Sample records for computing biological functions

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

    PubMed Central

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

    2010-01-01

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

  2. 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

  3. 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

  4. All biology is computational biology.

    PubMed

    Markowetz, Florian

    2017-03-01

    Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.

  5. Digital and biological computing in organizations.

    PubMed

    Kampfner, Roberto R

    2002-01-01

    Michael Conrad unveiled many of the fundamental characteristics of biological computing. Underlying the behavioral variability and the adaptability of biological systems are these characteristics, including the ability of biological information processing to exploit quantum features at the atomic level, the powerful 3-D pattern recognition capabilities of macromolecules, the computational efficiency, and the ability to support biological function. Among many other things, Conrad formalized and explicated the underlying principles of biological adaptability, characterized the differences between biological and digital computing in terms of a fundamental tradeoff between adaptability and programmability of information processing, and discussed the challenges of interfacing digital computers and human society. This paper is about the encounter of biological and digital computing. The focus is on the nature of the biological information processing infrastructure of organizations and how it can be extended effectively with digital computing. In order to achieve this goal effectively, however, we need to embed properly digital computing into the information processing aspects of human and social behavior and intelligence, which are fundamentally biological. Conrad's legacy provides a firm, strong, and inspiring foundation for this endeavor.

  6. 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.

  7. Computational Systems Biology

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

    McDermott, Jason E.; Samudrala, Ram; Bumgarner, Roger E.

    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, 2005more » #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

  8. Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.

    PubMed

    Bonham, Kevin S; Stefan, Melanie I

    2017-10-01

    While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance.

  9. Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology

    PubMed Central

    2017-01-01

    While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance. PMID:29023441

  10. 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.

  11. 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.

  12. Computational protein design-the next generation tool to expand synthetic biology applications.

    PubMed

    Gainza-Cirauqui, Pablo; Correia, Bruno Emanuel

    2018-05-02

    One powerful approach to engineer synthetic biology pathways is the assembly of proteins sourced from one or more natural organisms. However, synthetic pathways often require custom functions or biophysical properties not displayed by natural proteins, limitations that could be overcome through modern protein engineering techniques. Structure-based computational protein design is a powerful tool to engineer new functional capabilities in proteins, and it is beginning to have a profound impact in synthetic biology. Here, we review efforts to increase the capabilities of synthetic biology using computational protein design. We focus primarily on computationally designed proteins not only validated in vitro, but also shown to modulate different activities in living cells. Efforts made to validate computational designs in cells can illustrate both the challenges and opportunities in the intersection of protein design and synthetic biology. We also highlight protein design approaches, which although not validated as conveyors of new cellular function in situ, may have rapid and innovative applications in synthetic biology. We foresee that in the near-future, computational protein design will vastly expand the functional capabilities of synthetic cells. Copyright © 2018. Published by Elsevier Ltd.

  13. 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.

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

    PubMed Central

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

    2011-01-01

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

  15. 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.

  16. 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

  17. Mathematical and Computational Modeling in Complex Biological Systems

    PubMed Central

    Li, Wenyang; Zhu, Xiaoliang

    2017-01-01

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

  18. Mathematical and Computational Modeling in Complex Biological Systems.

    PubMed

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

    2017-01-01

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

  19. Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

    PubMed

    Yin, Zekun; Lan, Haidong; Tan, Guangming; Lu, Mian; Vasilakos, Athanasios V; Liu, Weiguo

    2017-01-01

    The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

  20. 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.

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

    PubMed

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

    2014-11-01

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

  2. The 'Biologically-Inspired Computing' Column

    NASA Technical Reports Server (NTRS)

    Hinchey, Mike

    2006-01-01

    The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

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

    PubMed

    D'Onofrio, David J; An, Gary

    2010-01-21

    The robust storage, updating and utilization of information are necessary for the maintenance and perpetuation of dynamic systems. These systems can exist as constructs of metal-oxide semiconductors and silicon, as in a digital computer, or in the "wetware" of organic compounds, proteins and nucleic acids that make up biological organisms. We propose that there are essential functional properties of centralized information-processing systems; for digital computers these properties reside in the computer's hard drive, and for eukaryotic cells they are manifest in the DNA and associated structures. Presented herein is a descriptive framework that compares DNA and its associated proteins and sub-nuclear structure with the structure and function of the computer hard drive. We identify four essential properties of information for a centralized storage and processing system: (1) orthogonal uniqueness, (2) low level formatting, (3) high level formatting and (4) translation of stored to usable form. The corresponding aspects of the DNA complex and a computer hard drive are categorized using this classification. This is intended to demonstrate a functional equivalence between the components of the two systems, and thus the systems themselves. Both the DNA complex and the computer hard drive contain components that fulfill the essential properties of a centralized information storage and processing system. The functional equivalence of these components provides insight into both the design process of engineered systems and the evolved solutions addressing similar system requirements. However, there are points where the comparison breaks down, particularly when there are externally imposed information-organizing structures on the computer hard drive. A specific example of this is the imposition of the File Allocation Table (FAT) during high level formatting of the computer hard drive and the subsequent loading of an operating system (OS). Biological systems do not have an

  9. A First Attempt to Bring Computational Biology into Advanced High School Biology Classrooms

    PubMed Central

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

    2011-01-01

    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. PMID:22046118

  10. 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.

  11. 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

  12. 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.

  13. Computational Systems Chemical Biology

    PubMed Central

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

    2013-01-01

    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 (Oprea et al., 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. PMID:20838980

  14. Secure Encapsulation and Publication of Biological Services in the Cloud Computing Environment

    PubMed Central

    Zhang, Weizhe; Wang, Xuehui; Lu, Bo; Kim, Tai-hoon

    2013-01-01

    Secure encapsulation and publication for bioinformatics software products based on web service are presented, and the basic function of biological information is realized in the cloud computing environment. In the encapsulation phase, the workflow and function of bioinformatics software are conducted, the encapsulation interfaces are designed, and the runtime interaction between users and computers is simulated. In the publication phase, the execution and management mechanisms and principles of the GRAM components are analyzed. The functions such as remote user job submission and job status query are implemented by using the GRAM components. The services of bioinformatics software are published to remote users. Finally the basic prototype system of the biological cloud is achieved. PMID:24078906

  15. Secure encapsulation and publication of biological services in the cloud computing environment.

    PubMed

    Zhang, Weizhe; Wang, Xuehui; Lu, Bo; Kim, Tai-hoon

    2013-01-01

    Secure encapsulation and publication for bioinformatics software products based on web service are presented, and the basic function of biological information is realized in the cloud computing environment. In the encapsulation phase, the workflow and function of bioinformatics software are conducted, the encapsulation interfaces are designed, and the runtime interaction between users and computers is simulated. In the publication phase, the execution and management mechanisms and principles of the GRAM components are analyzed. The functions such as remote user job submission and job status query are implemented by using the GRAM components. The services of bioinformatics software are published to remote users. Finally the basic prototype system of the biological cloud is achieved.

  16. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

    PubMed Central

    Gu, Shuo

    2017-01-01

    With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed. PMID:28690664

  17. Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective.

    PubMed

    Gu, Shuo; Pei, Jianfeng

    2017-01-01

    With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

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

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

    James, Conrad D.; Schiess, Adrian B.; Howell, Jamie

    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 willmore » 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.« less

  19. Focus issue: series on computational and systems biology.

    PubMed

    Gough, Nancy R

    2011-09-06

    The application of computational biology and systems biology is yielding quantitative insight into cellular regulatory phenomena. For the month of September, Science Signaling highlights research featuring computational approaches to understanding cell signaling and investigation of signaling networks, a series of Teaching Resources from a course in systems biology, and various other articles and resources relevant to the application of computational biology and systems biology to the study of signal transduction.

