the molecular basis of psoriasis. This also attests to the promise of systems biology research in skin diseases, with psoriasis as a systemic component. The present study reports, to the best of our knowledge, the largest set of microarray datasets on psoriasis, to offer new insights into the disease mechanisms with a proposal of a disease pathway. We call for greater computational systems biology research and analyses in dermatology and skin diseases in general.

  2. Dynamics of Boolean networks controlled by biologically meaningful functions.

    PubMed

    Raeymaekers, L

    2002-10-01

    The remarkably stable dynamics displayed by randomly constructed Boolean networks is one of the most striking examples of the spontaneous emergence of self-organization in model systems composed of many interacting elements (Kauffman, S., J. theor. Biol.22, 437-467, 1969; The Origins of Order, Oxford University Press, Oxford, 1993). The dynamics of such networks is most stable for a connectivity of two inputs per element, and decreases dramatically with increasing number of connections. Whereas the simplicity of this model system allows the tracing of the dynamical trajectories, it leaves out many features of real biological connections. For instance, the dynamics has been studied in detail only for networks constructed by allowing all theoretically possible Boolean rules, whereas only a subset of them make sense in the material world. This paper analyses the effect on the dynamics of using only Boolean functions which are meaningful in a biological sense. This analysis is particularly relevant for nets with more than two inputs per element because biological networks generally appear to be more extensively interconnected. Sets of the meaningful functions were assembled for up to four inputs per element. The use of these rules results in a smaller number of distinct attractors which have a shorter length, with relatively little sensitivity to the size of the network and to the number of inputs per element. Forcing away the activator/inhibitor ratio from the expected value of 50% further enhances the stability. This effect is more pronounced for networks consisting of a majority of activators than for networks with a corresponding majority of inhibitors, indicating that the former allow the evolution of larger genetic networks. The data further support the idea of the usefulness of logical networks as a conceptual framework for the understanding of real-world phenomena.

  3. Event-based text mining for biology and functional genomics.

    PubMed

    Ananiadou, Sophia; Thompson, Paul; Nawaz, Raheel; McNaught, John; Kell, Douglas B

    2015-05-01

    The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of 'events', i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

  4. Sucrose metabolism gene families and their biological functions.

    PubMed

    Jiang, Shu-Ye; Chi, Yun-Hua; Wang, Ji-Zhou; Zhou, Jun-Xia; Cheng, Yan-Song; Zhang, Bao-Lan; Ma, Ali; Vanitha, Jeevanandam; Ramachandran, Srinivasan

    2015-11-30

    Sucrose, as the main product of photosynthesis, plays crucial roles in plant development. Although studies on general metabolism pathway were well documented, less information is available on the genome-wide identification of these genes, their expansion and evolutionary history as well as their biological functions. We focused on four sucrose metabolism related gene families including sucrose synthase, sucrose phosphate synthase, sucrose phosphate phosphatase and UDP-glucose pyrophosphorylase. These gene families exhibited different expansion and evolutionary history as their host genomes experienced differentiated rates of the whole genome duplication, tandem and segmental duplication, or mobile element mediated gene gain and loss. They were evolutionarily conserved under purifying selection among species and expression divergence played important roles for gene survival after expansion. However, we have detected recent positive selection during intra-species divergence. Overexpression of 15 sorghum genes in Arabidopsis revealed their roles in biomass accumulation, flowering time control, seed germination and response to high salinity and sugar stresses. Our studies uncovered the molecular mechanisms of gene expansion and evolution and also provided new insight into the role of positive selection in intra-species divergence. Overexpression data revealed novel biological functions of these genes in flowering time control and seed germination under normal and stress conditions.

  5. Sucrose metabolism gene families and their biological functions

    PubMed Central

    Jiang, Shu-Ye; Chi, Yun-Hua; Wang, Ji-Zhou; Zhou, Jun-Xia; Cheng, Yan-Song; Zhang, Bao-Lan; Ma, Ali; Vanitha, Jeevanandam; Ramachandran, Srinivasan

    2015-01-01

    Sucrose, as the main product of photosynthesis, plays crucial roles in plant development. Although studies on general metabolism pathway were well documented, less information is available on the genome-wide identification of these genes, their expansion and evolutionary history as well as their biological functions. We focused on four sucrose metabolism related gene families including sucrose synthase, sucrose phosphate synthase, sucrose phosphate phosphatase and UDP-glucose pyrophosphorylase. These gene families exhibited different expansion and evolutionary history as their host genomes experienced differentiated rates of the whole genome duplication, tandem and segmental duplication, or mobile element mediated gene gain and loss. They were evolutionarily conserved under purifying selection among species and expression divergence played important roles for gene survival after expansion. However, we have detected recent positive selection during intra-species divergence. Overexpression of 15 sorghum genes in Arabidopsis revealed their roles in biomass accumulation, flowering time control, seed germination and response to high salinity and sugar stresses. Our studies uncovered the molecular mechanisms of gene expansion and evolution and also provided new insight into the role of positive selection in intra-species divergence. Overexpression data revealed novel biological functions of these genes in flowering time control and seed germination under normal and stress conditions. PMID:26616172

  6. Biological neural networks as model systems for designing future parallel processing computers

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  7. The program XEASY for computer-supported NMR spectral analysis of biological macromolecules.

    PubMed

    Bartels, C; Xia, T H; Billeter, M; Güntert, P; Wüthrich, K

    1995-07-01

    A new program package, XEASY, was written for interactive computer support of the analysis of NMR spectra for three-dimensional structure determination of biological macromolecules. XEASY was developed for work with 2D, 3D and 4D NMR data sets. It includes all the functions performed by the precursor program EASY, which was designed for the analysis of 2D NMR spectra, i.e., peak picking and support of sequence-specific resonance assignments, cross-peak assignments, cross-peak integration and rate constant determination for dynamic processes. Since the program utilizes the X-window system and the Motif widget set, it is portable on a wide range of UNIX workstations. The design objective was to provide maximal computer support for the analysis of spectra, while providing the user with complete control over the final resonance assignments. Technically important features of XEASY are the use and flexible visual display of 'strips', i.e., two-dimensional spectral regions that contain the relevant parts of 3D or 4D NMR spectra, automated sorting routines to narrow down the selection of strips that need to be interactively considered in a particular assignment step, a protocol of resonance assignments that can be used for reliable bookkeeping, independent of the assignment strategy used, and capabilities for proper treatment of spectral folding and efficient transfer of resonance assignments between spectra of different types and different dimensionality, including projected, reduced-dimensionality triple-resonance experiments.

  8. Multiscale computational models in physical systems biology of intracellular trafficking

    PubMed Central

    Tourdot, Richard W.; Bradley, Ryan P.; Ramakrishnan, Natesan

    2015-01-01

    In intracellular trafficking, a definitive understanding of the interplay between protein binding and membrane morphology remains incomplete. The authors describe a computational approach by integrating coarse-grained molecular dynamics (CGMD) simulations with continuum Monte Carlo (CM) simulations of the membrane to study protein–membrane interactions and the ensuing membrane curvature. They relate the curvature field strength discerned from the molecular level to its effect at the cellular length-scale. They perform thermodynamic integration on the CM model to describe the free energy landscape of vesiculation in clathrin-mediated endocytosis. The method presented here delineates membrane morphologies and maps out the free energy changes associated with membrane remodeling due to varying coat sizes, coat curvature strengths, membrane bending rigidities, and tensions; furthermore several constraints on mechanisms underlying clathrin-mediated endocytosis have also been identified, Their CGMD simulations have revealed the importance of PIP2 for stable binding of proteins essential for curvature induction in the bilayer and have provided a molecular basis for the positive curvature induction by the epsin N-terminal homology (EIMTH) domain. Calculation of the free energy landscape for vesicle budding has identified the critical size and curvature strength of a clathrin coat required for nucleation and stabilisation of a mature vesicle. PMID:25257021

  9. Persistence and availability of Web services in computational biology.

    PubMed

    Schultheiss, Sebastian J; Münch, Marc-Christian; Andreeva, Gergana D; Rätsch, Gunnar

    2011-01-01

    We have conducted a study on the long-term availability of bioinformatics Web services: an observation of 927 Web services published in the annual Nucleic Acids Research Web Server Issues between 2003 and 2009. We found that 72% of Web sites are still available at the published addresses, only 9% of services are completely unavailable. Older addresses often redirect to new pages. We checked the functionality of all available services: for 33%, we could not test functionality because there was no example data or a related problem; 13% were truly no longer working as expected; we could positively confirm functionality only for 45% of all services. Additionally, we conducted a survey among 872 Web Server Issue corresponding authors; 274 replied. 78% of all respondents indicate their services have been developed solely by students and researchers without a permanent position. Consequently, these services are in danger of falling into disrepair after the original developers move to another institution, and indeed, for 24% of services, there is no plan for maintenance, according to the respondents. We introduce a Web service quality scoring system that correlates with the number of citations: services with a high score are cited 1.8 times more often than low-scoring services. We have identified key characteristics that are predictive of a service's survival, providing reviewers, editors, and Web service developers with the means to assess or improve Web services. A Web service conforming to these criteria receives more citations and provides more reliable service for its users. The most effective way of ensuring continued access to a service is a persistent Web address, offered either by the publishing journal, or created on the authors' own initiative, for example at http://bioweb.me. The community would benefit the most from a policy requiring any source code needed to reproduce results to be deposited in a public repository.

  10. Persistence and availability of Web services in computational biology.

    PubMed

    Schultheiss, Sebastian J; Münch, Marc-Christian; Andreeva, Gergana D; Rätsch, Gunnar

    2011-01-01

    We have conducted a study on the long-term availability of bioinformatics Web services: an observation of 927 Web services published in the annual Nucleic Acids Research Web Server Issues between 2003 and 2009. We found that 72% of Web sites are still available at the published addresses, only 9% of services are completely unavailable. Older addresses often redirect to new pages. We checked the functionality of all available services: for 33%, we could not test functionality because there was no example data or a related problem; 13% were truly no longer working as expected; we could positively confirm functionality only for 45% of all services. Additionally, we conducted a survey among 872 Web Server Issue corresponding authors; 274 replied. 78% of all respondents indicate their services have been developed solely by students and researchers without a permanent position. Consequently, these services are in danger of falling into disrepair after the original developers move to another institution, and indeed, for 24% of services, there is no plan for maintenance, according to the respondents. We introduce a Web service quality scoring system that correlates with the number of citations: services with a high score are cited 1.8 times more often than low-scoring services. We have identified key characteristics that are predictive of a service's survival, providing reviewers, editors, and Web service developers with the means to assess or improve Web services. A Web service conforming to these criteria receives more citations and provides more reliable service for its users. The most effective way of ensuring continued access to a service is a persistent Web address, offered either by the publishing journal, or created on the authors' own initiative, for example at http://bioweb.me. The community would benefit the most from a policy requiring any source code needed to reproduce results to be deposited in a public repository. PMID:21966383

  11. Computing Health Quality Measures Using Informatics for Integrating Biology and the Bedside

    PubMed Central

    Murphy, Shawn N

    2013-01-01

    Background The Health Quality Measures Format (HQMF) is a Health Level 7 (HL7) standard for expressing computable Clinical Quality Measures (CQMs). Creating tools to process HQMF queries in clinical databases will become increasingly important as the United States moves forward with its Health Information Technology Strategic Plan to Stages 2 and 3 of the Meaningful Use incentive program (MU2 and MU3). Informatics for Integrating Biology and the Bedside (i2b2) is one of the analytical databases used as part of the Office of the National Coordinator (ONC)’s Query Health platform to move toward this goal. Objective Our goal is to integrate i2b2 with the Query Health HQMF architecture, to prepare for other HQMF use-cases (such as MU2 and MU3), and to articulate the functional overlap between i2b2 and HQMF. Therefore, we analyze the structure of HQMF, and then we apply this understanding to HQMF computation on the i2b2 clinical analytical database platform. Specifically, we develop a translator between two query languages, HQMF and i2b2, so that the i2b2 platform can compute HQMF queries. Methods We use the HQMF structure of queries for aggregate reporting, which define clinical data elements and the temporal and logical relationships between them. We use the i2b2 XML format, which allows flexible querying of a complex clinical data repository in an easy-to-understand domain-specific language. Results The translator can represent nearly any i2b2-XML query as HQMF and execute in i2b2 nearly any HQMF query expressible in i2b2-XML. This translator is part of the freely available reference implementation of the QueryHealth initiative. We analyze limitations of the conversion and find it covers many, but not all, of the complex temporal and logical operators required by quality measures. Conclusions HQMF is an expressive language for defining quality measures, and it will be important to understand and implement for CQM computation, in both meaningful use and population

  12. Computer-Intensive Algebra and Students' Conceptual Knowledge of Functions.

    ERIC Educational Resources Information Center

    O'Callaghan, Brian R.

    1998-01-01

    Describes a research project that examined the effects of the Computer-Intensive Algebra (CIA) and traditional algebra curricula on students' (N=802) understanding of the function concept. Results indicate that CIA students achieved a better understanding of functions and were better at the components of modeling, interpreting, and translating.…

  13. Positive Wigner Functions Render Classical Simulation of Quantum Computation Efficient

    NASA Astrophysics Data System (ADS)

    Mari, A.; Eisert, J.

    2012-12-01

    We show that quantum circuits where the initial state and all the following quantum operations can be represented by positive Wigner functions can be classically efficiently simulated. This is true both for continuous-variable as well as discrete variable systems in odd prime dimensions, two cases which will be treated on entirely the same footing. Noting the fact that Clifford and Gaussian operations preserve the positivity of the Wigner function, our result generalizes the Gottesman-Knill theorem. Our algorithm provides a way of sampling from the output distribution of a computation or a simulation, including the efficient sampling from an approximate output distribution in the case of sampling imperfections for initial states, gates, or measurements. In this sense, this work highlights the role of the positive Wigner function as separating classically efficiently simulable systems from those that are potentially universal for quantum computing and simulation, and it emphasizes the role of negativity of the Wigner function as a computational resource.

  14. iTools: A Framework for Classification, Categorization and Integration of Computational Biology Resources

    PubMed Central

    Dinov, Ivo D.; Rubin, Daniel; Lorensen, William; Dugan, Jonathan; Ma, Jeff; Murphy, Shawn; Kirschner, Beth; Bug, William; Sherman, Michael; Floratos, Aris; Kennedy, David; Jagadish, H. V.; Schmidt, Jeanette; Athey, Brian; Califano, Andrea; Musen, Mark; Altman, Russ; Kikinis, Ron; Kohane, Isaac; Delp, Scott; Parker, D. Stott; Toga, Arthur W.

    2008-01-01

    The advancement of the computational biology field hinges on progress in three fundamental directions – the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources–data, software tools and web-services. The iTools design, implementation and resource meta - data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource

  15. iTools: a framework for classification, categorization and integration of computational biology resources.

    PubMed

    Dinov, Ivo D; Rubin, Daniel; Lorensen, William; Dugan, Jonathan; Ma, Jeff; Murphy, Shawn; Kirschner, Beth; Bug, William; Sherman, Michael; Floratos, Aris; Kennedy, David; Jagadish, H V; Schmidt, Jeanette; Athey, Brian; Califano, Andrea; Musen, Mark; Altman, Russ; Kikinis, Ron; Kohane, Isaac; Delp, Scott; Parker, D Stott; Toga, Arthur W

    2008-05-28

    The advancement of the computational biology field hinges on progress in three fundamental directions--the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources--data, software tools and web-services. The iTools design, implementation and resource meta-data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management

  16. Structure activity relationships: their function in biological prediction

    SciTech Connect

    Schultz, T.W.

    1982-01-01

    Quantitative structure activity relationships provide a means of ranking or predicting biological effects based on chemical structure. For each compound used to formulate a structure activity model two kinds of quantitative information are required: (1) biological activity and (2) molecular properties. Molecular properties are of three types: (1) molecular shape, (2) physiochemical parameters, and (3) abstract quantitations of molecular structure. Currently the two best descriptors are the hydrophobic parameter, log 1-octanol/water partition coefficient (log P), and the /sup 1/X/sup v/(one-chi-v) molecular connectivity index. Biological responses can be divided into three main categories: (1) non-specific effects due to membrane perturbation, (2) non-specific effects due to interaction with functional groups of proteins, and (3) specific effects due to interaction with receptors. Twenty-six synthetic fossil fuel-related nitrogen-containing aromatic compounds were examined to determine the quantitative correlation between log P and /sup 1/X/sup v/ and population growth impairment of Tetrahymena pyriformis. Nitro-containing compounds are the most active, followed by amino-containing compounds and azaarenes. Within each analog series activity increases with alkyl substitution and ring addition. The planar model log BR = 0.5564 log P + 0.3000 /sup 1/X/sup v/ -2.0138 was determined using mono-nitrogen substituted compounds. Attempts to extrapolate this model to dinitrogen-containing molecules were, for the most part, unsuccessful because of a change in mode of action from membrane perturbation to uncoupling of oxidative phosphoralation.