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

    PubMed

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

    2017-09-01

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

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

    PubMed

    Chalkidis, Georgios; Nagasaki, Masao; Miyano, Satoru

    2011-01-01

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

  2. 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…

  3. Computational Tools for Stem Cell Biology

    PubMed Central

    Bian, Qin; Cahan, Patrick

    2016-01-01

    For over half a century, the field of developmental biology has leveraged computation to explore mechanisms of developmental processes. More recently, computational approaches have been critical in the translation of high throughput data into knowledge of both developmental and stem cell biology. In the last several years, a new sub-discipline of computational stem cell biology has emerged that synthesizes the modeling of systems-level aspects of stem cells with high-throughput molecular data. In this review, we provide an overview of this new field and pay particular attention to the impact that single-cell transcriptomics is expected to have on our understanding of development and our ability to engineer cell fate. PMID:27318512

  4. Computational Tools for Stem Cell Biology.

    PubMed

    Bian, Qin; Cahan, Patrick

    2016-12-01

    For over half a century, the field of developmental biology has leveraged computation to explore mechanisms of developmental processes. More recently, computational approaches have been critical in the translation of high throughput data into knowledge of both developmental and stem cell biology. In the past several years, a new subdiscipline of computational stem cell biology has emerged that synthesizes the modeling of systems-level aspects of stem cells with high-throughput molecular data. In this review, we provide an overview of this new field and pay particular attention to the impact that single cell transcriptomics is expected to have on our understanding of development and our ability to engineer cell fate. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Computer-aided design of biological circuits using TinkerCell.

    PubMed

    Chandran, Deepak; Bergmann, Frank T; Sauro, Herbert M

    2010-01-01

    Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze, and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field. © 2010 Landes Bioscience

  6. Computer-aided design of biological circuits using TinkerCell

    PubMed Central

    Bergmann, Frank T; Sauro, Herbert M

    2010-01-01

    Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field. PMID:21327060

  7. 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.

  8. 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

  9. 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. Copyright © 2013 Wiley Periodicals, Inc.

  10. UC Merced Center for Computational Biology Final Report

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

    Colvin, Michael; Watanabe, Masakatsu

    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 transformationmore » 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

  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.

  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. Computation of forces from deformed visco-elastic biological tissues

    NASA Astrophysics Data System (ADS)

    Muñoz, José J.; Amat, David; Conte, Vito

    2018-04-01

    We present a least-squares based inverse analysis of visco-elastic biological tissues. The proposed method computes the set of contractile forces (dipoles) at the cell boundaries that induce the observed and quantified deformations. We show that the computation of these forces requires the regularisation of the problem functional for some load configurations that we study here. The functional measures the error of the dynamic problem being discretised in time with a second-order implicit time-stepping and in space with standard finite elements. We analyse the uniqueness of the inverse problem and estimate the regularisation parameter by means of an L-curved criterion. We apply the methodology to a simple toy problem and to an in vivo set of morphogenetic deformations of the Drosophila embryo.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. Computational analyses in cognitive neuroscience: in defense of biological implausibility.

    PubMed

    Dror, I E; Gallogly, D P

    1999-06-01

    Because cognitive neuroscience researchers attempt to understand the human mind by bridging behavior and brain, they expect computational analyses to be biologically plausible. In this paper, biologically implausible computational analyses are shown to have critical and essential roles in the various stages and domains of cognitive neuroscience research. Specifically, biologically implausible computational analyses can contribute to (1) understanding and characterizing the problem that is being studied, (2) examining the availability of information and its representation, and (3) evaluating and understanding the neuronal solution. In the context of the distinct types of contributions made by certain computational analyses, the biological plausibility of those analyses is altogether irrelevant. These biologically implausible models are nevertheless relevant and important for biologically driven research.

  3. 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.

  4. 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…

  5. 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

  6. 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)

  7. 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.

  8. 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. Copyright © 2012 John Wiley & Sons, Ltd.

  9. 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.

  10. 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.

  11. The applications of computers in biological research

    NASA Technical Reports Server (NTRS)

    Wei, Jennifer

    1988-01-01

    Research in many fields could not be done without computers. There is often a great deal of technical data, even in the biological fields, that need to be analyzed. These data, unfortunately, previously absorbed much of every researcher's time. Now, due to the steady increase in computer technology, biological researchers are able to make incredible advances in their work without the added worries of tedious and difficult tasks such as the many mathematical calculations involved in today's research and health care.

  12. 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…

  13. 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)

  14. 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)

  15. Ten quick tips for machine learning in computational biology.

    PubMed

    Chicco, Davide

    2017-01-01

    Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

  16. Computation of repetitions and regularities of biologically weighted sequences.

    PubMed

    Christodoulakis, M; Iliopoulos, C; Mouchard, L; Perdikuri, K; Tsakalidis, A; Tsichlas, K

    2006-01-01

    Biological weighted sequences are used extensively in molecular biology as profiles for protein families, in the representation of binding sites and often for the representation of sequences produced by a shotgun sequencing strategy. In this paper, we address three fundamental problems in the area of biologically weighted sequences: (i) computation of repetitions, (ii) pattern matching, and (iii) computation of regularities. Our algorithms can be used as basic building blocks for more sophisticated algorithms applied on weighted sequences.

  17. 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…

  18. 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. © 2014 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).

  19. 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.

  20. Deterministic Function Computation with Chemical Reaction Networks*

    PubMed Central

    Chen, Ho-Lin; Doty, David; Soloveichik, David

    2013-01-01

    Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. CRNs are widely used to describe information processing occurring in natural cellular regulatory networks, and with upcoming advances in synthetic biology, CRNs are a promising language for the design of artificial molecular control circuitry. Nonetheless, despite the widespread use of CRNs in the natural sciences, the range of computational behaviors exhibited by CRNs is not well understood. CRNs have been shown to be efficiently Turing-universal (i.e., able to simulate arbitrary algorithms) when allowing for a small probability of error. CRNs that are guaranteed to converge on a correct answer, on the other hand, have been shown to decide only the semilinear predicates (a multi-dimensional generalization of “eventually periodic” sets). We introduce the notion of function, rather than predicate, computation by representing the output of a function f : ℕk → ℕl by a count of some molecular species, i.e., if the CRN starts with x1, …, xk molecules of some “input” species X1, …, Xk, the CRN is guaranteed to converge to having f(x1, …, xk) molecules of the “output” species Y1, …, Yl. We show that a function f : ℕk → ℕl is deterministically computed by a CRN if and only if its graph {(x, y) ∈ ℕk × ℕl ∣ f(x) = y} is a semilinear set. Finally, we show that each semilinear function f (a function whose graph is a semilinear set) can be computed by a CRN on input x in expected time O(polylog ∥x∥1). PMID:25383068

  1. A machine-learned computational functional genomics-based approach to drug classification.

    PubMed

    Lötsch, Jörn; Ultsch, Alfred

    2016-12-01

    The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug

  2. [Comparison study between biological vision and computer vision].

    PubMed

    Liu, W; Yuan, X G; Yang, C X; Liu, Z Q; Wang, R

    2001-08-01

    The development and bearing of biology vision in structure and mechanism were discussed, especially on the aspects including anatomical structure of biological vision, tentative classification of reception field, parallel processing of visual information, feedback and conformity effect of visual cortical, and so on. The new advance in the field was introduced through the study of the morphology of biological vision. Besides, comparison between biological vision and computer vision was made, and their similarities and differences were pointed out.

  3. Conservation of Dynamics Associated with Biological Function in an Enzyme Superfamily.

    PubMed

    Narayanan, Chitra; Bernard, David N; Bafna, Khushboo; Gagné, Donald; Chennubhotla, Chakra S; Doucet, Nicolas; Agarwal, Pratul K

    2018-03-06

    Enzyme superfamily members that share common chemical and/or biological functions also share common features. While the role of structure is well characterized, the link between enzyme function and dynamics is not well understood. We present a systematic characterization of intrinsic dynamics of over 20 members of the pancreatic-type RNase superfamily, which share a common structural fold. This study is motivated by the fact that the range of chemical activity as well as molecular motions of RNase homologs spans over 10 5 folds. Dynamics was characterized using a combination of nuclear magnetic resonance experiments and computer simulations. Phylogenetic clustering led to the grouping of sequences into functionally distinct subfamilies. Detailed characterization of the diverse RNases showed conserved dynamical traits for enzymes within subfamilies. These results suggest that selective pressure for the conservation of dynamical behavior, among other factors, may be linked to the distinct chemical and biological functions in an enzyme superfamily. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Phase-contrast x-ray computed tomography for biological imaging

    NASA Astrophysics Data System (ADS)

    Momose, Atsushi; Takeda, Tohoru; Itai, Yuji

    1997-10-01

    We have shown so far that 3D structures in biological sot tissues such as cancer can be revealed by phase-contrast x- ray computed tomography using an x-ray interferometer. As a next step, we aim at applications of this technique to in vivo observation, including radiographic applications. For this purpose, the size of view field is desired to be more than a few centimeters. Therefore, a larger x-ray interferometer should be used with x-rays of higher energy. We have evaluated the optimal x-ray energy from an aspect of does as a function of sample size. Moreover, desired spatial resolution to an image sensor is discussed as functions of x-ray energy and sample size, basing on a requirement in the analysis of interference fringes.