  17. DOE EPSCoR Initiative in Structural and computational Biology/Bioinformatics

    SciTech Connect

    Wallace, Susan S.

    2008-02-21

    The overall goal of the DOE EPSCoR Initiative in Structural and Computational Biology was to enhance the competiveness of Vermont research in these scientific areas. To develop self-sustaining infrastructure, we increased the critical mass of faculty, developed shared resources that made junior researchers more competitive for federal research grants, implemented programs to train graduate and undergraduate students who participated in these research areas and provided seed money for research projects. During the time period funded by this DOE initiative: (1) four new faculty were recruited to the University of Vermont using DOE resources, three in Computational Biology and one in Structural Biology; (2) technical support was provided for the Computational and Structural Biology facilities; (3) twenty-two graduate students were directly funded by fellowships; (4) fifteen undergraduate students were supported during the summer; and (5) twenty-eight pilot projects were supported. Taken together these dollars resulted in a plethora of published papers, many in high profile journals in the fields and directly impacted competitive extramural funding based on structural or computational biology resulting in 49 million dollars awarded in grants (Appendix I), a 600% return on investment by DOE, the State and University.

  18. Integration of multiscale dendritic spine structure and function data into systems biology models

    PubMed Central

    Mancuso, James J.; Cheng, Jie; Yin, Zheng; Gilliam, Jared C.; Xia, Xiaofeng; Li, Xuping; Wong, Stephen T. C.

    2014-01-01

    Comprising 1011 neurons with 1014 synaptic connections the human brain is the ultimate systems biology puzzle. An increasing body of evidence highlights the observation that changes in brain function, both normal and pathological, consistently correlate with dynamic changes in neuronal anatomy. Anatomical changes occur on a full range of scales from the trafficking of individual proteins, to alterations in synaptic morphology both individually and on a systems level, to reductions in long distance connectivity and brain volume. The major sites of contact for synapsing neurons are dendritic spines, which provide an excellent metric for the number and strength of signaling connections between elements of functional neuronal circuits. A comprehensive model of anatomical changes and their functional consequences would be a holy grail for the field of systems neuroscience but its realization appears far on the horizon. Various imaging technologies have advanced to allow for multi-scale visualization of brain plasticity and pathology, but computational analysis of the big data sets involved forms the bottleneck toward the creation of multiscale models of brain structure and function. While a full accounting of techniques and progress toward a comprehensive model of brain anatomy and function is beyond the scope of this or any other single paper, this review serves to highlight the opportunities for analysis of neuronal spine anatomy and function provided by new imaging technologies and the high-throughput application of older technologies while surveying the strengths and weaknesses of currently available computational analytical tools and room for future improvement. PMID:25429262

  19. Androgen Receptor Structure, Function and Biology: From Bench to Bedside

    PubMed Central

    Davey, Rachel A; Grossmann, Mathis

    2016-01-01

    The actions of androgens such as testosterone and dihydrotestosterone are mediated via the androgen receptor (AR), a ligand-dependent nuclear transcription factor and member of the steroid hormone nuclear receptor family. Given its widespread expression in many cells and tissues, the AR has a diverse range of biological actions including important roles in the development and maintenance of the reproductive, musculoskeletal, cardiovascular, immune, neural and haemopoietic systems. AR signalling may also be involved in the development of tumours in the prostate, bladder, liver, kidney and lung. Androgens can exert their actions via the AR in a DNA binding-dependent manner to regulate target gene transcription, or in a non-DNA binding-dependent manner to initiate rapid, cellular events such as the phosphorylation of 2nd messenger signalling cascades. More recently, ligand-independent actions of the AR have also been identified. Given the large volume of studies relating to androgens and the AR, this review is not intended as an extensive review of all studies investigating the AR, but rather as an overview of the structure, function, signalling pathways and biology of the AR as well as its important role in clinical medicine, with emphasis on recent developments in this field. PMID:27057074

  20. Insights into IL-23 biology: From structure to function.

    PubMed

    Floss, Doreen M; Schröder, Jutta; Franke, Manuel; Scheller, Jürgen

    2015-10-01

    Interleukin (IL-)23 is a central cytokine controlling TH17 development. Overshooting IL-23 signaling contribute to autoimmune diseases. Moreover, GWAS studies have identified several SNPs within the IL-23 receptor, which are associated with autoimmune diseases. IL-23 is a member of the IL-12-type cytokine family and consists of IL-23p19 and p40. Within the IL-12 family, IL-12 and IL-23 share the p40 cytokine subunit and the IL-12Rβ1 as one chain of the receptor complex. For signaling, IL-23 triggers heterodimerization of IL-12Rβ1 and the IL-23R. Subsequently, signal transduction pathways including JAK/STAT, MAPK and PI3K are activated. Most studies have investigated the biological relevance of IL-23 in the development of TH17 cells and autoimmunity, whereas less is known about the molecular context of IL-23 biology. Therefore, we focused on IL-23 receptor complex assembly, signal transduction and functional relevance of IL-23R SNPs in the context of IL-23-inhibitory principles.

  1. Linking biological soil crust diversity to ecological functions

    NASA Astrophysics Data System (ADS)

    Glaser, Karin; Borchhardt, Nadine; Schulz, Karoline; Mikhailyuk, Tatiana; Baumann, Karen; Leinweber, Peter; Ulf, Karsten

    2016-04-01

    Biological soil crusts (BSCs) are an association of different microorganisms and soil particles in the top millimeters of the soil. They are formed by algae, cyanobacteria, microfungi, bacteria, bryophytes and lichens in various compositions. Our aim was to determine and compare the biodiversity of all occurring organisms in biogeographically different habitats, ranging from polar (both Arctic and Antarctic), subpolar (Scandinavia), temperate (Germany) to dry regions (Chile). The combination of microscopy and molecular techniques (next-generation sequencing) revealed highly diverse crust communities, whose composition clustered by region and correlates with habitat characteristics such as water content. The BSC biodiversity was then linked to the ecological function of the crusts. The functional role of the BSCs in the biogeochemical cycles of carbon, nitrogen and phosphorous is evaluated using an array of state of the art soil chemistry methods including Py-FIMS (pyrolysis field ionization mass spectrometry) and XANES (x-ray absorbance near edge structure). Total P as well as P fractions were quantified in all BSCs, adjacent soil underneath and comparable nearby soil of BSC-free areas revealing a remarkable accumulation of total phosphorous and a distinct pattern of P fractions in the crust. Further, we observed an indication of a different P-speciation composition in the crust compared with BSC-free soil. The data allow answering the question whether BSCs act as sink or source for these compounds, and how biodiversity controls the biogeochemical function of BSCs.

  2. Comparative genomics of pectinacetylesterases: Insight on function and biology

    PubMed Central

    de Souza, Amancio José; Pauly, Markus

    2015-01-01

    Pectin acetylation influences the gelling ability of this important plant polysaccharide for the food industry. Plant apoplastic pectinacetylesterases (PAEs) play a key role in regulating the degree of pectin acetylation and modifying their expression thus represents one way to engineer plant polysaccharides for food applications. Identifying the major active enzymes within the PAE gene family will aid in our understanding of this biological phenomena as well as provide the tools for direct trait manipulation. Using comparative genomics we propose that there is a minimal set of 4 distinct PAEs in plants. Possible functional diversification of the PAE family in the grasses is also explored with the identification of 3 groups of PAE genes specific to grasses. PMID:26237162

  3. Biological activity of lactoferrin-functionalized biomimetic hydroxyapatite nanocrystals

    PubMed Central

    Nocerino, Nunzia; Fulgione, Andrea; Iannaccone, Marco; Tomasetta, Laura; Ianniello, Flora; Martora, Francesca; Lelli, Marco; Roveri, Norberto; Capuano, Federico; Capparelli, Rosanna

    2014-01-01

    The emergence of bacterial strains resistant to antibiotics is a general public health problem. Progress in developing new molecules with antimicrobial properties has been made. In this study, we evaluated the biological activity of a hybrid nanocomposite composed of synthetic biomimetic hydroxyapatite surface-functionalized by lactoferrin (LF-HA). We evaluated the antimicrobial, anti-inflammatory, and antioxidant properties of LF-HA and found that the composite was active against both Gram-positive and Gram-negative bacteria, and that it modulated proinflammatory and anti-inflammatory responses and enhanced antioxidant properties as compared with LF alone. These results indicate the possibility of using LF-HA as an antimicrobial system and biomimetic hydroxyapatite as a candidate for innovative biomedical applications. PMID:24623976

  4. Peptide Self-Assembly for Crafting Functional Biological Materials

    PubMed Central

    Matson, John B.; Zha, R. Helen; Stupp, Samuel I.

    2011-01-01

    Self-assembling, peptide-based scaffolds are frontrunners in the search for biomaterials with widespread impact in regenerative medicine. The inherent biocompatibility and cell signaling capabilities of peptides, in combination with control of secondary structure, has led to the development of a broad range of functional materials with potential for many novel therapies. More recently, membranes formed through complexation of peptide nanostructures with natural biopolymers have led to the development of hierarchically-structured constructs with potentially far-reaching applications in biology and medicine. In this review, we highlight recent advances in peptide-based gels and membranes, including work from our group and others. Specifically, we discuss the application of peptide-based materials in the regeneration of bone and enamel, cartilage, and the central nervous system, as well as the transplantation of islets, wound-healing, cardiovascular therapies, and treatment of erectile dysfunction after prostatectomy PMID:22125413

  5. An interdepartmental Ph.D. program in computational biology and bioinformatics: the Yale perspective.

    PubMed

    Gerstein, Mark; Greenbaum, Dov; Cheung, Kei; Miller, Perry L

    2007-02-01

    Computational biology and bioinformatics (CBB), the terms often used interchangeably, represent a rapidly evolving biological discipline. With the clear potential for discovery and innovation, and the need to deal with the deluge of biological data, many academic institutions are committing significant resources to develop CBB research and training programs. Yale formally established an interdepartmental Ph.D. program in CBB in May 2003. This paper describes Yale's program, discussing the scope of the field, the program's goals and curriculum, as well as a number of issues that arose in implementing the program. (Further updated information is available from the program's website, www.cbb.yale.edu.)

  6. Functional cis-regulatory genomics for systems biology

    PubMed Central

    Nam, Jongmin; Dong, Ping; Tarpine, Ryan; Istrail, Sorin; Davidson, Eric H.

    2010-01-01

    Gene expression is controlled by interactions between trans-regulatory factors and cis-regulatory DNA sequences, and these interactions constitute the essential functional linkages of gene regulatory networks (GRNs). Validation of GRN models requires experimental cis-regulatory tests of predicted linkages to authenticate their identities and proposed functions. However, cis-regulatory analysis is, at present, at a severe bottleneck in genomic system biology because of the demanding experimental methodologies currently in use for discovering cis-regulatory modules (CRMs), in the genome, and for measuring their activities. Here we demonstrate a high-throughput approach to both discovery and quantitative characterization of CRMs. The unique aspect is use of DNA sequence tags to “barcode” CRM expression constructs, which can then be mixed, injected together into sea urchin eggs, and subsequently deconvolved. This method has increased the rate of cis-regulatory analysis by >100-fold compared with conventional one-by-one reporter assays. The utility of the DNA-tag reporters was demonstrated by the rapid discovery of 81 active CRMs from 37 previously unexplored sea urchin genes. We then obtained simultaneous high-resolution temporal characterization of the regulatory activities of more than 80 CRMs. On average 2–3 CRMs were discovered per gene. Comparison of endogenous gene expression profiles with those of the CRMs recovered from each gene showed that, for most cases, at least one CRM is active in each phase of endogenous expression, suggesting that CRM recovery was comprehensive. This approach will qualitatively alter the practice of GRN construction as well as validation, and will impact many additional areas of regulatory system biology. PMID:20142491

  7. Functionalized nanoparticles for biological imaging and detection applications

    NASA Astrophysics Data System (ADS)

    Mei, Bing C.

    Semiconductor quantum dots (QDs) and gold nanoparticles (AuNPs) have gained tremendous attention in the last decade as a result of their size-dependent spectroscopic properties. These nanoparticles have been a subject of intense study to bridge the gap between macroscopic and atomic behavior, as well as to generate new materials for novel applications in therapeutics, biological sensing, light emitting devices, microelectronics, lasers, and solar cells. One of the most promising areas for the use of these nanoparticles is in biotechnology, where their size-dependent optical properties are harnessed for imaging and sensing applications. However, these nanoparticles, as synthesized, are often not stable in aqueous media and lack simple and reliable means of covalently linking to biomolecules. The focus of this work is to advance the progress of these nanomaterials for biotechnology by synthesizing them, characterizing their optical properties and rendering them water-soluble and functional while maintaining their coveted optical properties. QDs were synthesized by an organometallic chemical procedure that utilizes coordinating solvents to provide brightly luminescent nanoparticles. The optical interactions of these QDs were studied as a function of concentration to identify particle size-dependent optimal concentrations, where scattering and indirection excitation are minimized and the amount light observed per particle is maximized. Both QDs and AuNPs were rendered water-soluble and stable in a broad range of biologically relevant conditions by using a series of ligands composed of dihydrolipoic acid (DHLA) appended to poly(ethylene glycol) methyl ether. By studying the stability of the surface modified AuNPs, we revealed some interesting information regarding the role of the surface ligand on the nanoparticle stability (i.e. solubility in high salt concentration, resistance to dithiothreitol competition and cyanide decomposition). Furthermore, the nanoparticles

  8. 3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging.

    PubMed

    Moon, Inkyu; Javidi, Bahram

    2008-12-01

    We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-line (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms.

  9. Removing the center from computing: biology's new mode of digital knowledge production.

    PubMed

    November, Joseph

    2011-06-01

    This article shows how the USA's National Institutes of Health (NIH) helped to bring about a major shift in the way computers are used to produce knowledge and in the design of computers themselves as a consequence of its early 1960s efforts to introduce information technology to biologists. Starting in 1960 the NIH sought to reform the life sciences by encouraging researchers to make use of digital electronic computers, but despite generous federal support biologists generally did not embrace the new technology. Initially the blame fell on biologists' lack of appropriate (i.e. digital) data for computers to process. However, when the NIH consulted MIT computer architect Wesley Clark about this problem, he argued that the computer's quality as a device that was centralized posed an even greater challenge to potential biologist users than did the computer's need for digital data. Clark convinced the NIH that if the agency hoped to effectively computerize biology, it would need to satisfy biologists' experimental and institutional needs by providing them the means to use a computer without going to a computing center. With NIH support, Clark developed the 1963 Laboratory Instrument Computer (LINC), a small, real-time interactive computer intended to be used inside the laboratory and controlled entirely by its biologist users. Once built, the LINC provided a viable alternative to the 1960s norm of large computers housed in computing centers. As such, the LINC not only became popular among biologists, but also served in later decades as an important precursor of today's computing norm in the sciences and far beyond, the personal computer.

  10. Computer method for identification of boiler transfer functions

    NASA Technical Reports Server (NTRS)

    Miles, J. H.

    1971-01-01

    An iterative computer method is described for identifying boiler transfer functions using frequency response data. An objective penalized performance measure and a nonlinear minimization technique are used to cause the locus of points generated by a transfer function to resemble the locus of points obtained from frequency response measurements. Different transfer functions can be tried until a satisfactory empirical transfer function to the system is found. To illustrate the method, some examples and some results from a study of a set of data consisting of measurements of the inlet impedance of a single tube forced flow boiler with inserts are given.

  11. Functional Skeletal Muscle Formation with a Biologic Scaffold

    PubMed Central

    Valentin, Jolene E.; Turner, Neill J.; Gilbert, Thomas W.; Badylak, Stephen F.