  5. 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.

  6. P43-S Computational Biology Applications Suite for High-Performance Computing (BioHPC.net)

    PubMed Central

    Pillardy, J.

    2007-01-01

    One of the challenges of high-performance computing (HPC) is user accessibility. At the Cornell University Computational Biology Service Unit, which is also a Microsoft HPC institute, we have developed a computational biology application suite that allows researchers from biological laboratories to submit their jobs to the parallel cluster through an easy-to-use Web interface. Through this system, we are providing users with popular bioinformatics tools including BLAST, HMMER, InterproScan, and MrBayes. The system is flexible and can be easily customized to include other software. It is also scalable; the installation on our servers currently processes approximately 8500 job submissions per year, many of them requiring massively parallel computations. It also has a built-in user management system, which can limit software and/or database access to specified users. TAIR, the major database of the plant model organism Arabidopsis, and SGN, the international tomato genome database, are both using our system for storage and data analysis. The system consists of a Web server running the interface (ASP.NET C#), Microsoft SQL server (ADO.NET), compute cluster running Microsoft Windows, ftp server, and file server. Users can interact with their jobs and data via a Web browser, ftp, or e-mail. The interface is accessible at http://cbsuapps.tc.cornell.edu/.

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

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

    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 inmore » 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.« less

  8. WE-DE-202-00: Connecting Radiation Physics with Computational Biology

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

    NONE

    Radiation therapy for the treatment of cancer has been established as a highly precise and effective way to eradicate a localized region of diseased tissue. To achieve further significant gains in the therapeutic ratio, we need to move towards biologically optimized treatment planning. To achieve this goal, we need to understand how the radiation-type dependent patterns of induced energy depositions within the cell (physics) connect via molecular, cellular and tissue reactions to treatment outcome such as tumor control and undesirable effects on normal tissue. Several computational biology approaches have been developed connecting physics to biology. Monte Carlo simulations are themore » most accurate method to calculate physical dose distributions at the nanometer scale, however simulations at the DNA scale are slow and repair processes are generally not simulated. Alternative models that rely on the random formation of individual DNA lesions within one or two turns of the DNA have been shown to reproduce the clusters of DNA lesions, including single strand breaks (SSBs), double strand breaks (DSBs) without the need for detailed track structure simulations. Efficient computational simulations of initial DNA damage induction facilitate computational modeling of DNA repair and other molecular and cellular processes. Mechanistic, multiscale models provide a useful conceptual framework to test biological hypotheses and help connect fundamental information about track structure and dosimetry at the sub-cellular level to dose-response effects on larger scales. In this symposium we will learn about the current state of the art of computational approaches estimating radiation damage at the cellular and sub-cellular scale. How can understanding the physics interactions at the DNA level be used to predict biological outcome? We will discuss if and how such calculations are relevant to advance our understanding of radiation damage and its repair, or, if the underlying

  9. ADAM: analysis of discrete models of biological systems using computer algebra.

    PubMed

    Hinkelmann, Franziska; Brandon, Madison; Guang, Bonny; McNeill, Rustin; Blekherman, Grigoriy; Veliz-Cuba, Alan; Laubenbacher, Reinhard

    2011-07-20

    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. 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. Discrete modeling techniques are a useful tool for analyzing complex biological systems and there is a need in the biological community for accessible efficient analysis tools. ADAM provides analysis methods based on mathematical algorithms as a web

  10. 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

  11. Functional model of biological neural networks.

    PubMed

    Lo, James Ting-Ho

    2010-12-01

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

  12. Advances in systems biology: computational algorithms and applications.

    PubMed

    Huang, Yufei; Zhao, Zhongming; Xu, Hua; Shyr, Yu; Zhang, Bing

    2012-01-01

    The 2012 International Conference on Intelligent Biology and Medicine (ICIBM 2012) was held on April 22-24, 2012 in Nashville, Tennessee, USA. The conference featured six technical sessions, one tutorial session, one workshop, and 3 keynote presentations that covered state-of-the-art research activities in genomics, systems biology, and intelligent computing. In addition to a major emphasis on the next generation sequencing (NGS)-driven informatics, ICIBM 2012 aligned significant interests in systems biology and its applications in medicine. We highlight in this editorial the selected papers from the meeting that address the developments of novel algorithms and applications in systems biology.

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

    PubMed

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

    2017-07-03

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

  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. The ISCB Student Council Internship Program: Expanding computational biology capacity worldwide

    PubMed Central

    Anupama, Jigisha; Shanmugam, Avinash Kumar; Santos, Alberto; Michaut, Magali

    2018-01-01

    Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one’s field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one’s research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program. PMID:29346365

  16. The ISCB Student Council Internship Program: Expanding computational biology capacity worldwide.

    PubMed

    Anupama, Jigisha; Francescatto, Margherita; Rahman, Farzana; Fatima, Nazeefa; DeBlasio, Dan; Shanmugam, Avinash Kumar; Satagopam, Venkata; Santos, Alberto; Kolekar, Pandurang; Michaut, Magali; Guney, Emre

    2018-01-01

    Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one's field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one's research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.

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

    PubMed Central

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

    2001-01-01

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

  18. Automated quantitative assessment of proteins' biological function in protein knowledge bases.

    PubMed

    Mayr, Gabriele; Lepperdinger, Günter; Lackner, Peter

    2008-01-01

    Primary protein sequence data are archived in databases together with information regarding corresponding biological functions. In this respect, UniProt/Swiss-Prot is currently the most comprehensive collection and it is routinely cross-examined when trying to unravel the biological role of hypothetical proteins. Bioscientists frequently extract single entries and further evaluate those on a subjective basis. In lieu of a standardized procedure for scoring the existing knowledge regarding individual proteins, we here report about a computer-assisted method, which we applied to score the present knowledge about any given Swiss-Prot entry. Applying this quantitative score allows the comparison of proteins with respect to their sequence yet highlights the comprehension of functional data. pfs analysis may be also applied for quality control of individual entries or for database management in order to rank entry listings.

  19. OpenStructure: a flexible software framework for computational structural biology.

    PubMed

    Biasini, Marco; Mariani, Valerio; Haas, Jürgen; Scheuber, Stefan; Schenk, Andreas D; Schwede, Torsten; Philippsen, Ansgar

    2010-10-15

    Developers of new methods in computational structural biology are often hampered in their research by incompatible software tools and non-standardized data formats. To address this problem, we have developed OpenStructure as a modular open source platform to provide a powerful, yet flexible general working environment for structural bioinformatics. OpenStructure consists primarily of a set of libraries written in C++ with a cleanly designed application programmer interface. All functionality can be accessed directly in C++ or in a Python layer, meeting both the requirements for high efficiency and ease of use. Powerful selection queries and the notion of entity views to represent these selections greatly facilitate the development and implementation of algorithms on structural data. The modular integration of computational core methods with powerful visualization tools makes OpenStructure an ideal working and development environment. Several applications, such as the latest versions of IPLT and QMean, have been implemented based on OpenStructure-demonstrating its value for the development of next-generation structural biology algorithms. Source code licensed under the GNU lesser general public license and binaries for MacOS X, Linux and Windows are available for download at http://www.openstructure.org. torsten.schwede@unibas.ch Supplementary data are available at Bioinformatics online.

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

    PubMed Central

    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. PMID:24688643

  1. 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

  2. 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

  3. Converting differential-equation models of biological systems to membrane computing.

    PubMed

    Muniyandi, Ravie Chandren; Zin, Abdullah Mohd; Sanders, J W

    2013-12-01

    This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand-receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  4. 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.

  5. 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…

  6. 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

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

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

    Tavare, S.

    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.

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

    PubMed Central

    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). PMID:26097686

  9. 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).

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

    PubMed Central

    Bentley, Peter J.

    2009-01-01

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

  11. 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

  12. 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

  13. 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…

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

    PubMed

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

    2016-02-01

    The overall aim of physiological research is to understand how living systems function in an integrative manner. Consequently, the discipline of physiology has since its infancy attempted to link multiple levels of biological organization. Increasingly this has involved mathematical and computational approaches, typically to model a small number of components spanning several levels of biological organization. With the advent of "omics" technologies, which can characterize the molecular state of a cell or tissue (intended as the level of expression and/or activity of its molecular components), the number of molecular components we can quantify has increased exponentially. Paradoxically, the unprecedented amount of experimental data has made it more difficult to derive conceptual models underlying essential mechanisms regulating mammalian physiology. We present an overview of state-of-the-art methods currently used to identifying biological networks underlying genomewide responses. These are based on a data-driven approach that relies on advanced computational methods designed to "learn" biology from observational data. In this review, we illustrate an application of these computational methodologies using a case study integrating an in vivo model representing the transcriptional state of hypoxic skeletal muscle with a clinical study representing muscle wasting in chronic obstructive pulmonary disease patients. The broader application of these approaches to modeling multiple levels of biological data in the context of modern physiology is discussed. Copyright © 2016 the American Physiological Society.