    2010-01-01

    Biologic scaffolds composed of extracellular matrix (ECM) have been used to reinforce or replace damaged or missing musculotendinous tissues in both preclinical studies and in human clinical applications. However, most studies have focused upon morphologic endpoints and few studies have assessed the in-situ functionality of newly formed tissue; especially new skeletal muscle tissue. The objective of the present study was to determine both the in-situ tetanic contractile response and histomorphologic characteristics of skeletal muscle tissue reconstructed using one of four test articles in a rodent abdominal wall model: 1) porcine small intestinal submucosa (SIS)-ECM; 2) carbodiimide-crosslinked porcine SIS-ECM; 3) autologous tissue; or 4) polypropylene mesh. Six months after surgery, the remodeled SIS-ECM showed almost complete replacement by islands and sheets of skeletal muscle, which generated a similar maximal contractile force to native tissue but with greater resistance to fatigue. The autologous tissue graft was replaced by a mixture of collagenous connective tissue, adipose tissue with fewer islands of skeletal muscle compared to SIS-ECM and a similar fatigue resistance to native muscle. Carbodiimide-crosslinked SIS-ECM and polypropylene mesh were characterized by a chronic inflammatory response and produced little or no measureable tetanic force. The findings of this study show that non-crosslinked xenogeneic SIS scaffolds and autologous tissue are associated with the restoration of functional skeletal muscle with histomorphologic characteristics that resemble native muscle. PMID:20638716

  12. Biosynthesis and biological functions of terpenoids in plants.

    PubMed

    Tholl, Dorothea

    2015-01-01

    Terpenoids (isoprenoids) represent the largest and most diverse class of chemicals among the myriad compounds produced by plants. Plants employ terpenoid metabolites for a variety of basic functions in growth and development but use the majority of terpenoids for more specialized chemical interactions and protection in the abiotic and biotic environment. Traditionally, plant-based terpenoids have been used by humans in the food, pharmaceutical, and chemical industries, and more recently have been exploited in the development of biofuel products. Genomic resources and emerging tools in synthetic biology facilitate the metabolic engineering of high-value terpenoid products in plants and microbes. Moreover, the ecological importance of terpenoids has gained increased attention to develop strategies for sustainable pest control and abiotic stress protection. Together, these efforts require a continuous growth in knowledge of the complex metabolic and molecular regulatory networks in terpenoid biosynthesis. This chapter gives an overview and highlights recent advances in our understanding of the organization, regulation, and diversification of core and specialized terpenoid metabolic pathways, and addresses the most important functions of volatile and nonvolatile terpenoid specialized metabolites in plants.

  13. Applications of the Aurora parallel Prolog system to computational molecular biology

    SciTech Connect

    Lusk, E.L.; Overbeek, R.; Mudambi, S.; Szeredi, P.

    1993-09-01

    We describe an investigation into the use of the Aurora parallel Prolog system in two applications within the area of computational molecular biology. The computational requirements were large, due to the nature of the applications, and were large, due to the nature of the applications, and were carried out on a scalable parallel computer the BBN ``Butterfly`` TC-2000. Results include both a demonstration that logic programming can be effective in the context of demanding applications on large-scale parallel machines, and some insights into parallel programming in Prolog.

  14. Using a Computer Simulation To Teach Science Process Skills to College Biology and Elementary Education Majors.

    ERIC Educational Resources Information Center

    Lee, Aimee T.; Hairston, Rosalina V.; Thames, Rachel; Lawrence, Tonya; Herron, Sherry S.

    2002-01-01

    Describes the Lateblight computer simulation implemented in the general biology laboratory and science methods course for elementary teachers to reinforce the processes of science and allow students to engage, explore, explain, elaborate, and evaluate the methods of building concepts in science. (Author/KHR)

  15. Problem-Solving Inquiry-Oriented Biology Tasks Integrating Practical Laboratory and Computer.

    ERIC Educational Resources Information Center

    Friedler, Yael; And Others

    1992-01-01

    Presents results of a study that examines the development and use of computer simulations for high school science instruction and for integrated laboratory and computerized tests that are part of the biology matriculation examination in Israel. Eleven implications for teaching are presented. (MDH)

  16. Effects of Computer Assisted Instruction (CAI) on Secondary School Students' Performance in Biology

    ERIC Educational Resources Information Center

    Yusuf, Mudasiru Olalere; Afolabi, Adedeji Olufemi

    2010-01-01

    This study investigated the effects of computer assisted instruction (CAI) on secondary school students' performance in biology. Also, the influence of gender on the performance of students exposed to CAI in individualised or cooperative learning settings package was examined. The research was a quasi experimental involving a 3 x 2 factorial…

  17. Effects of Computer-Assisted Instruction on Performance of Senior High School Biology Students in Ghana

    ERIC Educational Resources Information Center

    Owusu, K. A.; Monney, K. A.; Appiah, J. Y.; Wilmot, E. M.

    2010-01-01

    This study investigated the comparative efficiency of computer-assisted instruction (CAI) and conventional teaching method in biology on senior high school students. A science class was selected in each of two randomly selected schools. The pretest-posttest non equivalent quasi experimental design was used. The students in the experimental group…

  18. Effects of Constructivist and Computer-Facilitated Strategies on Achievement in Heterogeneous Secondary Biology.

    ERIC Educational Resources Information Center

    Duffy, Maryellen; Barowy, William

    This paper describes the effects of the implementation of constructivist techniques with interactive computer simulations on conceptual understanding of plant nutrition and critical thinking skills in heterogeneously grouped secondary biology classrooms. The study focused on three strategies for teaching plant nutrition: (1) traditional; (2)…

  19. Computer Labs as Techno-Pedagogical Tools for Learning Biology--Exploring ICT Practices in India

    ERIC Educational Resources Information Center

    Nayark, Ajitha K.; Barker, Miles

    2014-01-01

    In Indian secondary schools, as in many countries, Information and Communication Technologies, ICT, are changing the image of learning places, the roles of teachers and students, and often the entire classroom learning ambience. This study investigates current practices for learning biology in school computer labs in India in the light of the…

  20. PARTNERING WITH DOE TO APPLY ADVANCED BIOLOGICAL, ENVIRONMENTAL, AND COMPUTATIONAL SCIENCE TO ENVIRONMENTAL ISSUES

    EPA Science Inventory

    On February 18, 2004, the U.S. Environmental Protection Agency and Department of Energy signed a Memorandum of Understanding to expand the research collaboration of both agencies to advance biological, environmental, and computational sciences for protecting human health and the ...

  1. Final report for Conference Support Grant "From Computational Biophysics to Systems Biology - CBSB12"

    SciTech Connect

    Hansmann, Ulrich H.E.

    2012-07-02

    This report summarizes the outcome of the international workshop From Computational Biophysics to Systems Biology (CBSB12) which was held June 3-5, 2012, at the University of Tennessee Conference Center in Knoxville, TN, and supported by DOE through the Conference Support Grant 120174. The purpose of CBSB12 was to provide a forum for the interaction between a data-mining interested systems biology community and a simulation and first-principle oriented computational biophysics/biochemistry community. CBSB12 was the sixth in a series of workshops of the same name organized in recent years, and the second that has been held in the USA. As in previous years, it gave researchers from physics, biology, and computer science an opportunity to acquaint each other with current trends in computational biophysics and systems biology, to explore venues of cooperation, and to establish together a detailed understanding of cells at a molecular level. The conference grant of $10,000 was used to cover registration fees and provide travel fellowships to selected students and postdoctoral scientists. By educating graduate students and providing a forum for young scientists to perform research into the working of cells at a molecular level, the workshop adds to DOE's mission of paving the way to exploit the abilities of living systems to capture, store and utilize energy.

  2. The flight telerobotic servicer: From functional architecture to computer architecture

    NASA Technical Reports Server (NTRS)

    Lumia, Ronald; Fiala, John

    1989-01-01

    After a brief tutorial on the NASA/National Bureau of Standards Standard Reference Model for Telerobot Control System Architecture (NASREM) functional architecture, the approach to its implementation is shown. First, interfaces must be defined which are capable of supporting the known algorithms. This is illustrated by considering the interfaces required for the SERVO level of the NASREM functional architecture. After interface definition, the specific computer architecture for the implementation must be determined. This choice is obviously technology dependent. An example illustrating one possible mapping of the NASREM functional architecture to a particular set of computers which implements it is shown. The result of choosing the NASREM functional architecture is that it provides a technology independent paradigm which can be mapped into a technology dependent implementation capable of evolving with technology in the laboratory and in space.

  3. Harnessing Polypharmacology with Computer-Aided Drug Design and Systems Biology.

    PubMed

    Wathieu, Henri; Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2016-01-01

    The ascent of polypharmacology in drug development has many implications for disease therapy, most notably in the efforts of drug discovery, drug repositioning, precision medicine and combination therapy. The single- target approach to drug development has encountered difficulties in predicting drugs that are both clinically efficacious and avoid toxicity. By contrast, polypharmacology offers the possibility of a controlled distribution of effects on a biological system. This review addresses possibilities and bottlenecks in the efficient computational application of polypharmacology. The two major areas we address are the discovery and prediction of multiple protein targets using the tools of computer-aided drug design, and the use of these protein targets in predicting therapeutic potential in the context of biological networks. The successful application of polypharmacology to systems biology and pharmacology has the potential to markedly accelerate the pace of development of novel therapies for multiple diseases, and has implications for the intellectual property landscape, likely requiring targeted changes in patent law. PMID:26907947

  4. Harnessing Polypharmacology with Computer-Aided Drug Design and Systems Biology.

    PubMed

    Wathieu, Henri; Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2016-01-01

    The ascent of polypharmacology in drug development has many implications for disease therapy, most notably in the efforts of drug discovery, drug repositioning, precision medicine and combination therapy. The single- target approach to drug development has encountered difficulties in predicting drugs that are both clinically efficacious and avoid toxicity. By contrast, polypharmacology offers the possibility of a controlled distribution of effects on a biological system. This review addresses possibilities and bottlenecks in the efficient computational application of polypharmacology. The two major areas we address are the discovery and prediction of multiple protein targets using the tools of computer-aided drug design, and the use of these protein targets in predicting therapeutic potential in the context of biological networks. The successful application of polypharmacology to systems biology and pharmacology has the potential to markedly accelerate the pace of development of novel therapies for multiple diseases, and has implications for the intellectual property landscape, likely requiring targeted changes in patent law.

  5. Quantum computing without wavefunctions: time-dependent density functional theory for universal quantum computation.

    PubMed

    Tempel, David G; Aspuru-Guzik, Alán

    2012-01-01

    We prove that the theorems of TDDFT can be extended to a class of qubit Hamiltonians that are universal for quantum computation. The theorems of TDDFT applied to universal Hamiltonians imply that single-qubit expectation values can be used as the basic variables in quantum computation and information theory, rather than wavefunctions. From a practical standpoint this opens the possibility of approximating observables of interest in quantum computations directly in terms of single-qubit quantities (i.e. as density functionals). Additionally, we also demonstrate that TDDFT provides an exact prescription for simulating universal Hamiltonians with other universal Hamiltonians that have different, and possibly easier-to-realize two-qubit interactions. This establishes the foundations of TDDFT for quantum computation and opens the possibility of developing density functionals for use in quantum algorithms.

  6. Quantum Computing Without Wavefunctions: Time-Dependent Density Functional Theory for Universal Quantum Computation

    PubMed Central

    Tempel, David G.; Aspuru-Guzik, Alán

    2012-01-01

    We prove that the theorems of TDDFT can be extended to a class of qubit Hamiltonians that are universal for quantum computation. The theorems of TDDFT applied to universal Hamiltonians imply that single-qubit expectation values can be used as the basic variables in quantum computation and information theory, rather than wavefunctions. From a practical standpoint this opens the possibility of approximating observables of interest in quantum computations directly in terms of single-qubit quantities (i.e. as density functionals). Additionally, we also demonstrate that TDDFT provides an exact prescription for simulating universal Hamiltonians with other universal Hamiltonians that have different, and possibly easier-to-realize two-qubit interactions. This establishes the foundations of TDDFT for quantum computation and opens the possibility of developing density functionals for use in quantum algorithms. PMID:22553483

  7. Shaping Small Bioactive Molecules to Untangle Their Biological Function: A Focus on Fluorescent Plant Hormones.

    PubMed

    Lace, Beatrice; Prandi, Cristina

    2016-08-01

    Modern biology overlaps with chemistry in explaining the structure and function of all cellular processes at the molecular level. Plant hormone research is perfectly located at the interface between these two disciplines, taking advantage of synthetic and computational chemistry as a tool to decipher the complex biological mechanisms regulating the action of plant hormones. These small signaling molecules regulate a wide range of developmental processes, adapting plant growth to ever changing environmental conditions. The synthesis of small bioactive molecules mimicking the activity of endogenous hormones allows us to unveil many molecular features of their functioning, giving rise to a new field, plant chemical biology. In this framework, fluorescence labeling of plant hormones is emerging as a successful strategy to track the fate of these challenging molecules inside living organisms. Thanks to the increasing availability of new fluorescent probes as well as advanced and innovative imaging technologies, we are now in a position to investigate many of the dynamic mechanisms through which plant hormones exert their action. Such a deep and detailed comprehension is mandatory for the development of new green technologies for practical applications. In this review, we summarize the results obtained so far concerning the fluorescent labeling of plant hormones, highlighting the basic steps leading to the design and synthesis of these compelling molecular tools and their applications.

  8. Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?

    PubMed Central

    Sevimoglu, Tuba

    2015-01-01

    Abstract Computational biology and ‘omics’ systems sciences are greatly impacting research on common diseases such as cancer. By contrast, dermatology covering an array of skin diseases with high prevalence in society, has received relatively less attention from ‘omics’ and computational biosciences. We are focusing on psoriasis, a common and debilitating autoimmune disease involving skin and joints. Using computational systems biology and reconstruction, topological, modular, and a novel correlational analyses (based on fold changes) of biological and transcriptional regulatory networks, we analyzed and integrated data from a total of twelve studies from the Gene Expression Omnibus (sample size = 534). Samples represented a comprehensive continuum from lesional and nonlesional skin, as well as bone marrow and dermal mesenchymal stem cells. We identified and propose here a JAK/STAT signaling pathway significant for psoriasis. Importantly, cytokines, interferon-stimulated genes, antimicrobial peptides, among other proteins, were involved in intrinsic parts of the proposed pathway. Several biomarker and therapeutic candidates such as SUB1 are discussed for future experimental studies. The integrative systems biology approach presented here illustrates a comprehensive perspective on the molecular basis of psoriasis. This also attests to the promise of systems biology research in skin diseases, with psoriasis as a systemic component. The present study reports, to the best of our knowledge, the largest set of microarray datasets on psoriasis, to offer new insights into the disease mechanisms with a proposal of a disease pathway. We call for greater computational systems biology research and analyses in dermatology and skin diseases in general. PMID:26480058

  9. Biologically relevant molecular transducer with increased computing power and iterative abilities.

    PubMed

    Ratner, Tamar; Piran, Ron; Jonoska, Natasha; Keinan, Ehud

    2013-05-23

    As computing devices, which process data and interconvert information, transducers can encode new information and use their output for subsequent computing, offering high computational power that may be equivalent to a universal Turing machine. We report on an experimental DNA-based molecular transducer that computes iteratively and produces biologically relevant outputs. As a proof of concept, the transducer accomplished division of numbers by 3. The iterative power was demonstrated by a recursive application on an obtained output. This device reads plasmids as input and processes the information according to a predetermined algorithm, which is represented by molecular software. The device writes new information on the plasmid using hardware that comprises DNA-manipulating enzymes. The computation produces dual output: a quotient, represented by newly encoded DNA, and a remainder, represented by E. coli phenotypes. This device algorithmically manipulates genetic codes. PMID:23706637

  10. Robust Computation of Morse-Smale Complexes of Bilinear Functions

    SciTech Connect

    Norgard, G; Bremer, P T

    2010-11-30

    The Morse-Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, existing algorithms to compute the MS complex are restricted to either piecewise linear or discrete scalar fields. This paper presents a new combinatorial algorithm to compute MS complexes for two dimensional piecewise bilinear functions defined on quadrilateral meshes. We derive a new invariant of the gradient flow within a bilinear cell and use it to develop a provably correct computation which is unaffected by numerical instabilities. This includes a combinatorial algorithm to detect and classify critical points as well as a way to determine the asymptotes of cell-based saddles and their intersection with cell edges. Finally, we introduce a simple data structure to compute and store integral lines on quadrilateral meshes which by construction prevents intersections and enables us to enforce constraints on the gradient flow to preserve known invariants.