  15. 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. © 2016 The Authors. Published under the terms of the CC BY 4.0 license.

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

    PubMed

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

    2013-01-01

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

  17. 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

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

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

    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 tomore » make use of the new data.3« less

  19. Textual data compression in computational biology: a synopsis.

    PubMed

    Giancarlo, Raffaele; Scaturro, Davide; Utro, Filippo

    2009-07-01

    Textual data compression, and the associated techniques coming from information theory, are often perceived as being of interest for data communication and storage. However, they are also deeply related to classification and data mining and analysis. In recent years, a substantial effort has been made for the application of textual data compression techniques to various computational biology tasks, ranging from storage and indexing of large datasets to comparison and reverse engineering of biological networks. The main focus of this review is on a systematic presentation of the key areas of bioinformatics and computational biology where compression has been used. When possible, a unifying organization of the main ideas and techniques is also provided. It goes without saying that most of the research results reviewed here offer software prototypes to the bioinformatics community. The Supplementary Material provides pointers to software and benchmark datasets for a range of applications of broad interest. In addition to provide reference to software, the Supplementary Material also gives a brief presentation of some fundamental results and techniques related to this paper. It is at: http://www.math.unipa.it/ approximately raffaele/suppMaterial/compReview/

  20. 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.

  1. Where next for the reproducibility agenda in computational biology?

    PubMed

    Lewis, Joanna; Breeze, Charles E; Charlesworth, Jane; Maclaren, Oliver J; Cooper, Jonathan

    2016-07-15

    The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. We use case studies from our own research experience to illustrate how concepts of reproducibility might be applied in computational biology. Several fields have developed 'minimum information' checklists to support the full reporting of computational simulations, analyses and results, and standardised data formats and model description languages can facilitate the use of multiple systems to address the same research question. We note the importance of defining the key features of a result to be reproduced, and the expected agreement between original and subsequent results. Dynamic, updatable tools for publishing methods and results are becoming increasingly common, but sometimes come at the cost of clear communication. In general, the reproducibility of computational research is improving but would benefit from additional resources and incentives. We conclude with a series of linked recommendations for improving reproducibility in computational biology through communication, policy, education and research practice. More reproducible research will lead to higher quality conclusions, deeper understanding and more valuable knowledge.

  2. Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

    PubMed

    Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E; Conolly, Rory B

    2010-02-01

    The new paradigm envisioned for toxicity testing in the 21st century advocates shifting from the current animal-based testing process to a combination of in vitro cell-based studies, high-throughput techniques, and in silico modeling. A strategic component of the vision is the adoption of the systems biology approach to acquire, analyze, and interpret toxicity pathway data. As key toxicity pathways are identified and their wiring details elucidated using traditional and high-throughput techniques, there is a pressing need to understand their qualitative and quantitative behaviors in response to perturbation by both physiological signals and exogenous stressors. The complexity of these molecular networks makes the task of understanding cellular responses merely by human intuition challenging, if not impossible. This process can be aided by mathematical modeling and computer simulation of the networks and their dynamic behaviors. A number of theoretical frameworks were developed in the last century for understanding dynamical systems in science and engineering disciplines. These frameworks, which include metabolic control analysis, biochemical systems theory, nonlinear dynamics, and control theory, can greatly facilitate the process of organizing, analyzing, and understanding toxicity pathways. Such analysis will require a comprehensive examination of the dynamic properties of "network motifs"--the basic building blocks of molecular circuits. Network motifs like feedback and feedforward loops appear repeatedly in various molecular circuits across cell types and enable vital cellular functions like homeostasis, all-or-none response, memory, and biological rhythm. These functional motifs and associated qualitative and quantitative properties are the predominant source of nonlinearities observed in cellular dose response data. Complex response behaviors can arise from toxicity pathways built upon combinations of network motifs. While the field of computational cell

  3. A Unifying Theory of Biological Function.

    PubMed

    van Hateren, J H

    2017-01-01

    A new theory that naturalizes biological function is explained and compared with earlier etiological and causal role theories. Etiological (or selected effects) theories explain functions from how they are caused over their evolutionary history. Causal role theories analyze how functional mechanisms serve the current capacities of their containing system. The new proposal unifies the key notions of both kinds of theories, but goes beyond them by explaining how functions in an organism can exist as factors with autonomous causal efficacy. The goal-directedness and normativity of functions exist in this strict sense as well. The theory depends on an internal physiological or neural process that mimics an organism's fitness, and modulates the organism's variability accordingly. The structure of the internal process can be subdivided into subprocesses that monitor specific functions in an organism. The theory matches well with each intuition on a previously published list of intuited ideas about biological functions, including intuitions that have posed difficulties for other theories.

  4. Computational intelligence approaches for pattern discovery in biological systems.

    PubMed

    Fogel, Gary B

    2008-07-01

    Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

  5. Implementation of logic functions and computations by chemical kinetics

    NASA Astrophysics Data System (ADS)

    Hjelmfelt, A.; Ross, J.

    We review our work on the computational functions of the kinetics of chemical networks. We examine spatially homogeneous networks which are based on prototypical reactions occurring in living cells and show the construction of logic gates and sequential and parallel networks. This work motivates the study of an important biochemical pathway, glycolysis, and we demonstrate that the switch that controls the flux in the direction of glycolysis or gluconeogenesis may be described as a fuzzy AND operator. We also study a spatially inhomogeneous network which shares features of theoretical and biological neural networks.

  6. Computational analysis of microRNA function in heart development.

    PubMed

    Liu, Ganqiang; Ding, Min; Chen, Jiajia; Huang, Jinyan; Wang, Haiyun; Jing, Qing; Shen, Bairong

    2010-09-01

    Emerging evidence suggests that specific spatio-temporal microRNA (miRNA) expression is required for heart development. In recent years, hundreds of miRNAs have been discovered. In contrast, functional annotations are available only for a very small fraction of these regulatory molecules. In order to provide a global perspective for the biologists who study the relationship between differentially expressed miRNAs and heart development, we employed computational analysis to uncover the specific cellular processes and biological pathways targeted by miRNAs in mouse heart development. Here, we utilized Gene Ontology (GO) categories, KEGG Pathway, and GeneGo Pathway Maps as a gene functional annotation system for miRNA target enrichment analysis. The target genes of miRNAs were found to be enriched in functional categories and pathway maps in which miRNAs could play important roles during heart development. Meanwhile, we developed miRHrt (http://sysbio.suda.edu.cn/mirhrt/), a database aiming to provide a comprehensive resource of miRNA function in regulating heart development. These computational analysis results effectively illustrated the correlation of differentially expressed miRNAs with cellular functions and heart development. We hope that the identified novel heart development-associated pathways and the database presented here would facilitate further understanding of the roles and mechanisms of miRNAs in heart development.

  7. Computational Tools to Assess Turbine Biological Performance

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

    Richmond, Marshall C.; Serkowski, John A.; Rakowski, Cynthia 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, amore » 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.« less

  8. 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…

  9. Modeling biological problems in computer science: a case study in genome assembly.

    PubMed

    Medvedev, Paul

    2018-01-30

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

  10. Elastic Multi-scale Mechanisms: Computation and Biological Evolution.

    PubMed

    Diaz Ochoa, Juan G

    2018-01-01

    Explanations based on low-level interacting elements are valuable and powerful since they contribute to identify the key mechanisms of biological functions. However, many dynamic systems based on low-level interacting elements with unambiguous, finite, and complete information of initial states generate future states that cannot be predicted, implying an increase of complexity and open-ended evolution. Such systems are like Turing machines, that overlap with dynamical systems that cannot halt. We argue that organisms find halting conditions by distorting these mechanisms, creating conditions for a constant creativity that drives evolution. We introduce a modulus of elasticity to measure the changes in these mechanisms in response to changes in the computed environment. We test this concept in a population of predators and predated cells with chemotactic mechanisms and demonstrate how the selection of a given mechanism depends on the entire population. We finally explore this concept in different frameworks and postulate that the identification of predictive mechanisms is only successful with small elasticity modulus.