  11. A data parallel strategy for aligning multiple biological sequences on multi-core computers.

    PubMed

    Zhu, Xiangyuan; Li, Kenli; Salah, Ahmad

    2013-05-01

    In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request.

  12. SNAP: A computer program for generating symbolic network functions

    NASA Technical Reports Server (NTRS)

    Lin, P. M.; Alderson, G. E.

    1970-01-01

    The computer program SNAP (symbolic network analysis program) generates symbolic network functions for networks containing R, L, and C type elements and all four types of controlled sources. The program is efficient with respect to program storage and execution time. A discussion of the basic algorithms is presented, together with user's and programmer's guides.

  13. An Integrated Bioinformatics and Computational Biology Approach Identifies New BH3-Only Protein Candidates.

    PubMed

    Hawley, Robert G; Chen, Yuzhong; Riz, Irene; Zeng, Chen

    2012-05-01

    In this study, we utilized an integrated bioinformatics and computational biology approach in search of new BH3-only proteins belonging to the BCL2 family of apoptotic regulators. The BH3 (BCL2 homology 3) domain mediates specific binding interactions among various BCL2 family members. It is composed of an amphipathic α-helical region of approximately 13 residues that has only a few amino acids that are highly conserved across all members. Using a generalized motif, we performed a genome-wide search for novel BH3-containing proteins in the NCBI Consensus Coding Sequence (CCDS) database. In addition to known pro-apoptotic BH3-only proteins, 197 proteins were recovered that satisfied the search criteria. These were categorized according to α-helical content and predictive binding to BCL-xL (encoded by BCL2L1) and MCL-1, two representative anti-apoptotic BCL2 family members, using position-specific scoring matrix models. Notably, the list is enriched for proteins associated with autophagy as well as a broad spectrum of cellular stress responses such as endoplasmic reticulum stress, oxidative stress, antiviral defense, and the DNA damage response. Several potential novel BH3-containing proteins are highlighted. In particular, the analysis strongly suggests that the apoptosis inhibitor and DNA damage response regulator, AVEN, which was originally isolated as a BCL-xL-interacting protein, is a functional BH3-only protein representing a distinct subclass of BCL2 family members.

  14. Polymer biomaterial constructs for regenerative medicine and functional biological systems

    NASA Astrophysics Data System (ADS)

    Meng, Linghui

    The use of collagen as a biomaterial is currently undergoing a renaissance in the tissue engineering field. The excellent biocompatibility and safety due to its biological characteristics, such as biodegradability and weak antigenicity, make collagen a primary material resource in medical applications. Described herein is work towards the development of novel collagen-based matrices, with additional multi-functionality imparted through a novel in-situ crosslinking approach. The process of electrospinning has become a widely used technique for the creation of fibrous scaffolds for tissue engineering applications due to its ability to rapidly create structures composed of nano-scale polymer fibers closely resembling the architecture of the extracellular matrix (ECM). Collagen-PCL sheath-core bicomponent fibrous scaffolds were fabricated using a novel variation on traditional electrospinning, known as co-axial electrospinning. The results showed that the addition of a synthetic polymer core into collagen nanofibers remarkably increased the mechanical strength of collagen matrices spun from the benign solvent system. A novel single-step, in-situ collagen crosslink approach was developed in order to solve the problems dominating traditional collagen crosslinking methods, such as dimensional shrinking and loss of porous morphology, and to simplify the crosslinking procedure for electrospun collagen scaffolds. The excess amount of NHS present in the crosslinking mixture was found to delay the EDC/collagen coupling reaction in a controlled fashion. Fundamental investigations into the development and characterization of in-situ crosslinked collagen matrices such as fibrous scaffolds, gels and sponges, as well as their biomedical applications including cell culture substrates, wound dressings, drug delivery matrices and bone regeneration substitutes, were performed. The preliminary mice studies indicated that the in-situ crosslinked collagen matrices could be good candidates

  15. Insect MicroRNAs: Biogenesis, Expression Profiling and Biological Functions

    PubMed Central

    Lucas, Keira; Raikhel, Alexander S.

    2012-01-01

    MicroRNAs (miRNA) are a class of endogenous regulatory RNA molecules 21-24 nucleotides in length that modulate gene expression at the post-transcriptional level via base pairing to target sites within messenger RNAs (mRNA). Typically, the miRNA “seed sequence” (nucleotides 2-8 at the 5′ end) binds complementary seed match sites within the 3′ untranslated region of mRNAs, resulting in either translational inhibition or mRNA degradation. MicroRNAs were first discovered in Caenorhabditis elegans and were shown to be involved in the timed regulation of developmental events. Since their discovery in the 1990s, thousands of potential miRNAs have since been identified in various organisms through small RNA cloning methods and/or computational prediction, and have been shown to play functionally important roles of gene regulation in invertebrates, vertebrates, plants, fungi and viruses. Numerous functions of miRNAs identified in Drosophila melanogaster have demonstrated a great significance of these regulatory molecules. However, elucidation of miRNA roles in non-drosophilid insects presents a challenging and important task. PMID:23165178

  16. Computer program for calculating and fitting thermodynamic functions

    NASA Technical Reports Server (NTRS)

    Mcbride, Bonnie J.; Gordon, Sanford

    1992-01-01

    A computer program is described which (1) calculates thermodynamic functions (heat capacity, enthalpy, entropy, and free energy) for several optional forms of the partition function, (2) fits these functions to empirical equations by means of a least-squares fit, and (3) calculates, as a function of temperture, heats of formation and equilibrium constants. The program provides several methods for calculating ideal gas properties. For monatomic gases, three methods are given which differ in the technique used for truncating the partition function. For diatomic and polyatomic molecules, five methods are given which differ in the corrections to the rigid-rotator harmonic-oscillator approximation. A method for estimating thermodynamic functions for some species is also given.

  17. Computing the hadronic vacuum polarization function by analytic continuation

    DOE PAGES

    Feng, Xu; Hashimoto, Shoji; Hotzel, Grit; Jansen, Karl; Petschlies, Marcus; Renner, Dru B.

    2013-08-29

    We propose a method to compute the hadronic vacuum polarization function on the lattice at continuous values of photon momenta bridging between the spacelike and timelike regions. We provide two independent demonstrations to show that this method leads to the desired hadronic vacuum polarization function in Minkowski spacetime. We present with the example of the leading-order QCD correction to the muon anomalous magnetic moment that this approach can provide a valuable alternative method for calculations of physical quantities where the hadronic vacuum polarization function enters.

  18. Environment parameters and basic functions for floating-point computation

    NASA Technical Reports Server (NTRS)

    Brown, W. S.; Feldman, S. I.

    1978-01-01

    A language-independent proposal for environment parameters and basic functions for floating-point computation is presented. Basic functions are proposed to analyze, synthesize, and scale floating-point numbers. The model provides a small set of parameters and a small set of axioms along with sharp measures of roundoff error. The parameters and functions can be used to write portable and robust codes that deal intimately with the floating-point representation. Subject to underflow and overflow constraints, a number can be scaled by a power of the floating-point radix inexpensively and without loss of precision. A specific representation for FORTRAN is included.

  19. Assessment of cardiac function: magnetic resonance and computed tomography.

    PubMed

    Greenberg, S B

    2000-10-01

    A complete cardiac study requires both anatomic and physiologic evaluation. Cardiac function can be evaluated noninvasively by magnetic resonance imaging (MRI)or ultrafast computed tomography (CT). MRI allows for evaluation of cardiac function by cine gradient echo imaging of the ventricles and flow analysis across cardiac valves and the great vessels. Cine gradient echo imaging is useful for evaluation of cardiac wall motion, ventricular volumes and ventricular mass. Flow analysis allows for measurement of velocity and flow during the cardiac cycle that reflects cardiac function. Ultrafast CT allows for measurement of cardiac indices similar to that provided by gradient echo imaging of the ventricles.

  20. A Survey of Computational Intelligence Techniques in Protein Function Prediction

    PubMed Central

    Tiwari, Arvind Kumar; Srivastava, Rajeev

    2014-01-01

    During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

  1. Lung function, biological monitoring, and biological effect monitoring of gemstone cutters exposed to beryls

    PubMed Central

    Wegner, R.; Heinrich-Ramm, R.; Nowak, D.; Olma, K.; Poschadel, B.; Szadkowski, D.

    2000-01-01

    OBJECTIVES—Gemstone cutters are potentially exposed to various carcinogenic and fibrogenic metals such as chromium, nickel, aluminium, and beryllium, as well as to lead. Increased beryllium concentrations had been reported in the air of workplaces of beryl cutters in Idar-Oberstein, Germany. The aim of the survey was to study the excretion of beryllium in cutters and grinders with occupational exposure to beryls—for example, aquamarines and emeralds—to examine the prevalence of beryllium sensitisation with the beryllium lymphocyte transformation test (BeLT), to examine the prevalence of lung disease induced by beryllium, to describe the internal load of the respective metals relative to work process, and to screen for genotoxic effects in this particular profession.
METHODS—In a cross sectional investigation, 57 out of 100 gemstone cutters working in 12 factories in Idar-Oberstein with occupational exposure to beryls underwent medical examinations, a chest radiograph, lung function testing (spirometry, airway resistance with the interrupter technique), and biological monitoring, including measurements of aluminium, chromium, and nickel in urine as well as lead in blood. Beryllium in urine was measured with a newly developed direct electrothermal atomic absorption spectroscopy technique with a measurement limit of 0.06 µg/l. Also, cytogenetic tests (rates of micronuclei and sister chromatid exchange), and a BeLT were performed. Airborne concentrations of beryllium were measured in three factories. As no adequate local control group was available, the cutters were categorised into those with an exposure to beryls of >4 hours/week (group A) and ⩽4 hours/week (group B).
RESULTS—Clinical, radiological, or spirometric abnormalities indicating pneumoconiosis were detected in none of the gemstone cutters. Metal concentrations in biological material were far below the respective biological limit values, and beryllium in urine was only measurable in

  2. Computing the origin and evolution of the ribosome from its structure — Uncovering processes of macromolecular accretion benefiting synthetic biology

    PubMed Central

    Caetano-Anollés, Gustavo; Caetano-Anollés, Derek

    2015-01-01

    Accretion occurs pervasively in nature at widely different timeframes. The process also manifests in the evolution of macromolecules. Here we review recent computational and structural biology studies of evolutionary accretion that make use of the ideographic (historical, retrodictive) and nomothetic (universal, predictive) scientific frameworks. Computational studies uncover explicit timelines of accretion of structural parts in molecular repertoires and molecules. Phylogenetic trees of protein structural domains and proteomes and their molecular functions were built from a genomic census of millions of encoded proteins and associated terminal Gene Ontology terms. Trees reveal a ‘metabolic-first’ origin of proteins, the late development of translation, and a patchwork distribution of proteins in biological networks mediated by molecular recruitment. Similarly, the natural history of ancient RNA molecules inferred from trees of molecular substructures built from a census of molecular features shows patchwork-like accretion patterns. Ideographic analyses of ribosomal history uncover the early appearance of structures supporting mRNA decoding and tRNA translocation, the coevolution of ribosomal proteins and RNA, and a first evolutionary transition that brings ribosomal subunits together into a processive protein biosynthetic complex. Nomothetic structural biology studies of tertiary interactions and ancient insertions in rRNA complement these findings, once concentric layering assumptions are removed. Patterns of coaxial helical stacking reveal a frustrated dynamics of outward and inward ribosomal growth possibly mediated by structural grafting. The early rise of the ribosomal ‘turnstile’ suggests an evolutionary transition in natural biological computation. Results make explicit the need to understand processes of molecular growth and information transfer of macromolecules. PMID:27096056

  3. A primer on molecular biology for imagers: III. Proteins: structure and function.

    PubMed

    Pandit, Sunil D; Li, King C P

    2004-04-01

    This article along with the first 2 in this series (4,12) completes the discussion on the key molecules and process inside the cell namely, DNA, RNA, and proteins. These 3 articles provide a very basic foundation for understanding molecular biology concepts and summarize some of the work of numerous scientists over the past century. We understand these processes far better now than we did in the past, but clearly this knowledge is by no means complete and a number of basic scientists are working hard to elucidate and understand the fundamental mechanisms that operate within a cell. Genes and gene products work with each other in complex, interconnected pathways, and in perfect harmony to make a functional cell, tissue, and an organism as a whole. There is a lot of cross-talk that happens between different proteins that interact with various other proteins, DNA, and RNA to establish pathways, networks, and molecular systems as a team working to perfection. The past 15 years have seen the rapid development of systems biology approaches. We live in an era that emphasizes multi-disciplinary, cross-functional teams to perform science rather than individual researchers working on the bench on a very specific problem. Global approaches have become more common and the amount of data generated must be managed by trained bioinformatics personnel and large computers. In our subsequent articles, we will discuss these global approaches and the areas of genomics, functional genomics, and proteomics that have revolutionized the way we perform science. PMID:15109017

  4. A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

    NASA Astrophysics Data System (ADS)

    Shekofteh, Yasser; Jafari, Sajad; Sprott, Julien Clinton; Hashemi Golpayegani, S. Mohammad Reza; Almasganj, Farshad

    2015-02-01

    As we know, many biological systems such as neurons or the heart can exhibit chaotic behavior. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. In this paper, a novel cost function is proposed to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian mixture model (GMM) which is fitted to the observed attractor generated by the real system. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. We have applied the proposed method to the parameter estimation of two important biological systems, a neuron and a cardiac pacemaker, which show chaotic behavior. Some simulated experiments are given to verify the usefulness of the proposed approach in clean and noisy conditions. The results show the adequacy of the proposed cost function.

  5. Integrated command, control, communications and computation system functional architecture

    NASA Technical Reports Server (NTRS)

    Cooley, C. G.; Gilbert, L. E.

    1981-01-01

    The functional architecture for an integrated command, control, communications, and computation system applicable to the command and control portion of the NASA End-to-End Data. System is described including the downlink data processing and analysis functions required to support the uplink processes. The functional architecture is composed of four elements: (1) the functional hierarchy which provides the decomposition and allocation of the command and control functions to the system elements; (2) the key system features which summarize the major system capabilities; (3) the operational activity threads which illustrate the interrelationahip between the system elements; and (4) the interfaces which illustrate those elements that originate or generate data and those elements that use the data. The interfaces also provide a description of the data and the data utilization and access techniques.

  6. Computational systems biology in drug discovery and development: methods and applications.

    PubMed

    Materi, Wayne; Wishart, David S

    2007-04-01

    Computational systems biology is an emerging field in biological simulation that attempts to model or simulate intra- and intercellular events using data gathered from genomic, proteomic or metabolomic experiments. The need to model complex temporal and spatiotemporal processes at many different scales has led to the emergence of numerous techniques, including systems of differential equations, Petri nets, cellular automata simulators, agent-based models and pi calculus. This review provides a brief summary and an assessment of most of these approaches. It also provides examples of how these methods are being used to facilitate drug discovery and development.

  7. Phytochrome from Green Plants: Properties and biological Function

    SciTech Connect

    Quail, Peter H.

    2014-07-25

    Pfr conformer reverses this activity upon initial light exposure, inducing the switch to photomorphogenic development. This reversal involves light-triggered translocation of the photoactivated phy molecule into the nucleus where it interacts with PIF-family members, inducing rapid phosphorylation and degradation of the PIFs via the ubiquitin-proteasome system. This degradation in turn elicits rapid alterations in gene expression that drive the deetiolation transition. This project has made considerable progress in defining phy-PIF signaling activity in controlling the SAR. The biological functions of the multiple PIF-family members in controlling the SAR, including dissection of the relative contributions of the individual PIFs to this process, as well as to diurnal growth-control oscillations, have been investigated using higher-order pif-mutant combinations. Using microarray analysis of a quadruple pif mutant we have defined the shade-induced, PIF-regulated transcriptional network genome-wide. This has revealed that a dynamic antagonism between the phys and PIFs generates selective reciprocal responses during deetiolation and the SAR in a rapidly light-responsive transcriptional network. Using integrated RNA-seq and ChIP-seq analysis of higher order pif-mutant combinations, we have defined the direct gene-targets of PIF transcriptional regulation, and have obtained evidence that this regulation involves differential direct targeting of rapidly light-responsive genes by the individual PIF-family members. This project has provided significant advances in our understanding of the molecular mechanisms by which the phy-PIF photosensory signaling pathway regulates an important bioenergy-related plant response to the light environment. The identification of molecular targets in the primary transcriptional-regulatory circuitry of this pathway has the potential to enable genetic or reverse-genetic manipulation of the partitioning of carbon between reproductive and

  8. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    NASA Technical Reports Server (NTRS)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  9. Seeing Is Believing: Quantifying Is Convincing: Computational Image Analysis in Biology.

    PubMed

    Sbalzarini, Ivo F

    2016-01-01

    Imaging is center stage in biology. Advances in microscopy and labeling techniques have enabled unprecedented observations and continue to inspire new developments. Efficient and accurate quantification and computational analysis of the acquired images, however, are becoming the bottleneck. We review different paradigms of computational image analysis for intracellular, single-cell, and tissue-level imaging, providing pointers to the specialized literature and listing available software tools. We place particular emphasis on clear categorization of image-analysis frameworks and on identifying current trends and challenges in the field. We further outline some of the methodological advances that are required in order to use images as quantitative scientific measurements. PMID:27207361

  10. A biologically consistent hierarchical framework for self-referencing survivalist computation

    NASA Astrophysics Data System (ADS)

    Cottam, Ron; Ranson, Willy; Vounckx, Roger

    2000-05-01

    Extensively scaled formally rational hardware and software are indirectly fallible, at the very least through temporal restrictions on the evaluation of their correctness. In addition, the apparent inability of formal rationality to successfully describe living systems as anything other than inanimate structures suggests that the development of self-referencing computational machines will require a different approach. There is currently a strong movement towards the adoption of semiotics as a descriptive medium in theoretical biology. We present a related computational semiosic construction (1, 2) consistent with evolutionary hierarchical emergence (3), which may serve as a framework for implementing anticipatory-oriented survivalist processing in real environments.

  11. Interdisciplinary research and education at the biology-engineering-computer science interface: a perspective.

    PubMed

    Tadmor, Brigitta; Tidor, Bruce

    2005-09-01

    Progress in the life sciences, including genome sequencing and high-throughput experimentation, offers an opportunity for understanding biology and medicine from a systems perspective. This 'new view', which complements the more traditional component-based approach, involves the integration of biological research with approaches from engineering disciplines and computer science. The result is more than a new set of technologies. Rather, it promises a fundamental reconceptualization of the life sciences based on the development of quantitative and predictive models to describe crucial processes. To achieve this change, learning communities are being formed at the interface of the life sciences, engineering and computer science. Through these communities, research and education will be integrated across disciplines and the challenges associated with multidisciplinary team-based science will be addressed.

  12. Computer Code For Calculation Of The Mutual Coherence Function

    NASA Astrophysics Data System (ADS)

    Bugnolo, Dimitri S.

    1986-05-01

    We present a computer code in FORTRAN 77 for the calculation of the mutual coherence function (MCF) of a plane wave normally incident on a stochastic half-space. This is an exact result. The user need only input the path length, the wavelength, the outer scale size, and the structure constant. This program may be used to calculate the MCF of a well-collimated laser beam in the atmosphere.

  13. Computations involving differential operators and their actions on functions

    NASA Technical Reports Server (NTRS)

    Crouch, Peter E.; Grossman, Robert; Larson, Richard

    1991-01-01

    The algorithms derived by Grossmann and Larson (1989) are further developed for rewriting expressions involving differential operators. The differential operators involved arise in the local analysis of nonlinear dynamical systems. These algorithms are extended in two different directions: the algorithms are generalized so that they apply to differential operators on groups and the data structures and algorithms are developed to compute symbolically the action of differential operators on functions. Both of these generalizations are needed for applications.

  14. Efficient quantum algorithm for computing n-time correlation functions.

    PubMed

    Pedernales, J S; Di Candia, R; Egusquiza, I L; Casanova, J; Solano, E

    2014-07-11

    We propose a method for computing n-time correlation functions of arbitrary spinorial, fermionic, and bosonic operators, consisting of an efficient quantum algorithm that encodes these correlations in an initially added ancillary qubit for probe and control tasks. For spinorial and fermionic systems, the reconstruction of arbitrary n-time correlation functions requires the measurement of two ancilla observables, while for bosonic variables time derivatives of the same observables are needed. Finally, we provide examples applicable to different quantum platforms in the frame of the linear response theory.

  15. Computational studies of the purine-functionalized graphene sheets

    NASA Astrophysics Data System (ADS)

    Mirzaei, Mahmoud; Yousefi, Mohammad

    2012-10-01

    We performed a computational work to investigate the properties of functionalized graphene sheets (S) by adenine (A) and guanine (G) purine nucleobases. To achieve the purpose of this work, we examined the functionalization of armchair and zigzag tips of the S model by each of the A and G purines. The results indicated that the optimized properties for the investigated hybrid structures are different depending on the tip of functionalization and the used purine nucleobase. Moreover, the atomic level properties of the investigated structures were investigated by evaluating quadrupole coupling constants (CQ) for the atoms of the optimized structures. The remarkable trend of the CQ parameters is that the changes of atomic properties are many more significant for the functionalization of the zigzag-tip by the G nucleobase, which is in agreement with the results of the optimized properties.

  16. Large Scale Computing and Storage Requirements for Biological and Environmental Research

    SciTech Connect

    DOE Office of Science, Biological and Environmental Research Program Office ,

    2009-09-30

    In May 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of Biological and Environmental Research (BER) held a workshop to characterize HPC requirements for BER-funded research over the subsequent three to five years. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. Chief among them: scientific progress in BER-funded research is limited by current allocations of computational resources. Additionally, growth in mission-critical computing -- combined with new requirements for collaborative data manipulation and analysis -- will demand ever increasing computing, storage, network, visualization, reliability and service richness from NERSC. This report expands upon these key points and adds others. It also presents a number of"case studies" as significant representative samples of the needs of science teams within BER. Workshop participants were asked to codify their requirements in this"case study" format, summarizing their science goals, methods of solution, current and 3-5 year computing requirements, and special software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel,"multi-core" environment that is expected to dominate HPC architectures over the next few years.

  17. Computer literacy for life sciences: helping the digital-era biology undergraduates face today's research.

    PubMed

    Smolinski, Tomasz G

    2010-01-01

    Computer literacy plays a critical role in today's life sciences research. Without the ability to use computers to efficiently manipulate and analyze large amounts of data resulting from biological experiments and simulations, many of the pressing questions in the life sciences could not be answered. Today's undergraduates, despite the ubiquity of computers in their lives, seem to be largely unfamiliar with how computers are being used to pursue and answer such questions. This article describes an innovative undergraduate-level course, titled Computer Literacy for Life Sciences, that aims to teach students the basics of a computerized scientific research pursuit. The purpose of the course is for students to develop a hands-on working experience in using standard computer software tools as well as computer techniques and methodologies used in life sciences research. This paper provides a detailed description of the didactical tools and assessment methods used in and outside of the classroom as well as a discussion of the lessons learned during the first installment of the course taught at Emory University in fall semester 2009.

  18. Computational aspects of the continuum quaternionic wave functions for hydrogen

    SciTech Connect

    Morais, J.

    2014-10-15

    Over the past few years considerable attention has been given to the role played by the Hydrogen Continuum Wave Functions (HCWFs) in quantum theory. The HCWFs arise via the method of separation of variables for the time-independent Schrödinger equation in spherical coordinates. The HCWFs are composed of products of a radial part involving associated Laguerre polynomials multiplied by exponential factors and an angular part that is the spherical harmonics. In the present paper we introduce the continuum wave functions for hydrogen within quaternionic analysis ((R)QHCWFs), a result which is not available in the existing literature. In particular, the underlying functions are of three real variables and take on either values in the reduced and full quaternions (identified, respectively, with R{sup 3} and R{sup 4}). We prove that the (R)QHCWFs are orthonormal to one another. The representation of these functions in terms of the HCWFs are explicitly given, from which several recurrence formulae for fast computer implementations can be derived. A summary of fundamental properties and further computation of the hydrogen-like atom transforms of the (R)QHCWFs are also discussed. We address all the above and explore some basic facts of the arising quaternionic function theory. As an application, we provide the reader with plot simulations that demonstrate the effectiveness of our approach. (R)QHCWFs are new in the literature and have some consequences that are now under investigation.

  19. Proceedings of the Second Annual Conference of the MidSouth Computational Biology and Bioinformatics Society

    PubMed Central

    Wren, Jonathan D; Slikker, William

    2005-01-01

    The MCBIOS 2004 conference brought together regional researchers and students in biology, computer science and bioinformatics on October 7th-9th 2004 to present their latest work. This editorial describes the conference itself and introduces the twelve peer-reviewed manuscripts accepted for publication in the Proceedings of the MCBIOS 2004 Conference. These manuscripts included new methods for analysis of high-throughput gene expression experiments, EST clustering, analysis of mass spectrometry data and genomic analysis PMID:16026594

  20. A hybrid method for the parallel computation of Green's functions

    SciTech Connect

    Petersen, Dan Erik; Li Song; Stokbro, Kurt; Sorensen, Hans Henrik B.; Hansen, Per Christian; Skelboe, Stig; Darve, Eric

    2009-08-01

    Quantum transport models for nanodevices using the non-equilibrium Green's function method require the repeated calculation of the block tridiagonal part of the Green's and lesser Green's function matrices. This problem is related to the calculation of the inverse of a sparse matrix. Because of the large number of times this calculation needs to be performed, this is computationally very expensive even on supercomputers. The classical approach is based on recurrence formulas which cannot be efficiently parallelized. This practically prevents the solution of large problems with hundreds of thousands of atoms. We propose new recurrences for a general class of sparse matrices to calculate Green's and lesser Green's function matrices which extend formulas derived by Takahashi and others. We show that these recurrences may lead to a dramatically reduced computational cost because they only require computing a small number of entries of the inverse matrix. Then, we propose a parallelization strategy for block tridiagonal matrices which involves a combination of Schur complement calculations and cyclic reduction. It achieves good scalability even on problems of modest size.

  1. A radial basis function network approach for the computation of inverse continuous time variant functions.

    PubMed

    Mayorga, René V; Carrera, Jonathan

    2007-06-01

    This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.

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

    PubMed

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

    2013-07-01

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

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

    PubMed Central

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

    2013-01-01

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

  4. Discovery of biological networks from diverse functional genomic data

    PubMed Central

    Myers, Chad L; Robson, Drew; Wible, Adam; Hibbs, Matthew A; Chiriac, Camelia; Theesfeld, Chandra L; Dolinski, Kara; Troyanskaya, Olga G

    2005-01-01

    We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web. PMID:16420673

  5. COMPUTER-AIDED DRUG DISCOVERY AND DEVELOPMENT (CADDD): in silico-chemico-biological approach

    PubMed Central

    Kapetanovic, I.M.

    2008-01-01

    It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3-D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve. PMID:17229415

  6. Application of comparative biology in GO functional annotation: the mouse model.

    PubMed

    Drabkin, Harold J; Christie, Karen R; Dolan, Mary E; Hill, David P; Ni, Li; Sitnikov, Dmitry; Blake, Judith A

    2015-10-01

    The Gene Ontology (GO) is an important component of modern biological knowledge representation with great utility for computational analysis of genomic and genetic data. The Gene Ontology Consortium (GOC) consists of a large team of contributors including curation teams from most model organism database groups as well as curation teams focused on representation of data relevant to specific human diseases. Key to the generation of consistent and comprehensive annotations is the development and use of shared standards and measures of curation quality. The GOC engages all contributors to work to a defined standard of curation that is presented here in the context of annotation of genes in the laboratory mouse. Comprehensive understanding of the origin, epistemology, and coverage of GO annotations is essential for most effective use of GO resources. Here the application of comparative approaches to capturing functional data in the mouse system is described. PMID:26141960

  7. Distinguishing Pattern Formation Phenotypes: Applying Minkowski Functionals to Cell Biology Systems

    NASA Astrophysics Data System (ADS)

    Rericha, Erin; Guven, Can; Parent, Carole; Losert, Wolfgang

    2011-03-01

    Spatial Clustering of proteins within cells or cells themselves frequently occur in cell biology systems. However quantifying the underlying order and determining the regulators of these cluster patterns have proved difficult due to the inherent high noise levels in the systems. For instance the patterns formed by wild type and cyclic-AMP regulatory mutant Dictyostelium cells are visually distinctive, yet the large error bars in measurements of the fractal number, area, Euler number, eccentricity, and wavelength making it difficult to quantitatively distinguish between the patterns. We apply a spatial analysis technique based on Minkowski functionals and develop metrics which clearly separate wild type and mutant cell lines into distinct categories. Having such a metric facilitated the development of a computational model for cellular aggregation and its regulators. Supported by NIH-NGHS Nanotechnology (R01GM085574) and the Burroughs Wellcome Fund.

  8. Ab Initio Calculations of the Electronic Structures and Biological Functions of Protein Molecules

    NASA Astrophysics Data System (ADS)

    Zheng, Haoping

    2003-04-01

    The self-consistent cluster-embedding (SCCE) calculation method reduces the computational effort from M3 to about M1 (M is the number of atoms in the system) with unchanged calculation precision. So the ab initio, all-electron calculation of the electronic structure and biological function of protein molecule becomes a reality, which will promote new proteomics considerably. The calculated results of two real protein molecules, the trypsin inhibitor from the seeds of squash Cucurbita maxima (CMTI-I, 436 atoms) and the Ascaris trypsin inhibitor (912 atoms, two three-dimensional structures), are presented. The reactive sites of the inhibitors are determined and explained. The precision of structure determination of inhibitors are tested theoretically.

  9. Ab Initio Calculations of the Electronic Structures and Biological Functions of Protein Molecules

    NASA Astrophysics Data System (ADS)

    Zheng, Haoping

    The self-consistent cluster-embedding (SCCE) calculation method reduces the computational effort from M3 to about M1 (M is the number of atoms in the system) with precise calculations. Thus the ab initio, all-electron calculation of the electronic structure and biological function of protein molecule has become a reality, which will promote new proteomics considerably. The calculated results of two real protein molecules, the trypsin inhibitor from the seeds of squash Cucurbita maxima (CMTI-I, 436 atoms) and the ascaris trypsin inhibitor (912 atoms, two three-dimensional structures), will be presented in this paper. The reactive sites of the inhibitors are determined and explained. The accuracy of structure determination of the inhibitors are tested theoretically.

  10. Green's Function Analysis of Periodic Structures in Computational Electromagnetics

    NASA Astrophysics Data System (ADS)

    Van Orden, Derek

    2011-12-01

    Periodic structures are used widely in electromagnetic devices, including filters, waveguiding structures, and antennas. Their electromagnetic properties may be analyzed computationally by solving an integral equation, in which an unknown equivalent current distribution in a single unit cell is convolved with a periodic Green's function that accounts for the system's boundary conditions. Fast computation of the periodic Green's function is therefore essential to achieve high accuracy solutions of complicated periodic structures, including analysis of modal wave propagation and scattering from external sources. This dissertation first presents alternative spectral representations of the periodic Green's function of the Helmholtz equation for cases of linear periodic systems in 2D and 3D free space and near planarly layered media. Although there exist multiple representations of the periodic Green's function, most are not efficient in the important case where the fields are observed near the array axis. We present spectral-spatial representations for rapid calculation of the periodic Green's functions for linear periodic arrays of current sources residing in free space as well as near a planarly layered medium. They are based on the integral expansion of the periodic Green's functions in terms of the spectral parameters transverse to the array axis. These schemes are important for the rapid computation of the interaction among unit cells of a periodic array, and, by extension, the complex dispersion relations of guided waves. Extensions of this approach to planar periodic structures are discussed. With these computation tools established, we study the traveling wave properties of linear resonant arrays placed near surfaces, and examine the coupling mechanisms that lead to radiation into guided waves supported by the surface. This behavior is especially important to understand the properties of periodic structures printed on dielectric substrates, such as periodic

  11. Path-Integration Computation of the Transport Properties of Polymers Nanoparticles and Complex Biological Structures

    NASA Astrophysics Data System (ADS)

    Douglas, Jack

    2014-03-01

    One of the things that puzzled me when I was a PhD student working under Karl Freed was the curious unity between the theoretical descriptions of excluded volume interactions in polymers, the hydrodynamic properties of polymers in solution, and the critical properties of fluid mixtures, gases and diverse other materials (magnets, superfluids,etc.) when these problems were formally expressed in terms of Wiener path integration and the interactions treated through a combination of epsilon expansion and renormalization group (RG) theory. It seemed that only the interaction labels changed from one problem to the other. What do these problems have in common? Essential clues to these interrelations became apparent when Karl Freed, myself and Shi-Qing Wang together began to study polymers interacting with hyper-surfaces of continuously variable dimension where the Feynman perturbation expansions could be performed through infinite order so that we could really understand what the RG theory was doing. It is evidently simply a particular method for resuming perturbation theory, and former ambiguities no longer existed. An integral equation extension of this type of exact calculation to ``surfaces'' of arbitrary fixed shape finally revealed the central mathematical object that links these diverse physical models- the capacity of polymer chains, whose value vanishes at the critical dimension of 4 and whose magnitude is linked to the friction coefficient of polymer chains, the virial coefficient of polymers and the 4-point function of the phi-4 field theory,...Once this central object was recognized, it then became possible solve diverse problems in material science through the calculation of capacity, and related ``virials'' properties, through Monte Carlo sampling of random walk paths. The essential ideas of this computational method are discussed and some applications given to non-trivial problems: nanotubes treated as either rigid rods or ensembles worm-like chains having

  12. Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach.