  11. A design automation framework for computational bioenergetics in biological networks.

    PubMed

    Angione, Claudio; Costanza, Jole; Carapezza, Giovanni; Lió, Pietro; Nicosia, Giuseppe

    2013-10-01

    The bioenergetic activity of mitochondria can be thoroughly investigated by using computational methods. In particular, in our work we focus on ATP and NADH, namely the metabolites representing the production of energy in the cell. We develop a computational framework to perform an exhaustive investigation at the level of species, reactions, genes and metabolic pathways. The framework integrates several methods implementing the state-of-the-art algorithms for many-objective optimization, sensitivity, and identifiability analysis applied to biological systems. We use this computational framework to analyze three case studies related to the human mitochondria and the algal metabolism of Chlamydomonas reinhardtii, formally described with algebraic differential equations or flux balance analysis. Integrating the results of our framework applied to interacting organelles would provide a general-purpose method for assessing the production of energy in a biological network.

  12. 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…

  13. Bacteria as computers making computers

    PubMed Central

    Danchin, Antoine

    2009-01-01

    Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments. PMID:19016882

  14. Bacteria as computers making computers.

    PubMed

    Danchin, Antoine

    2009-01-01

    Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

  15. A Contribution of the Computer to Biology Education at the University.

    ERIC Educational Resources Information Center

    Anxolabehere, D.; And Others

    1980-01-01

    Described is part of the O.P.E. laboratory computer-based biology program designed for undergraduate medical and biology students. Described is an embryology dialogue in which the student proceeds through three stages in the knowledge and understanding of the concept of competence. (Author/DS)

  16. 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

  17. A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease.

    PubMed

    Zanzoni, Andreas

    2016-01-01

    Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies.

  18. Chromatin Computation

    PubMed Central

    Bryant, Barbara

    2012-01-01

    In living cells, DNA is packaged along with protein and RNA into chromatin. Chemical modifications to nucleotides and histone proteins are added, removed and recognized by multi-functional molecular complexes. Here I define a new computational model, in which chromatin modifications are information units that can be written onto a one-dimensional string of nucleosomes, analogous to the symbols written onto cells of a Turing machine tape, and chromatin-modifying complexes are modeled as read-write rules that operate on a finite set of adjacent nucleosomes. I illustrate the use of this “chromatin computer” to solve an instance of the Hamiltonian path problem. I prove that chromatin computers are computationally universal – and therefore more powerful than the logic circuits often used to model transcription factor control of gene expression. Features of biological chromatin provide a rich instruction set for efficient computation of nontrivial algorithms in biological time scales. Modeling chromatin as a computer shifts how we think about chromatin function, suggests new approaches to medical intervention, and lays the groundwork for the engineering of a new class of biological computing machines. PMID:22567109

  19. High performance computing in biology: multimillion atom simulations of nanoscale systems

    PubMed Central

    Sanbonmatsu, K. Y.; Tung, C.-S.

    2007-01-01

    Computational methods have been used in biology for sequence analysis (bioinformatics), all-atom simulation (molecular dynamics and quantum calculations), and more recently for modeling biological networks (systems biology). Of these three techniques, all-atom simulation is currently the most computationally demanding, in terms of compute load, communication speed, and memory load. Breakthroughs in electrostatic force calculation and dynamic load balancing have enabled molecular dynamics simulations of large biomolecular complexes. Here, we report simulation results for the ribosome, using approximately 2.64 million atoms, the largest all-atom biomolecular simulation published to date. Several other nanoscale systems with different numbers of atoms were studied to measure the performance of the NAMD molecular dynamics simulation program on the Los Alamos National Laboratory Q Machine. We demonstrate that multimillion atom systems represent a 'sweet spot' for the NAMD code on large supercomputers. NAMD displays an unprecedented 85% parallel scaling efficiency for the ribosome system on 1024 CPUs. We also review recent targeted molecular dynamics simulations of the ribosome that prove useful for studying conformational changes of this large biomolecular complex in atomic detail. PMID:17187988

  20. Distinguishing between "function" and "effect" in genome biology.

    PubMed

    Doolittle, W Ford; Brunet, Tyler D P; Linquist, Stefan; Gregory, T Ryan

    2014-05-09

    Much confusion in genome biology results from conflation of possible meanings of the word "function." We suggest that, in this connection, attention should be paid to evolutionary biologists and philosophers who have previously dealt with this problem. We need only decide that although all genomic structures have effects, only some of them should be said to have functions. Although it will very often be difficult or impossible to establish function (strictly defined), it should not automatically be assumed. We enjoin genomicists in particular to pay greater attention to parsing biological effects. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  1. Computing Functions by Approximating the Input

    ERIC Educational Resources Information Center

    Goldberg, Mayer

    2012-01-01

    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…

  2. Multi-scale computational modeling of developmental biology.

    PubMed

    Setty, Yaki

    2012-08-01

    Normal development of multicellular organisms is regulated by a highly complex process in which a set of precursor cells proliferate, differentiate and move, forming over time a functioning tissue. To handle their complexity, developmental systems can be studied over distinct scales. The dynamics of each scale is determined by the collective activity of entities at the scale below it. I describe a multi-scale computational approach for modeling developmental systems and detail the methodology through a synthetic example of a developmental system that retains key features of real developmental systems. I discuss the simulation of the system as it emerges from cross-scale and intra-scale interactions and describe how an in silico study can be carried out by modifying these interactions in a way that mimics in vivo experiments. I highlight biological features of the results through a comparison with findings in Caenorhabditis elegans germline development and finally discuss about the applications of the approach in real developmental systems and propose future extensions. The source code of the model of the synthetic developmental system can be found in www.wisdom.weizmann.ac.il/~yaki/MultiScaleModel. yaki.setty@gmail.com Supplementary data are available at Bioinformatics online.

  3. 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…

  4. 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

  5. Using Computer Technology to Create a Revolutionary New Style of Biology.

    ERIC Educational Resources Information Center

    Monaghan, Peter

    1993-01-01

    A $13-million gift of William Gates III to the University of Washington has enabled establishment of the country's first department in molecular biotechnology, a combination of medicine and molecular biology to be practiced by researchers versed in a variety of fields, including computer science, computation, applied physics, and engineering. (MSE)

  6. MACBenAbim: A Multi-platform Mobile Application for searching keyterms in Computational Biology and Bioinformatics.

    PubMed

    Oluwagbemi, Olugbenga O; Adewumi, Adewole; Esuruoso, Abimbola

    2012-01-01

    Computational biology and bioinformatics are gradually gaining grounds in Africa and other developing nations of the world. However, in these countries, some of the challenges of computational biology and bioinformatics education are inadequate infrastructures, and lack of readily-available complementary and motivational tools to support learning as well as research. This has lowered the morale of many promising undergraduates, postgraduates and researchers from aspiring to undertake future study in these fields. In this paper, we developed and described MACBenAbim (Multi-platform Mobile Application for Computational Biology and Bioinformatics), a flexible user-friendly tool to search for, define and describe the meanings of keyterms in computational biology and bioinformatics, thus expanding the frontiers of knowledge of the users. This tool also has the capability of achieving visualization of results on a mobile multi-platform context. MACBenAbim is available from the authors for non-commercial purposes.

  7. 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

  8. Computational Models for Calcium-Mediated Astrocyte Functions.

    PubMed

    Manninen, Tiina; Havela, Riikka; Linne, Marja-Leena

    2018-01-01

    The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro , but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus

  9. Computational Models for Calcium-Mediated Astrocyte Functions

    PubMed Central

    Manninen, Tiina; Havela, Riikka; Linne, Marja-Leena

    2018-01-01

    The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus

  10. Discovery of novel bacterial toxins by genomics and computational biology.

    PubMed

    Doxey, Andrew C; Mansfield, Michael J; Montecucco, Cesare

    2018-06-01

    Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes. Copyright © 2018. Published by Elsevier Ltd.

  11. 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

  12. Computing chemical organizations in biological networks.

    PubMed

    Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter

    2008-07-15

    Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.

  13. 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

  14. 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

  15. From Jabberwocky to genome: Lewis Carroll and computational biology.

    PubMed

    Searls, D B

    2001-01-01

    In addition to his literary output, Lewis Carroll created a vast range of games and puzzles that depend upon wordplay of various kinds, especially the manipulation of alphabetic symbols in diverse contexts. Such wordplay reveals a turn of mind well suited to methodologies used in modern computational biology.