    PubMed

    Muhammad, Syed Aun; Ahmed, Safia; Ali, Amjad; Huang, Hui; Wu, Xiaogang; Yang, X Frank; Naz, Anam; Chen, Jake

    2014-07-01

    A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the

  13. Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing.

    PubMed

    Buganza Tepole, A; Kuhl, E

    2016-01-01

    Wound healing is a synchronized cascade of chemical, biological, and mechanical phenomena, which act in concert to restore the damaged tissue. An imbalance between these events can induce painful scarring. Despite intense efforts to decipher the mechanisms of wound healing, the role of mechanics remains poorly understood. Here, we establish a computational systems biology model to identify the chemical, biological, and mechanical mechanisms of scar formation. First, we introduce the generic problem of coupled chemo-bio-mechanics. Then, we introduce the model problem of wound healing in terms of a particular chemical signal, inflammation, a particular biological cell type, fibroblasts, and a particular mechanical model, isotropic hyperelasticity. We explore the cross-talk between chemical, biological, and mechanical signals and show that all three fields have a significant impact on scar formation. Our model is the first step toward rigorous multiscale, multifield modeling in wound healing. Our formulation has the potential to improve effective wound management and optimize treatment on an individualized patient-specific basis.

  14. Structural and Computational Biology in the Design of Immunogenic Vaccine Antigens.

    PubMed

    Liljeroos, Lassi; Malito, Enrico; Ferlenghi, Ilaria; Bottomley, Matthew James

    2015-01-01

    Vaccination is historically one of the most important medical interventions for the prevention of infectious disease. Previously, vaccines were typically made of rather crude mixtures of inactivated or attenuated causative agents. However, over the last 10-20 years, several important technological and computational advances have enabled major progress in the discovery and design of potently immunogenic recombinant protein vaccine antigens. Here we discuss three key breakthrough approaches that have potentiated structural and computational vaccine design. Firstly, genomic sciences gave birth to the field of reverse vaccinology, which has enabled the rapid computational identification of potential vaccine antigens. Secondly, major advances in structural biology, experimental epitope mapping, and computational epitope prediction have yielded molecular insights into the immunogenic determinants defining protective antigens, enabling their rational optimization. Thirdly, and most recently, computational approaches have been used to convert this wealth of structural and immunological information into the design of improved vaccine antigens. This review aims to illustrate the growing power of combining sequencing, structural and computational approaches, and we discuss how this may drive the design of novel immunogens suitable for future vaccines urgently needed to increase the global prevention of infectious disease.

  15. Structural and Computational Biology in the Design of Immunogenic Vaccine Antigens

    PubMed Central

    Liljeroos, Lassi; Malito, Enrico; Ferlenghi, Ilaria; Bottomley, Matthew James

    2015-01-01

    Vaccination is historically one of the most important medical interventions for the prevention of infectious disease. Previously, vaccines were typically made of rather crude mixtures of inactivated or attenuated causative agents. However, over the last 10–20 years, several important technological and computational advances have enabled major progress in the discovery and design of potently immunogenic recombinant protein vaccine antigens. Here we discuss three key breakthrough approaches that have potentiated structural and computational vaccine design. Firstly, genomic sciences gave birth to the field of reverse vaccinology, which has enabled the rapid computational identification of potential vaccine antigens. Secondly, major advances in structural biology, experimental epitope mapping, and computational epitope prediction have yielded molecular insights into the immunogenic determinants defining protective antigens, enabling their rational optimization. Thirdly, and most recently, computational approaches have been used to convert this wealth of structural and immunological information into the design of improved vaccine antigens. This review aims to illustrate the growing power of combining sequencing, structural and computational approaches, and we discuss how this may drive the design of novel immunogens suitable for future vaccines urgently needed to increase the global prevention of infectious disease. PMID:26526043

  16. On the Hydrodynamic Function of Sharkskin: A Computational Investigation

    NASA Astrophysics Data System (ADS)

    Boomsma, Aaron; Sotiropoulos, Fotis

    2014-11-01

    Denticles (placoid scales) are small structures that cover the epidermis of some sharks. The hydrodynamic function of denticles is unclear. Because they resemble riblets, they have been thought to passively reduce skin-friction-for which there is some experimental evidence. Others have experimentally shown that denticles increase skin-friction and have hypothesized that denticles act as vortex generators to delay separation. To help clarify their function, we use high-resolution large eddy and direct numerical simulations, with an immersed boundary method, to simulate flow patterns past and calculate the drag force on Mako Short Fin denticles. Simulations are carried out for the denticles placed in a canonical turbulent boundary layer as well as in the vicinity of a separation bubble. The computed results elucidate the three-dimensional structure of the flow around denticles and provide insights into the hydrodynamic function of sharkskin.

  17. Teacher Perceptions of the Integration of Laptop Computers in Their High School Biology Classrooms

    NASA Astrophysics Data System (ADS)

    Gundy, Morag S.

    2011-12-01

    Studies indicate that teachers, and in particular science teachers in the senior high school grades, do not integrate laptop computers into their instruction to the extent anticipated by researchers. This technology has not spread easily to other teachers even with improved access to hardware and software, increased support, and a paradigm shift from teacher-centred to student-centred education. Although a number of studies have focused on the issues and problems related to the integration of laptops in classroom instruction, these studies, largely quantitative in nature, have tended to bypass the role teachers play in integrating laptop computers into their instruction. This thesis documents and describes the role of Ontario high school science teachers in the integration of laptop computers in the classroom. Ten teachers who have successfully integrated laptop computers into their biology courses participated in this descriptive study. Their perceptions of implementing laptops into their biology courses, key factors about the implementation process, and how the implementation was accomplished are examined. The study also identifies the conditions which they feel would allow this innovation to be implemented by other teachers. Key findings of the study indicate that teachers must initiate, implement and sustain an emergent and still evolving innovation; teacher perceptions change and continue to change with increased experience using laptops in the science classroom; changes in teaching approaches are significant as a result of the introduction of laptop technology; and, the teachers considered the acquisition and use of new teaching materials to be an important aspect of integrating laptop computers into instruction. Ongoing challenges for appropriate professional development, sharing of knowledge, skills and teaching materials are identified. The study provides a body of practical knowledge for biology teachers who are considering the integration of laptops into

  18. Multiple von Neumann computers: an evolutionary approach to functional emergence.

    PubMed

    Suzuki, H

    1997-01-01

    A novel system composed of multiple von Neumann computers and an appropriate problem environment is proposed and simulated. Each computer has a memory to store the machine instruction program, and when a program is executed, a series of machine codes in the memory is sequentially decoded, leading to register operations in the central processing unit (CPU). By means of these operations, the computer not only can handle its generally used registers but also can read and write the environmental database. Simulation is driven by genetic algorithms (GAs) performed on the population of program memories. Mutation and crossover create program diversity in the memory, and selection facilitates the reproduction of appropriate programs. Through these evolutionary operations, advantageous combinations of machine codes are created and fixed in the population one by one, and the higher function, which enables the computer to calculate an appropriate number from the environment, finally emerges in the program memory. In the latter half of the article, the performance of GAs on this system is studied. Under different sets of parameters, the evolutionary speed, which is determined by the time until the domination of the final program, is examined and the conditions for faster evolution are clarified. At an intermediate mutation rate and at an intermediate population size, crossover helps create novel advantageous sets of machine codes and evidently accelerates optimization by GAs.

  19. 21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow...

  20. 21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow...

  1. 21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow...

  2. 21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow...

  3. 21 CFR 870.1435 - Single-function, preprogrammed diagnostic computer.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Single-function, preprogrammed diagnostic computer... Single-function, preprogrammed diagnostic computer. (a) Identification. A single-function, preprogrammed diagnostic computer is a hard-wired computer that calculates a specific physiological or blood-flow...

  4. Computer Modeling of the Earliest Cellular Structures and Functions

    NASA Technical Reports Server (NTRS)

    Pohorille, Andrew; Chipot, Christophe; Schweighofer, Karl

    2000-01-01

    In the absence of extinct or extant record of protocells (the earliest ancestors of contemporary cells). the most direct way to test our understanding of the origin of cellular life is to construct laboratory models of protocells. Such efforts are currently underway in the NASA Astrobiology Program. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs for molecules that perform proto-cellular functions. Many of these functions, such as import of nutrients, capture and storage of energy. and response to changes in the environment are carried out by proteins bound to membrane< We will discuss a series of large-scale, molecular-level computer simulations which demonstrate (a) how small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes, (b) how these peptides aggregate to form membrane-spanning structures (eg. channels), and (c) by what mechanisms such aggregates perform essential proto-cellular functions, such as proton transport of protons across cell walls, a key step in cellular bioenergetics. The simulations were performed using the molecular dynamics method, in which Newton's equations of motion for each item in the system are solved iteratively. The problems of interest required simulations on multi-nanosecond time scales, which corresponded to 10(exp 6)-10(exp 8) time steps.

  5. Biogenesis and biological function of marine algal oxylipins.

    PubMed

    Gerwick, W H; Roberts, M A; Vulpanovici, A; Ballantine, D L

    1999-01-01

    The biogenetic source of most marine algal oxylipins, which are many and of diverse structure, can logically be unified through a common lipoxygenase-derived hydroperoxide to epoxy allylic carbocation transformation. The biological role of oxylipins in algae remains an enigma, although numerous ideas have been put forth. Herein, we hypothesize and provide some evidence for an osmoregulatory role for these metabolites.

  6. Oxidative metabolites of lycopene and their biological functions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    To gain a better understanding of the beneficial biological activities of lycopene on cancer prevention, a greater knowledge of the metabolism of lycopene is needed. In particular, the identification of lycopene metabolites and oxidation products in vivo; the importance of tissue specific lycopene c...

  7. Heterogeneous processes at the intersection of chemistry and biology: A computational approach

    SciTech Connect

    Kuo, I W; Mundy, C J

    2008-02-11

    Heterogeneous processes hold the key to understanding many problems in biology and atmospheric science. In particular, recent experiments have shown that heterogeneous chemistry at the surface of sea-salt aerosols plays a large role in important atmospheric processes with far reaching implications towards understanding of the fate and transport of aerosolized chemical weapons (i.e. organophosphates such as sarin and VX). Unfortunately, the precise mechanistic details of the simplest surface enhanced chemical reactions remain unknown. Understanding heterogeneous processes also has implications in the biological sciences. Traditionally, it is accepted that enzymes catalyze reactions by stabilizing the transition state, thereby lowering the free energy barrier. However, recent findings have shown that a multitude of phenomena likely contribute to the efficiency of enzymes, such as coupled protein motion, quantum mechanical tunneling, or strong electrostatic binding. The objective of this project was to develop and validate a single computational framework based on first principles simulations using tera-scale computational resources to answer fundamental scientific questions about heterogeneous chemical processes relevant to atmospheric chemistry and biological sciences.

  8. Motion as a source of environmental information: a fresh view on biological motion computation by insect brains.

    PubMed

    Egelhaaf, Martin; Kern, Roland; Lindemann, Jens Peter

    2014-01-01

    Despite their miniature brains insects, such as flies, bees and wasps, are able to navigate by highly erobatic flight maneuvers in cluttered environments. They rely on spatial information that is contained in the retinal motion patterns induced on the eyes while moving around ("optic flow") to accomplish their extraordinary performance. Thereby, they employ an active flight and gaze strategy that separates rapid saccade-like turns from translatory flight phases where the gaze direction is kept largely constant. This behavioral strategy facilitates the processing of environmental information, because information about the distance of the animal to objects in the environment is only contained in the optic flow generated by translatory motion. However, motion detectors as are widespread in biological systems do not represent veridically the velocity of the optic flow vectors, but also reflect textural information about the environment. This characteristic has often been regarded as a limitation of a biological motion detection mechanism. In contrast, we conclude from analyses challenging insect movement detectors with image flow as generated during translatory locomotion through cluttered natural environments that this mechanism represents the contours of nearby objects. Contrast borders are a main carrier of functionally relevant object information in artificial and natural sceneries. The motion detection system thus segregates in a computationally parsimonious way the environment into behaviorally relevant nearby objects and-in many behavioral contexts-less relevant distant structures. Hence, by making use of an active flight and gaze strategy, insects are capable of performing extraordinarily well even with a computationally simple motion detection mechanism.

  9. Non-functioning adrenal adenomas discovered incidentally on computed tomography

    SciTech Connect

    Mitnick, J.S.; Bosniak, M.A.; Megibow, A.J.; Naidich, D.P.

    1983-08-01

    Eighteen patients with unilateral non-metastatic non-functioning adrenal masses were studied with computed tomography (CT). Pathological examination in cases revealed benign adrenal adenomas. The others were followed up with serial CT scans and found to show no change in tumor size over a period of six months to three years. On the basis of these findings, the authors suggest certain criteria of a benign adrenal mass, including (a) diameter less than 5 cm, (b) smooth contour, (c) well-defined margin, and (d) no change in size on follow-up. Serial CT scanning can be used as an alternative to surgery in the management of many of these patients.

  10. FOREWORD: Third Nordic Symposium on Computer Simulation in Physics, Chemistry, Biology and Mathematics

    NASA Astrophysics Data System (ADS)

    Kaski, K.; Salomaa, M.

    1990-01-01

    These are Proceedings of the Third Nordic Symposium on Computer Simulation in Physics, Chemistry, Biology, and Mathematics, held August 25-26, 1989, at Lahti (Finland). The Symposium belongs to an annual series of Meetings, the first one of which was arranged in 1987 at Lund (Sweden) and the second one in 1988 at Kolle-Kolle near Copenhagen (Denmark). Although these Symposia have thus far been essentially Nordic events, their international character has increased significantly; the trend is vividly reflected through contributions in the present Topical Issue. The interdisciplinary nature of Computational Science is central to the activity; this fundamental aspect is also responsible, in an essential way, for its rapidly increasing impact. Crucially important to a wide spectrum of superficially disparate fields is the common need for extensive - and often quite demanding - computational modelling. For such theoretical models, no closed-form (analytical) solutions are available or they would be extremely difficult to find; hence one must rather resort to the Art of performing computational investigations. Among the unifying features in the computational research are the methods of simulation employed; methods which frequently are quite closely related with each other even for faculties of science that are quite unrelated. Computer simulation in Natural Sciences is presently apprehended as a discipline on its own right, occupying a broad region somewhere between the experimental and theoretical methods, but also partially overlapping with and complementing them. - Whichever its proper definition may be, the computational approach serves as a novel and an extremely versatile tool with which one can equally well perform "pure" experimental modelling and conduct "computational theory". Computational studies that have earlier been made possible only through supercomputers have opened unexpected, as well as exciting, novel frontiers equally in mathematics (e.g., fractals

  11. A Combination of Hand-Held Models and Computer Imaging Programs Helps Students Answer Oral Questions about Molecular Structure and Function: A Controlled Investigation of Student Learning

    ERIC Educational Resources Information Center

    Harris, Michelle A.; Peck, Ronald F.; Colton, Shannon; Morris, Jennifer; Neto, Elias Chaibub; Kallio, Julie

    2009-01-01

    We conducted a controlled investigation to examine whether a combination of computer imagery and tactile tools helps introductory cell biology laboratory undergraduate students better learn about protein structure/function relationships as compared with computer imagery alone. In all five laboratory sections, students used the molecular imaging…

  12. Enhancing the Reliability of Spectral Correlation Function with Distributed Computing

    NASA Astrophysics Data System (ADS)

    Alfaqawi, M. I.; Chebil, J.; Habaebi, M. H.; Ramli, N.; Mohamad, H.