  16. 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…

  17. 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…

  18. Community-driven computational biology with Debian Linux.

    PubMed

    Möller, Steffen; Krabbenhöft, Hajo Nils; Tille, Andreas; Paleino, David; Williams, Alan; Wolstencroft, Katy; Goble, Carole; Holland, Richard; Belhachemi, Dominique; Plessy, Charles

    2010-12-21

    The Open Source movement and its technologies are popular in the bioinformatics community because they provide freely available tools and resources for research. In order to feed the steady demand for updates on software and associated data, a service infrastructure is required for sharing and providing these tools to heterogeneous computing environments. The Debian Med initiative provides ready and coherent software packages for medical informatics and bioinformatics. These packages can be used together in Taverna workflows via the UseCase plugin to manage execution on local or remote machines. If such packages are available in cloud computing environments, the underlying hardware and the analysis pipelines can be shared along with the software. Debian Med closes the gap between developers and users. It provides a simple method for offering new releases of software and data resources, thus provisioning a local infrastructure for computational biology. For geographically distributed teams it can ensure they are working on the same versions of tools, in the same conditions. This contributes to the world-wide networking of researchers.

  19. Community-driven computational biology with Debian Linux

    PubMed Central

    2010-01-01

    Background The Open Source movement and its technologies are popular in the bioinformatics community because they provide freely available tools and resources for research. In order to feed the steady demand for updates on software and associated data, a service infrastructure is required for sharing and providing these tools to heterogeneous computing environments. Results The Debian Med initiative provides ready and coherent software packages for medical informatics and bioinformatics. These packages can be used together in Taverna workflows via the UseCase plugin to manage execution on local or remote machines. If such packages are available in cloud computing environments, the underlying hardware and the analysis pipelines can be shared along with the software. Conclusions Debian Med closes the gap between developers and users. It provides a simple method for offering new releases of software and data resources, thus provisioning a local infrastructure for computational biology. For geographically distributed teams it can ensure they are working on the same versions of tools, in the same conditions. This contributes to the world-wide networking of researchers. PMID:21210984

  20. Agents in bioinformatics, computational and systems biology.

    PubMed

    Merelli, Emanuela; Armano, Giuliano; Cannata, Nicola; Corradini, Flavio; d'Inverno, Mark; Doms, Andreas; Lord, Phillip; Martin, Andrew; Milanesi, Luciano; Möller, Steffen; Schroeder, Michael; Luck, Michael

    2007-01-01

    The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

  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. 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…

  3. 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.

  4. 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...

  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. Biological adaptations for functional features of language in the face of cultural evolution.

    PubMed

    Christiansen, Morten H; Reali, Florencia; Chater, Nick

    2011-04-01

    Although there may be no true language universals, it is nonetheless possible to discern several family resemblance patterns across the languages of the world. Recent work on the cultural evolution of language indicates the source of these patterns is unlikely to be an innate universal grammar evolved through biological adaptations for arbitrary linguistic features. Instead, it has been suggested that the patterns of resemblance emerge because language has been shaped by the brain, with individual languages representing different but partially overlapping solutions to the same set of nonlinguistic constraints. Here, we use computational simulations to investigate whether biological adaptation for functional features of language, deriving from cognitive and communicative constraints, may nonetheless be possible alongside rapid cultural evolution. Specifically, we focus on the Baldwin effect as an evolutionary mechanism by which previously learned linguistic features might become innate through natural selection across many generations of language users. The results indicate that cultural evolution of language does not necessarily prevent functional features of language from becoming genetically fixed, thus potentially providing a particularly informative source of constraints on cross-linguistic resemblance patterns.

  7. 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.

  8. Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets.

    PubMed

    Xu, Weijun; Lucke, Andrew J; Fairlie, David P

    2015-04-01

    Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Automatic computation of transfer functions

    DOEpatents

    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.

  10. On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

    PubMed

    Gomez-Ramirez, Jaime; Sanz, Ricardo

    2013-09-01

    One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. 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

  12. 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.

  13. Functional Programming in Computer Science

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

    Anderson, Loren James; Davis, Marion Kei

    We explore functional programming through a 16-week internship at Los Alamos National Laboratory. Functional programming is a branch of computer science that has exploded in popularity over the past decade due to its high-level syntax, ease of parallelization, and abundant applications. First, we summarize functional programming by listing the advantages of functional programming languages over the usual imperative languages, and we introduce the concept of parsing. Second, we discuss the importance of lambda calculus in the theory of functional programming. Lambda calculus was invented by Alonzo Church in the 1930s to formalize the concept of effective computability, and every functionalmore » language is essentially some implementation of lambda calculus. Finally, we display the lasting products of the internship: additions to a compiler and runtime system for the pure functional language STG, including both a set of tests that indicate the validity of updates to the compiler and a compiler pass that checks for illegal instances of duplicate names.« less

  14. Computers and the Learning of Biological Concepts: Attitudes and Achievement of Nigerian Students.

    ERIC Educational Resources Information Center

    Jegede, Olugbemiro J.

    1991-01-01

    Compared attitudes toward computer use and achievement in biology for three groups of Nigerian students (n=64): (1) working alone with computer; (2) working in groups of three on the computer; (3) and a control group that received normal instruction (lecture). Students in the second group had the highest scores on attitude. No significant…

  15. 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.

  16. 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.

  17. 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.

  18. 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 parameter...

  19. 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 parameter...

  20. 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 parameter...

  1. 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 parameter...

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

    PubMed Central

    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. PMID:25708611

  3. Integer Linear Programming in Computational Biology

    NASA Astrophysics Data System (ADS)

    Althaus, Ernst; Klau, Gunnar W.; Kohlbacher, Oliver; Lenhof, Hans-Peter; Reinert, Knut

    Computational molecular biology (bioinformatics) is a young research field that is rich in NP-hard optimization problems. The problem instances encountered are often huge and comprise thousands of variables. Since their introduction into the field of bioinformatics in 1997, integer linear programming (ILP) techniques have been successfully applied to many optimization problems. These approaches have added much momentum to development and progress in related areas. In particular, ILP-based approaches have become a standard optimization technique in bioinformatics. In this review, we present applications of ILP-based techniques developed by members and former members of Kurt Mehlhorn’s group. These techniques were introduced to bioinformatics in a series of papers and popularized by demonstration of their effectiveness and potential.

  4. Efficient randomization of biological networks while preserving functional characterization of individual nodes.

    PubMed

    Iorio, Francesco; Bernardo-Faura, Marti; Gobbi, Andrea; Cokelaer, Thomas; Jurman, Giuseppe; Saez-Rodriguez, Julio

    2016-12-20

    Networks are popular and powerful tools to describe and model biological processes. Many computational methods have been developed to infer biological networks from literature, high-throughput experiments, and combinations of both. Additionally, a wide range of tools has been developed to map experimental data onto reference biological networks, in order to extract meaningful modules. Many of these methods assess results' significance against null distributions of randomized networks. However, these standard unconstrained randomizations do not preserve the functional characterization of the nodes in the reference networks (i.e. their degrees and connection signs), hence including potential biases in the assessment. Building on our previous work about rewiring bipartite networks, we propose a method for rewiring any type of unweighted networks. In particular we formally demonstrate that the problem of rewiring a signed and directed network preserving its functional connectivity (F-rewiring) reduces to the problem of rewiring two induced bipartite networks. Additionally, we reformulate the lower bound to the iterations' number of the switching-algorithm to make it suitable for the F-rewiring of networks of any size. Finally, we present BiRewire3, an open-source Bioconductor package enabling the F-rewiring of any type of unweighted network. We illustrate its application to a case study about the identification of modules from gene expression data mapped on protein interaction networks, and a second one focused on building logic models from more complex signed-directed reference signaling networks and phosphoproteomic data. BiRewire3 it is freely available at https://www.bioconductor.org/packages/BiRewire/ , and it should have a broad application as it allows an efficient and analytically derived statistical assessment of results from any network biology tool.

  5. 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.

  6. 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

  7. Synthetic analog computation in living cells.

    PubMed

    Daniel, Ramiz; Rubens, Jacob R; Sarpeshkar, Rahul; Lu, Timothy K

    2013-05-30

    A central goal of synthetic biology is to achieve multi-signal integration and processing in living cells for diagnostic, therapeutic and biotechnology applications. Digital logic has been used to build small-scale circuits, but other frameworks may be needed for efficient computation in the resource-limited environments of cells. Here we demonstrate that synthetic analog gene circuits can be engineered to execute sophisticated computational functions in living cells using just three transcription factors. Such synthetic analog gene circuits exploit feedback to implement logarithmically linear sensing, addition, ratiometric and power-law computations. The circuits exhibit Weber's law behaviour as in natural biological systems, operate over a wide dynamic range of up to four orders of magnitude and can be designed to have tunable transfer functions. Our circuits can be composed to implement higher-order functions that are well described by both intricate biochemical models and simple mathematical functions. By exploiting analog building-block functions that are already naturally present in cells, this approach efficiently implements arithmetic operations and complex functions in the logarithmic domain. Such circuits may lead to new applications for synthetic biology and biotechnology that require complex computations with limited parts, need wide-dynamic-range biosensing or would benefit from the fine control of gene expression.

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

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

    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 previousmore » 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.« less

  9. System biology of gene regulation.

    PubMed

    Baitaluk, Michael

    2009-01-01

    A famous joke story that exhibits the traditionally awkward alliance between theory and experiment and showing the differences between experimental biologists and theoretical modelers is when a University sends a biologist, a mathematician, a physicist, and a computer scientist to a walking trip in an attempt to stimulate interdisciplinary research. During a break, they watch a cow in a field nearby and the leader of the group asks, "I wonder how one could decide on the size of a cow?" Since a cow is a biological object, the biologist responded first: "I have seen many cows in this area and know it is a big cow." The mathematician argued, "The true volume is determined by integrating the mathematical function that describes the outer surface of the cow's body." The physicist suggested: "Let's assume the cow is a sphere...." Finally the computer scientist became nervous and said that he didn't bring his computer because there is no Internet connection up there on the hill. In this humorous but explanatory story suggestions proposed by theorists can be taken to reflect the view of many experimental biologists that computer scientists and theorists are too far removed from biological reality and therefore their theories and approaches are not of much immediate usefulness. Conversely, the statement of the biologist mirrors the view of many traditional theoretical and computational scientists that biological experiments are for the most part simply descriptive, lack rigor, and that much of the resulting biological data are of questionable functional relevance. One of the goals of current biology as a multidisciplinary science is to bring people from different scientific areas together on the same "hill" and teach them to speak the same "language." In fact, of course, when presenting their data, most experimentalist biologists do provide an interpretation and explanation for the results, and many theorists/computer scientists aim to answer (or at least to fully describe

  10. Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia.

    PubMed

    Yang, Xinan Holly; Li, Meiyi; Wang, Bin; Zhu, Wanqi; Desgardin, Aurelie; Onel, Kenan; de Jong, Jill; Chen, Jianjun; Chen, Luonan; Cunningham, John M

    2015-03-24

    Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational "gene-to-function, snapshot-to-dynamics, and biology-to-clinic" framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform. We introduced an adjustable "soft threshold" to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08). The proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about

  11. RATGRAPH: Computer Graphing of Rational Functions.

    ERIC Educational Resources Information Center

    Minch, Bradley A.

    1987-01-01

    Presents an easy-to-use Applesoft BASIC program that graphs rational functions and any asymptotes that the functions might have. Discusses the nature of rational functions, graphing them manually, employing a computer to graph rational functions, and describes how the program works. (TW)

  12. 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.

  13. Computational adaptive optics for broadband optical interferometric tomography of biological tissue

    NASA Astrophysics Data System (ADS)

    Boppart, Stephen A.

    2015-03-01

    High-resolution real-time tomography of biological tissues is important for many areas of biological investigations and medical applications. Cellular level optical tomography, however, has been challenging because of the compromise between transverse imaging resolution and depth-of-field, the system and sample aberrations that may be present, and the low imaging sensitivity deep in scattering tissues. The use of computed optical imaging techniques has the potential to address several of these long-standing limitations and challenges. Two related techniques are interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO). Through three-dimensional Fourierdomain resampling, in combination with high-speed OCT, ISAM can be used to achieve high-resolution in vivo tomography with enhanced depth sensitivity over a depth-of-field extended by more than an order-of-magnitude, in realtime. Subsequently, aberration correction with CAO can be performed in a tomogram, rather than to the optical beam of a broadband optical interferometry system. Based on principles of Fourier optics, aberration correction with CAO is performed on a virtual pupil using Zernike polynomials, offering the potential to augment or even replace the more complicated and expensive adaptive optics hardware with algorithms implemented on a standard desktop computer. Interferometric tomographic reconstructions are characterized with tissue phantoms containing sub-resolution scattering particles, and in both ex vivo and in vivo biological tissue. This review will collectively establish the foundation for high-speed volumetric cellular-level optical interferometric tomography in living tissues.

  14. Message from the ISCB: 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

    2015-02-15

    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 and three-dimensional 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$2000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology 2016, Orlando, FL). dkovats@iscb.org or rost@in.tum.de. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  15. The physical characteristics of human proteins in different biological functions.

    PubMed

    Wang, Tengjiao; Tang, Hailin

    2017-01-01

    The physical properties of gene products are the foundation of their biological functions. In this study, we systematically explored relationships between physical properties and biological functions. The physical properties including origin time, evolution pressure, mRNA and protein stability, molecular weight, hydrophobicity, acidity/alkaline, amino acid compositions, and chromosome location. The biological functions are defined from 4 aspects: biological process, molecular function, cellular component and cell/tissue/organ expression. We found that the proteins associated with basic material and energy metabolism process originated earlier, while the proteins associated with immune, neurological system process etc. originated later. Tissues may have a strong influence on evolution pressure. The proteins associated with energy metabolism are double-stable. Immune and peripheral cell proteins tend to be mRNA stable/protein unstable. There are very few function items with double-unstable of mRNA and protein. The proteins involved in the cell adhesion tend to consist of large proteins with high proportion of small amino acids. The proteins of organic acid transport, neurological system process and amine transport have significantly high hydrophobicity. Interestingly, the proteins involved in olfactory receptor activity tend to have high frequency of aromatic, sulfuric and hydroxyl amino acids.

  16. The physical characteristics of human proteins in different biological functions

    PubMed Central

    Tang, Hailin

    2017-01-01

    The physical properties of gene products are the foundation of their biological functions. In this study, we systematically explored relationships between physical properties and biological functions. The physical properties including origin time, evolution pressure, mRNA and protein stability, molecular weight, hydrophobicity, acidity/alkaline, amino acid compositions, and chromosome location. The biological functions are defined from 4 aspects: biological process, molecular function, cellular component and cell/tissue/organ expression. We found that the proteins associated with basic material and energy metabolism process originated earlier, while the proteins associated with immune, neurological system process etc. originated later. Tissues may have a strong influence on evolution pressure. The proteins associated with energy metabolism are double-stable. Immune and peripheral cell proteins tend to be mRNA stable/protein unstable. There are very few function items with double-unstable of mRNA and protein. The proteins involved in the cell adhesion tend to consist of large proteins with high proportion of small amino acids. The proteins of organic acid transport, neurological system process and amine transport have significantly high hydrophobicity. Interestingly, the proteins involved in olfactory receptor activity tend to have high frequency of aromatic, sulfuric and hydroxyl amino acids. PMID:28459865

  17. A biologically plausible computational model for auditory object recognition.

    PubMed

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

    2009-01-01

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

  18. Highlights from the Eighth International Society for Computational Biology (ISCB) Student Council Symposium 2012

    PubMed Central

    2012-01-01

    The report summarizes the scientific content of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB) held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) conference in Long Beach, California on July 13, 2012.

  19. Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction.

    PubMed

    Mazandu, Gaston K; Mulder, Nicola J

    2012-07-01

    Despite ever-increasing amounts of sequence and functional genomics data, there is still a deficiency of functional annotation for many newly sequenced proteins. For Mycobacterium tuberculosis (MTB), more than half of its genome is still uncharacterized, which hampers the search for new drug targets within the bacterial pathogen and limits our understanding of its pathogenicity. As for many other genomes, the annotations of proteins in the MTB proteome were generally inferred from sequence homology, which is effective but its applicability has limitations. We have carried out large-scale biological data integration to produce an MTB protein functional interaction network. Protein functional relationships were extracted from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and additional functional interactions from microarray, sequence and protein signature data. The confidence level of protein relationships in the additional functional interaction data was evaluated using a dynamic data-driven scoring system. This functional network has been used to predict functions of uncharacterized proteins using Gene Ontology (GO) terms, and the semantic similarity between these terms measured using a state-of-the-art GO similarity metric. To achieve better trade-off between improvement of quality, genomic coverage and scalability, this prediction is done by observing the key principles driving the biological organization of the functional network. This study yields a new functionally characterized MTB strain CDC1551 proteome, consisting of 3804 and 3698 proteins out of 4195 with annotations in terms of the biological process and molecular function ontologies, respectively. These data can contribute to research into the Development of effective anti-tubercular drugs with novel biological mechanisms of action. Copyright © 2011 Elsevier B.V. All rights reserved.