    2013-12-01

    Various random time series used in signal processing systems are cyclostationary due to the sinusoidal carriers, pulse trains, periodic motion, or physical phenomenon. The cyclostationarity of the signal could be analysed by using the spectral correlation function (SCF). However, SCF is considered high complex due to the 2-D functionality and the required long observation time. The SCF could be computed in various methods however there are two methods used in practice such as FFT accumulation method (FAM) and strip spectral correlation algorithm (SSCA). This paper shows the benefit on the complexity and the reliability due to the workload distribution of one processor over different cooperated processors. The paper found that with increasing the reliability of the SCF, the number of the cooperated processors to achieve the half of the maximum complexity will reduce.

  13. The role of ontologies in biological and biomedical research: a functional perspective

    PubMed Central

    Schofield, Paul N.; Gkoutos, Georgios V.

    2015-01-01

    Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications. PMID:25863278

  14. The role of ontologies in biological and biomedical research: a functional perspective.

    PubMed

    Hoehndorf, Robert; Schofield, Paul N; Gkoutos, Georgios V

    2015-11-01

    Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.

  15. Multiscale Modeling of Biological Functions: From Enzymes to Molecular Machines (Nobel Lecture)

    PubMed Central

    Warshel, Arieh

    2016-01-01

    Adetailed understanding of the action of biological molecules is a pre-requisite for rational advances in health sciences and related fields. Here, the challenge is to move from available structural information to a clear understanding of the underlying function of the system. In light of the complexity of macromolecular complexes, it is essential to use computer simulations to describe how the molecular forces are related to a given function. However, using a full and reliable quantum mechanical representation of large molecular systems has been practically impossible. The solution to this (and related) problems has emerged from the realization that large systems can be spatially divided into a region where the quantum mechanical description is essential (e.g. a region where bonds are being broken), with the remainder of the system being represented on a simpler level by empirical force fields. This idea has been particularly effective in the development of the combined quantum mechanics/molecular mechanics (QM/MM) models. Here, the coupling between the electrostatic effects of the quantum and classical subsystems has been a key to the advances in describing the functions of enzymes and other biological molecules. The same idea of representing complex systems in different resolutions in both time and length scales has been found to be very useful in modeling the action of complex systems. In such cases, starting with coarse grained (CG) representations that were originally found to be very useful in simulating protein folding, and augmenting them with a focus on electrostatic energies, has led to models that are particularly effective in probing the action of molecular machines. The same multiscale idea is likely to play a major role in modeling of even more complex systems, including cells and collections of cells. PMID:25060243

  16. Multiscale modeling of biological functions: from enzymes to molecular machines (Nobel Lecture).

    PubMed

    Warshel, Arieh

    2014-09-15

    A detailed understanding of the action of biological molecules is a pre-requisite for rational advances in health sciences and related fields. Here, the challenge is to move from available structural information to a clear understanding of the underlying function of the system. In light of the complexity of macromolecular complexes, it is essential to use computer simulations to describe how the molecular forces are related to a given function. However, using a full and reliable quantum mechanical representation of large molecular systems has been practically impossible. The solution to this (and related) problems has emerged from the realization that large systems can be spatially divided into a region where the quantum mechanical description is essential (e.g. a region where bonds are being broken), with the remainder of the system being represented on a simpler level by empirical force fields. This idea has been particularly effective in the development of the combined quantum mechanics/molecular mechanics (QM/MM) models. Here, the coupling between the electrostatic effects of the quantum and classical subsystems has been a key to the advances in describing the functions of enzymes and other biological molecules. The same idea of representing complex systems in different resolutions in both time and length scales has been found to be very useful in modeling the action of complex systems. In such cases, starting with coarse grained (CG) representations that were originally found to be very useful in simulating protein folding, and augmenting them with a focus on electrostatic energies, has led to models that are particularly effective in probing the action of molecular machines. The same multiscale idea is likely to play a major role in modeling of even more complex systems, including cells and collections of cells.

  17. Overview of significant challenges in molecular biology amenable to computational methods.

    PubMed

    Glaeser, R M

    1994-01-01

    Many challenging but significant opportunities exist for the development of theoretical approaches in modern Cell and Molecular Biology. The creation of data bases which contain extremely large amounts of information has proven to be an unexpectedly important facto-tin gaining acceptance and respectability for theoretical work that builds on nothing more than what is in the data base itself, such as theoretical work involving the analysis of known protein structures, or the development of more powerful homology searches. Other opportunities, not yet accepted by a broad community, involve work on complex networks (metabolic, genetic, immunologic and neural networks) and work on the "physics of how things work." The DOE National Laboratory System represents the ideal institution that would be well suited to the role of being an "incubator" for the creation of a theoretical and computational discipline within modern biology. PMID:7755540

  18. [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.

  19. The Development of Computational Biology in South Africa: Successes Achieved and Lessons Learnt

    PubMed Central

    Mulder, Nicola J.; Christoffels, Alan; de Oliveira, Tulio; Gamieldien, Junaid; Hazelhurst, Scott; Joubert, Fourie; Kumuthini, Judit; Pillay, Ché S.; Snoep, Jacky L.; Tastan Bishop, Özlem; Tiffin, Nicki

    2016-01-01

    Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt. PMID:26845152

  20. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    PubMed

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator. PMID:26451831

  1. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    PubMed

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

  2. The Development of Computational Biology in South Africa: Successes Achieved and Lessons Learnt.

    PubMed

    Mulder, Nicola J; Christoffels, Alan; de Oliveira, Tulio; Gamieldien, Junaid; Hazelhurst, Scott; Joubert, Fourie; Kumuthini, Judit; Pillay, Ché S; Snoep, Jacky L; Tastan Bishop, Özlem; Tiffin, Nicki

    2016-02-01

    Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt.

  3. Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

    PubMed Central

    Laraia, Luca; McKenzie, Grahame; Spring, David R.; Venkitaraman, Ashok R.; Huggins, David J.

    2015-01-01

    Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs. PMID:26091166

  4. Computation of the lattice Green function for a dislocation

    NASA Astrophysics Data System (ADS)

    Tan, Anne Marie Z.; Trinkle, Dallas R.

    2016-08-01

    Modeling isolated dislocations is challenging due to their long-ranged strain fields. Flexible boundary condition methods capture the correct long-range strain field of a defect by coupling the defect core to an infinite harmonic bulk through the lattice Green function (LGF). To improve the accuracy and efficiency of flexible boundary condition methods, we develop a numerical method to compute the LGF specifically for a dislocation geometry; in contrast to previous methods, where the LGF was computed for the perfect bulk as an approximation for the dislocation. Our approach directly accounts for the topology of a dislocation, and the errors in the LGF computation converge rapidly for edge dislocations in a simple cubic model system as well as in BCC Fe with an empirical potential. When used within the flexible boundary condition approach, the dislocation LGF relaxes dislocation core geometries in fewer iterations than when the perfect bulk LGF is used as an approximation for the dislocation, making a flexible boundary condition approach more efficient.

  5. An Atomistic Statistically Effective Energy Function for Computational Protein Design.

    PubMed

    Topham, Christopher M; Barbe, Sophie; André, Isabelle

    2016-08-01

    Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue-based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical

  6. An Atomistic Statistically Effective Energy Function for Computational Protein Design.

    PubMed

    Topham, Christopher M; Barbe, Sophie; André, Isabelle

    2016-08-01

    Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue-based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical

  7. Functional Connectivity’s Degenerate View of Brain Computation

    PubMed Central

    Giron, Alain; Rudrauf, David

    2016-01-01

    Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled. Empirical evidence features mixed results: from little to significant FC variability and correlation with cognitive functions, within and between participants. We used a unified approach combining multivariate analysis, bootstrap and computational modeling to characterize the potential variety of patterns of FC and SC both qualitatively and quantitatively. Empirical data and simulations from generative models with different dynamical behaviors demonstrated, largely irrespective of FC metrics, that a linear subspace with dimension one or two could explain much of the variability across patterns of FC. On the contrary, the variability across BOLD time-courses could not be reduced to such a small subspace. FC appeared to strongly reflect SC and to be partly governed by a Gaussian process. The main differences between simulated and empirical data related to limitations of DWI-based SC estimation (and SC itself could then be estimated from FC). Above and beyond the limited dynamical range of the BOLD signal itself, measures of FC may offer a degenerate representation of brain interactions, with limited access to the underlying complexity. They feature an invariant common core, reflecting the channel capacity of the network as conditioned by SC, with a limited, though perhaps meaningful residual variability. PMID:27736900

  8. Proceedings of the 2013 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.

    PubMed

    Wren, Jonathan D; Dozmorov, Mikhail G; Burian, Dennis; Kaundal, Rakesh; Perkins, Andy; Perkins, Ed; Kupfer, Doris M; Springer, Gordon K

    2013-01-01

    The tenth annual conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS 2013), "The 10th Anniversary in a Decade of Change: Discovery in a Sea of Data", took place at the Stoney Creek Inn & Conference Center in Columbia, Missouri on April 5-6, 2013. This year's Conference Chairs were Gordon Springer and Chi-Ren Shyu from the University of Missouri and Edward Perkins from the US Army Corps of Engineers Engineering Research and Development Center, who is also the current MCBIOS President (2012-3). There were 151 registrants and a total of 111 abstracts (51 oral presentations and 60 poster session abstracts).

  9. Towards computational prediction of microRNA function and activity

    PubMed Central

    Ulitsky, Igor; Laurent, Louise C.; Shamir, Ron

    2010-01-01

    While it has been established that microRNAs (miRNAs) play key roles throughout development and are dysregulated in many human pathologies, the specific processes and pathways regulated by individual miRNAs are mostly unknown. Here, we use computational target predictions in order to automatically infer the processes affected by human miRNAs. Our approach improves upon standard statistical tools by addressing specific characteristics of miRNA regulation. Our analysis is based on a novel compendium of experimentally verified miRNA-pathway and miRNA-process associations that we constructed, which can be a useful resource by itself. Our method also predicts novel miRNA-regulated pathways, refines the annotation of miRNAs for which only crude functions are known, and assigns differential functions to miRNAs with closely related sequences. Applying our approach to groups of co-expressed genes allows us to identify miRNAs and genomic miRNA clusters with functional importance in specific stages of early human development. A full list of the predicted mRNA functions is available at http://acgt.cs.tau.ac.il/fame/. PMID:20576699

  10. Experimental evidence validating the computational inference of functional associations from gene fusion events: a critical survey.

    PubMed

    Promponas, Vasilis J; Ouzounis, Christos A; Iliopoulos, Ioannis

    2014-05-01

    More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology.

  11. Experimental evidence validating the computational inference of functional associations from gene fusion events: a critical survey

    PubMed Central

    Promponas, Vasilis J.; Ouzounis, Christos A.; Iliopoulos, Ioannis

    2014-01-01

    More than a decade ago, a number of methods were proposed for the inference of protein interactions, using whole-genome information from gene clusters, gene fusions and phylogenetic profiles. This structural and evolutionary view of entire genomes has provided a valuable approach for the functional characterization of proteins, especially those without sequence similarity to proteins of known function. Furthermore, this view has raised the real possibility to detect functional associations of genes and their corresponding proteins for any entire genome sequence. Yet, despite these exciting developments, there have been relatively few cases of real use of these methods outside the computational biology field, as reflected from citation analysis. These methods have the potential to be used in high-throughput experimental settings in functional genomics and proteomics to validate results with very high accuracy and good coverage. In this critical survey, we provide a comprehensive overview of 30 most prominent examples of single pairwise protein interaction cases in small-scale studies, where protein interactions have either been detected by gene fusion or yielded additional, corroborating evidence from biochemical observations. Our conclusion is that with the derivation of a validated gold-standard corpus and better data integration with big experiments, gene fusion detection can truly become a valuable tool for large-scale experimental biology. PMID:23220349

  12. ISCB: past-present perspective for the International Society for Computational Biology.

    PubMed

    Rost, Burkhard

    2014-01-01

    Since its establishment in 1997, International Society for Computational Biology (ISCB) has contributed importantly toward advancing the understanding of living systems through computation. The ISCB represents nearly 3000 members working in >70 countries. It has doubled the number of members since 2007. At the same time, the number of meetings organized by the ISCB has increased from two in 2007 to eight in 2013, and the society has cemented many lasting alliances with regional societies and specialist groups. ISCB is ready to grow into a challenging and promising future. The progress over the past 7 years has resulted from the vision, and possibly more importantly, the passion and hard working dedication of many individuals.

  13. Understanding sequence similarity and framework analysis between centromere proteins using computational biology.

    PubMed

    Doss, C George Priya; Chakrabarty, Chiranjib; Debajyoti, C; Debottam, S

    2014-11-01

    Certain mysteries pointing toward their recruitment pathways, cell cycle regulation mechanisms, spindle checkpoint assembly, and chromosome segregation process are considered the centre of attraction in cancer research. In modern times, with the established databases, ranges of computational platforms have provided a platform to examine almost all the physiological and biochemical evidences in disease-associated phenotypes. Using existing computational methods, we have utilized the amino acid residues to understand the similarity within the evolutionary variance of different associated centromere proteins. This study related to sequence similarity, protein-protein networking, co-expression analysis, and evolutionary trajectory of centromere proteins will speed up the understanding about centromere biology and will create a road map for upcoming researchers who are initiating their work of clinical sequencing using centromere proteins.

  14. ISCB: past-present perspective for the International Society for Computational Biology.

    PubMed

    Rost, Burkhard

    2013-12-15

    Since its establishment in 1997, International Society for Computational Biology (ISCB) has contributed importantly toward advancing the understanding of living systems through computation. The ISCB represents nearly 3000 members working in >70 countries. It has doubled the number of members since 2007. At the same time, the number of meetings organized by the ISCB has increased from two in 2007 to eight in 2013, and the society has cemented many lasting alliances with regional societies and specialist groups. ISCB is ready to grow into a challenging and promising future. The progress over the past 7 years has resulted from the vision, and possibly more importantly, the passion and hard working dedication of many individuals.

  15. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

    PubMed Central

    Li, Jun; Zhao, Patrick X.

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/. PMID:27446133

  16. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    PubMed

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

  17. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    PubMed

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/. PMID:27446133

  18. Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts

    PubMed Central

    Capitani, Guido; Duarte, Jose M.; Baskaran, Kumaran; Bliven, Spencer; Somody, Joseph C.

    2016-01-01

    Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein–protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein–protein docking. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: guido.capitani@psi.ch PMID:26508758

  19. Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts.

    PubMed

    Capitani, Guido; Duarte, Jose M; Baskaran, Kumaran; Bliven, Spencer; Somody, Joseph C

    2016-02-15

    Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein-protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein-protein docking.

  20. Building DNA nanostructures for molecular computation, templated assembly, and biological applications.

    PubMed

    Rangnekar, Abhijit; LaBean, Thomas H

    2014-06-17

    CONSPECTUS: DNA is a critical biomolecule well-known for its roles in biology and genetics. Moreover, its double-helical structure and the Watson-Crick pairing of its bases make DNA structurally predictable. This predictability enables design and synthesis of artificial DNA nanostructures by suitable programming of the base sequences of DNA strands. Since the advent of the field of DNA nanotechnology in 1982, a variety of DNA nanostructures have been designed and used for numerous applications. In this Account, we discuss the progress made by our lab which has contributed toward the overall advancement of the field. Tile-based DNA nanostructures are an integral part of structural DNA nanotechnology. These structures are formed using several short, chemically synthesized DNA strands by programming their base sequences so that they self-assemble into desired constructs. Design and assembly of several DNA tiles will be discussed in this Account. Tiles include, for example, TX tiles with three parallel, coplanar duplexes, 4 × 4 cross-tiles with four arms, and weave-tiles with weave-like architecture. Another category of tiles we will present involve multiple parallel duplexes that assemble to form closed tubular structures. All of these tile types have been used to form micrometer-scale one- and two-dimensional arrays and lattices. Origami-based structures constitute another category where a long single-stranded DNA scaffold is folded into desired shapes by association with multiple short staple strands. This Account will describe the efforts by our lab in devising new strategies to improve the maximum size of origami structures. The various DNA nanostructures detailed here have been used in a wide variety of different applications. This Account will discuss the use of DNA tiles for logical computation, encoding information as molecular barcodes, and functionalization for patterning of other nanoscale organic and inorganic materials. Consequently, we have used DNA

  1. A proposal for augmenting biological model construction with a semi-intelligent computational modeling assistant

    PubMed Central

    Christley, Scott; An, Gary

    2013-01-01

    The translational challenge in biomedical research lies in the effective and efficient transfer of mechanistic knowledge from one biological context to another. Implicit in this process is the establishment of causality from correlation in the form of mechanistic hypotheses. Effectively addressing the translational challenge requires the use of automated methods, including the ability to computationally capture the dynamic aspect of putative hypotheses such that they can be evaluated in a high throughput fashion. Ontologies provide structure and organization to biomedical knowledge; converting these representations into executable models/simulations is the next necessary step. Researchers need the ability to map their conceptual models into a model specification that can be transformed into an executable simulation program. We suggest this mapping process, which approximates certain steps in the development of a computational model, can be expressed as a set of logical rules, and a semi-intelligent computational agent, the Computational Modeling Assistant (CMA), can perform reasoning to develop a plan to achieve the construction of an executable model. Presented herein is a description and implementation for a model construction reasoning process between biomedical and simulation ontologies that is performed by the CMA to produce the specification of an executable model that can be used for dynamic knowledge representation. PMID:23990750

  2. Non-coding RNAs: Classification, Biology and Functioning.

    PubMed

    Hombach, Sonja; Kretz, Markus

    2016-01-01

    One of the long-standing principles of molecular biology is that DNA acts as a template for transcription of messenger RNAs, which serve as blueprints for protein translation. A rapidly growing number of exceptions to this rule have been reported over the past decades: they include long known classes of RNAs involved in translation such as transfer RNAs and ribosomal RNAs, small nuclear RNAs involved in splicing events, and small nucleolar RNAs mainly involved in the modification of other small RNAs, such as ribosomal RNAs and transfer RNAs. More recently, several classes of short regulatory non-coding RNAs, including piwi-associated RNAs, endogenous short-interfering RNAs and microRNAs have been discovered in mammals, which act as key regulators of gene expression in many different cellular pathways and systems. Additionally, the human genome encodes several thousand long non-protein coding RNAs >200 nucleotides in length, some of which play crucial roles in a variety of biological processes such as epigenetic control of chromatin, promoter-specific gene regulation, mRNA stability, X-chromosome inactivation and imprinting. In this chapter, we will introduce several classes of short and long non-coding RNAs, describe their diverse roles in mammalian gene regulation and give examples for known modes of action. PMID:27573892

  3. Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

    PubMed

    Kirschner, Denise E; Hunt, C Anthony; Marino, Simeone; Fallahi-Sichani, Mohammad; Linderman, Jennifer J

    2014-01-01

    The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work.

  4. Proceedings of the workshop on advanced computer technologies and biological sequencing

    SciTech Connect

    Not Available

    1988-11-01

    The participants in the workshop agree that advanced computer technologies will play a significant role in biological sequencing. They suggest a strategy based on the following four recommendations: define a set of model projects, and develop a complete set of data management and analysis tools for these model projects; seek to consolidate appropriate databases, while allowing for the flexible development and design of tools that will permit further consolidation, In the longer term, develop a coordinated effort that will allow networking of all relevant databases; encourage the development, collection, and distribution of analysis tools; and address user interface issues and encourage the development of graphics and visualization tools. Section 3 of this report elaborates on each of these recommendations. Section 2 contains the tutorials presented at the workshop and a summary of the comments made in the discussion period following the tutorials. These tutorials were an integral part of the workshop: they provided a forum for the discussion of the needs of biologists in managing and analyzing biological sequencing data, and the capabilities of advanced computer technologies in meeting those needs. Also included in Section 2 is an informal paper on fifth generation technologies, prepared by two of the participants. Appendix A contains the documents (edited for grammar) prepared by the participants and groups at the workshop. Appendix B contains the workshop program.

  5. An Evolutionary Computation Approach to Examine Functional Brain Plasticity

    PubMed Central

    Roy, Arnab; Campbell, Colin; Bernier, Rachel A.; Hillary, Frank G.

    2016-01-01

    One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength

  6. An Evolutionary Computation Approach to Examine Functional Brain Plasticity.

    PubMed

    Roy, Arnab; Campbell, Colin; Bernier, Rachel A; Hillary, Frank G

    2016-01-01

    One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength

  7. Computer Modeling of Protocellular Functions: Peptide Insertion in Membranes

    NASA Technical Reports Server (NTRS)

    Rodriquez-Gomez, D.; Darve, E.; Pohorille, A.

    2006-01-01

    Lipid vesicles became the precursors to protocells by acquiring the capabilities needed to survive and reproduce. These include transport of ions, nutrients and waste products across cell walls and capture of energy and its conversion into a chemically usable form. In modem organisms these functions are carried out by membrane-bound proteins (about 30% of the genome codes for this kind of proteins). A number of properties of alpha-helical peptides suggest that their associations are excellent candidates for protobiological precursors of proteins. In particular, some simple a-helical peptides can aggregate spontaneously and form functional channels. This process can be described conceptually by a three-step thermodynamic cycle: 1 - folding of helices at the water-membrane interface, 2 - helix insertion into the lipid bilayer and 3 - specific interactions of these helices that result in functional tertiary structures. Although a crucial step, helix insertion has not been adequately studied because of the insolubility and aggregation of hydrophobic peptides. In this work, we use computer simulation methods (Molecular Dynamics) to characterize the energetics of helix insertion and we discuss its importance in an evolutionary context. Specifically, helices could self-assemble only if their interactions were sufficiently strong to compensate the unfavorable Free Energy of insertion of individual helices into membranes, providing a selection mechanism for protobiological evolution.

  8. [Biological problems of origin and development of various physiological functions (theory and application)].

    PubMed

    Ivanov, K P

    2001-01-01

    The author presents some idea about origin and development of some physiological functions: outer breathing, breath function of blood, blood circulation, thermoregulation, energy supply. The conclusions about main directions of evolution of these functions and duration of their development in phylogeny were drawn. The author gave some examples of abrupt changes of development of these functions in different groups of animals and discussed possible reasons of such changes. General quantitative estimation of the results of evolution of these functions from the position of their summArized efficiency was done. Quantitative characteristics of optimization and efficiency limits of physiological functions were suggested on the base of new data in general biology and comparative physiology. The author put toward the hypothesis about conventional "mistakes" of evolution and showed deep biological reasons of some seriOus illness. The examples of some applied problems in biology, physiology and medicine that can be solved with the data on evolution of physiological functions are presented. PMID:11548400

  9. Calibrated peer review for computer-assisted learning of biological research competencies.

    PubMed

    Clase, Kari L; Gundlach, Ellen; Pelaez, Nancy J

    2010-09-01

    Recently, both science and technology faculty have been recognizing biological research competencies that are valued but rarely assessed. Some of these valued learning outcomes include scientific methods and thinking, critical assessment of primary papers, quantitative reasoning, communication, and putting biological research into a historical and broader social context. This article presents examples of Calibrated Peer Review (CPR) assignments that illustrate a computer-assisted method to help students achieve biological research competencies. A new release of CPR is appropriate for engaging students online in reading and writing about investigations. A participant perception inventory was designed for use as a repeated measure to discriminate among beginning, middle, and ending student perceptions. Examples are provided to demonstrate how to assess student perceptions of what they gain from instruction related to science research competencies. Results suggest that students in a large enrollment class consider CPR to be useful for helping them learn about quantitative and categorical research variables; the use of the experimental method to test ideas; the use of controls; analysis, interpretation, and presentation of data; and how to critically read primary papers.

  10. Computational optimization for S-type biological systems: cockroach genetic algorithm.

    PubMed

    Wu, Shinq-Jen; Wu, Cheng-Tao

    2013-10-01

    S-type biological systems (S-systems) are demonstrated to be universal approximations of continuous biological systems. S-systems are easy to be generalized to large systems. The systems are identified through data-driven identification techniques (cluster-based algorithms or computational methods). However, S-systems' identification is challenging because multiple attractors exist in such highly nonlinear systems. Moreover, in some biological systems the interactive effect cannot be neglected even the interaction order is small. Therefore, learning should be focused on increasing the gap between the true and redundant interaction. In addition, a wide searching space is necessary because no prior information is provided. The used technologies should have the ability to achieve convergence enhancement and diversity preservation. Cockroaches live in nearly all habitats and survive for more than 300 million years. In this paper, we mimic cockroaches' competitive swarm behavior and integrated it with advanced evolutionary operations. The proposed cockroach genetic algorithm (CGA) possesses strong snatching-food ability to rush forward to a target and high migration ability to escape from local minimum. CGA was tested with three small-scale systems, a twenty-state medium-scale system and a thirty-state large-scale system. A wide search space ([0,100] for rate constants and [-100,100] for kinetic orders) with random or bad initial starts are used to show the high exploration performance.

  11. Assessing Executive Function Using a Computer Game: Computational Modeling of Cognitive Processes

    PubMed Central

    Hagler, Stuart; Jimison, Holly B.; Pavel, Misha

    2014-01-01

    Early and reliable detection of cognitive decline is one of the most important challenges of current healthcare. In this project we developed an approach whereby a frequently played computer game can be used to assess a variety of cognitive processes and estimate the results of the pen-and-paper Trail-Making Test (TMT) – known to measure executive function, as well as visual pattern recognition, speed of processing, working memory, and set-switching ability. We developed a computational model of the TMT based on a decomposition of the test into several independent processes, each characterized by a set of parameters that can be estimated from play of a computer game designed to resemble the TMT. An empirical evaluation of the model suggests that it is possible to use the game data to estimate the parameters of the underlying cognitive processes and using the values of the parameters to estimate the TMT performance. Cognitive measures and trends in these measures can be used to identify individuals for further assessment, to provide a mechanism for improving the early detection of neurological problems, and to provide feedback and monitoring for cognitive interventions in the home. PMID:25014944

  12. Computing the Partition Function for Kinetically Trapped RNA Secondary Structures

    PubMed Central

    Lorenz, William A.; Clote, Peter

    2011-01-01

    An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in time and space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server

  13. Computing the partition function for kinetically trapped RNA secondary structures.

    PubMed

    Lorenz, William A; Clote, Peter

    2011-01-01

    An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in O(n3) time and O(n2) space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures--indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy

  14. The role of computer networking in investigating unusual disease outbreaks and allegations of biological and toxin weapons use.

    PubMed

    Woodall, J

    1998-01-01

    Computer networking can aid in the epidemiological investigation of unusual disease outbreaks and possible uses of biological weapons. Exchange of computerized data over the Internet has many advantages in facilitating the investigation of the source of a disease outbreak. It is especially useful in the investigation of suspected or alleged releases of biological weapons. Computer networking through the Internet a fosters a truly global disease outbreak early warning system in which both government and non-government sources are contributing. Such information exchange is of great potential benefit to the Biological Weapons Convention and the attempts to develop a verification protocol. PMID:9800103

  15. More Ideas for Monitoring Biological Experiments with the BBC Computer: Absorption Spectra, Yeast Growth, Enzyme Reactions and Animal Behaviour.

    ERIC Educational Resources Information Center

    Openshaw, Peter

    1988-01-01

    Presented are five ideas for A-level biology experiments using a laboratory computer interface. Topics investigated include photosynthesis, yeast growth, animal movements, pulse rates, and oxygen consumption and production by organisms. Includes instructions specific to the BBC computer system. (CW)

  16. Delivering The Benefits of Chemical-Biological Integration in Computational Toxicology at the EPA (ACS Fall meeting)

    EPA Science Inventory

    Abstract: Researchers at the EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The intent...

  17. Computational Effective Fault Detection by Means of Signature Functions

    PubMed Central

    Baranski, Przemyslaw; Pietrzak, Piotr

    2016-01-01

    The paper presents a computationally effective method for fault detection. A system’s responses are measured under healthy and ill conditions. These signals are used to calculate so-called signature functions that create a signal space. The current system’s response is projected into this space. The signal location in this space easily allows to determine the fault. No classifier such as a neural network, hidden Markov models, etc. is required. The advantage of this proposed method is its efficiency, as computing projections amount to calculating dot products. Therefore, this method is suitable for real-time embedded systems due to its simplicity and undemanding processing capabilities which permit the use of low-cost hardware and allow rapid implementation. The approach performs well for systems that can be considered linear and stationary. The communication presents an application, whereby an industrial process of moulding is supervised. The machine is composed of forms (dies) whose alignment must be precisely set and maintained during the work. Typically, the process is stopped periodically to manually control the alignment. The applied algorithm allows on-line monitoring of the device by analysing the acceleration signal from a sensor mounted on a die. This enables to detect failures at an early stage thus prolonging the machine’s life. PMID:26949942

  18. Imaging local brain function with emission computed tomography

    SciTech Connect

    Kuhl, D.E.

    1984-03-01

    Positron emission tomography (PET) using /sup 18/F-fluorodeoxyglucose (FDG) was used to map local cerebral glucose utilization in the study of local cerebral function. This information differs fundamentally from structural assessment by means of computed tomography (CT). In normal human volunteers, the FDG scan was used to determine the cerebral metabolic response to conrolled sensory stimulation and the effects of aging. Cerebral metabolic patterns are distinctive among depressed and demented elderly patients. The FDG scan appears normal in the depressed patient, studded with multiple metabolic defects in patients with multiple infarct dementia, and in the patients with Alzheimer disease, metabolism is particularly reduced in the parietal cortex, but only slightly reduced in the caudate and thalamus. The interictal FDG scan effectively detects hypometabolic brain zones that are sites of onset for seizures in patients with partial epilepsy, even though these zones usually appear normal on CT scans. The future prospects of PET are discussed.

  19. Beyond Iron: Non-Classical Biological Functions of Bacterial Siderophores

    PubMed Central

    Johnstone, Timothy C.; Nolan, Elizabeth M.

    2015-01-01

    Bacteria secrete small molecules known as siderophores to acquire iron from their surroundings. For over 60 years, investigations into the bioinorganic chemistry of these molecules, including fundamental coordination chemistry studies, have provided insight into the crucial role that siderophores play in bacterial iron homeostasis. The importance of understanding the fundamental chemistry underlying bacterial life has been highlighted evermore in recent years because of the emergence of antibiotic-resistant bacteria and the need to prevent the global rise of these superbugs. Increasing reports of siderophores functioning in capacities other than iron transport have appeared recently, but reports of “non-classical” siderophore functions have long paralleled those of iron transport. One particular non-classical function of these iron chelators, namely antibiotic activity, was even documented before the role of siderophores in iron transport was established. In this Perspective, we present an exposition of past and current work into non-classical functions of siderophores and highlight the directions in which we anticipate that this research is headed. Examples include the ability of siderophores to function as zincophores, chalkophores, and metallophores for a variety of other metals, sequester heavy metal toxins, transport boron, act as signalling molecules, regulate oxidative stress, and provide antibacterial activity. PMID:25764171

  20. Beyond iron: non-classical biological functions of bacterial siderophores.

    PubMed

    Johnstone, Timothy C; Nolan, Elizabeth M

    2015-04-14

    Bacteria secrete small molecules known as siderophores to acquire iron from their surroundings. For over 60 years, investigations into the bioinorganic chemistry of these molecules, including fundamental coordination chemistry studies, have provided insight into the crucial role that siderophores play in bacterial iron homeostasis. The importance of understanding the fundamental chemistry underlying bacterial life has been highlighted evermore in recent years because of the emergence of antibiotic-resistant bacteria and the need to prevent the global rise of these superbugs. Increasing reports of siderophores functioning in capacities other than iron transport have appeared recently, but reports of "non-classical" siderophore functions have long paralleled those of iron transport. One particular non-classical function of these iron chelators, namely antibiotic activity, was documented before the role of siderophores in iron transport was established. In this Perspective, we present an exposition of past and current work into non-classical functions of siderophores and highlight the directions in which we anticipate that this research is headed. Examples include the ability of siderophores to function as zincophores, chalkophores, and metallophores for a variety of other metals, sequester heavy metal toxins, transport boron, act as signalling molecules, regulate oxidative stress, and provide antibacterial activity. PMID:25764171