  20. 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.

  1. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

    PubMed

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

  2. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

    PubMed Central

    Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. PMID:27847827

  3. 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.

  4. 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

  5. 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

  6. 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…

  7. Linear-array based full-view high-resolution photoacoustic computed tomography of whole mouse brain functions in vivo

    NASA Astrophysics Data System (ADS)

    Li, Lei; Zhang, Pengfei; Wang, Lihong V.

    2018-02-01

    Photoacoustic computed tomography (PACT) is a non-invasive imaging technique offering high contrast, high resolution, and deep penetration in biological tissues. We report a photoacoustic computed tomography (PACT) system equipped with a high frequency linear array for anatomical and functional imaging of the mouse whole brain. The linear array was rotationally scanned in the coronal plane to achieve the full-view coverage. We investigated spontaneous neural activities in the deep brain by monitoring the hemodynamics and observed strong interhemispherical correlations between contralateral regions, both in the cortical layer and in the deep regions.

  8. Permeating disciplines: Overcoming barriers between molecular simulations and classical structure-function approaches in biological ion transport.

    PubMed

    Howard, Rebecca J; Carnevale, Vincenzo; Delemotte, Lucie; Hellmich, Ute A; Rothberg, Brad S

    2018-04-01

    Ion translocation across biological barriers is a fundamental requirement for life. In many cases, controlling this process-for example with neuroactive drugs-demands an understanding of rapid and reversible structural changes in membrane-embedded proteins, including ion channels and transporters. Classical approaches to electrophysiology and structural biology have provided valuable insights into several such proteins over macroscopic, often discontinuous scales of space and time. Integrating these observations into meaningful mechanistic models now relies increasingly on computational methods, particularly molecular dynamics simulations, while surfacing important challenges in data management and conceptual alignment. Here, we seek to provide contemporary context, concrete examples, and a look to the future for bridging disciplinary gaps in biological ion transport. This article is part of a Special Issue entitled: Beyond the Structure-Function Horizon of Membrane Proteins edited by Ute Hellmich, Rupak Doshi and Benjamin McIlwain. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. 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.

  10. Exploring biological interaction networks with tailored weighted quasi-bicliques

    PubMed Central

    2012-01-01

    Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks. PMID:22759421

  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. Computer modeling in developmental biology: growing today, essential tomorrow.

    PubMed

    Sharpe, James

    2017-12-01

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

  13. BLUES function method in computational physics

    NASA Astrophysics Data System (ADS)

    Indekeu, Joseph O.; Müller-Nedebock, Kristian K.

    2018-04-01

    We introduce a computational method in physics that goes ‘beyond linear use of equation superposition’ (BLUES). A BLUES function is defined as a solution of a nonlinear differential equation (DE) with a delta source that is at the same time a Green’s function for a related linear DE. For an arbitrary source, the BLUES function can be used to construct an exact solution to the nonlinear DE with a different, but related source. Alternatively, the BLUES function can be used to construct an approximate piecewise analytical solution to the nonlinear DE with an arbitrary source. For this alternative use the related linear DE need not be known. The method is illustrated in a few examples using analytical calculations and numerical computations. Areas for further applications are suggested.

  14. Optimizing high performance computing workflow for protein functional annotation

    PubMed Central

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

    2014-01-01

    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

  15. 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.

  16. Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy

    PubMed Central

    Schroll, Henning; Hamker, Fred H.

    2013-01-01

    Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other. PMID:24416002

  17. Evidence of pervasive biologically functional secondary structures within the genomes of eukaryotic single-stranded DNA viruses.

    PubMed

    Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y F; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie; Martin, Darren Patrick

    2014-02-01

    Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here.

  18. Evidence of Pervasive Biologically Functional Secondary Structures within the Genomes of Eukaryotic Single-Stranded DNA Viruses

    PubMed Central

    Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y. F.; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie

    2014-01-01

    Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here. PMID:24284329

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2016-01-01

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

  1. Parameter Estimation and Model Selection in Computational Biology

    PubMed Central

    Lillacci, Gabriele; Khammash, Mustafa

    2010-01-01

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

  2. 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.

  3. Periodicity computation of generalized mathematical biology problems involving delay differential equations.

    PubMed

    Jasim Mohammed, M; Ibrahim, Rabha W; Ahmad, M Z

    2017-03-01

    In this paper, we consider a low initial population model. Our aim is to study the periodicity computation of this model by using neutral differential equations, which are recognized in various studies including biology. We generalize the neutral Rayleigh equation for the third-order by exploiting the model of fractional calculus, in particular the Riemann-Liouville differential operator. We establish the existence and uniqueness of a periodic computational outcome. The technique depends on the continuation theorem of the coincidence degree theory. Besides, an example is presented to demonstrate the finding.

  4. [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.

  5. Biodiversity, ecosystem functioning, and classical biological control.

    PubMed

    Evans, Edward W

    Increasing concern over worldwide loss of biodiversity has led ecologists to focus intently on how ecosystem functioning may depend on diversity. In applied entomology, there is longstanding interest in the issue, especially as regards the importance of natural enemy diversity for pest control. Here I review parallels in interest, conceptual framework, and conclusions concerning biodiversity as it affects ecosystem functioning in general and classical biological control in particular. Whereas the former focuses on implications of loss of diversity, the latter focuses on implications of increase in diversity as additional species of natural enemies are introduced to novel communities in new geographic regions for insect pest and weed control. Many field studies now demonstrate that ecosystem functioning, e.g., as reflected in primary productivity, is enhanced and stabilized over time by high diversity as the community increases in its efficiency in exploiting available resources. Similarly, there is growing field support for the generalization that increasing species and functional diversity of natural enemies leads to increasing pest suppression. Nonetheless a central concern of classical biological control in particular, as it seeks to minimize non-target effects, remains as to whether one or a few species of natural enemies can provide sufficient pest control.

  6. 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,…

  7. Computing Evans functions numerically via boundary-value problems

    NASA Astrophysics Data System (ADS)

    Barker, Blake; Nguyen, Rose; Sandstede, Björn; Ventura, Nathaniel; Wahl, Colin

    2018-03-01

    The Evans function has been used extensively to study spectral stability of travelling-wave solutions in spatially extended partial differential equations. To compute Evans functions numerically, several shooting methods have been developed. In this paper, an alternative scheme for the numerical computation of Evans functions is presented that relies on an appropriate boundary-value problem formulation. Convergence of the algorithm is proved, and several examples, including the computation of eigenvalues for a multi-dimensional problem, are given. The main advantage of the scheme proposed here compared with earlier methods is that the scheme is linear and scalable to large problems.

  8. 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…

  9. 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…

  10. 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.

  11. Computational Functional Analysis of Lipid Metabolic Enzymes.

    PubMed

    Bagnato, Carolina; Have, Arjen Ten; Prados, María B; Beligni, María V

    2017-01-01

    The computational analysis of enzymes that participate in lipid metabolism has both common and unique challenges when compared to the whole protein universe. Some of the hurdles that interfere with the functional annotation of lipid metabolic enzymes that are common to other pathways include the definition of proper starting datasets, the construction of reliable multiple sequence alignments, the definition of appropriate evolutionary models, and the reconstruction of phylogenetic trees with high statistical support, particularly for large datasets. Most enzymes that take part in lipid metabolism belong to complex superfamilies with many members that are not involved in lipid metabolism. In addition, some enzymes that do not have sequence similarity catalyze similar or even identical reactions. Some of the challenges that, albeit not unique, are more specific to lipid metabolism refer to the high compartmentalization of the routes, the catalysis in hydrophobic environments and, related to this, the function near or in biological membranes.In this work, we provide guidelines intended to assist in the proper functional annotation of lipid metabolic enzymes, based on previous experiences related to the phospholipase D superfamily and the annotation of the triglyceride synthesis pathway in algae. We describe a pipeline that starts with the definition of an initial set of sequences to be used in similarity-based searches and ends in the reconstruction of phylogenies. We also mention the main issues that have to be taken into consideration when using tools to analyze subcellular localization, hydrophobicity patterns, or presence of transmembrane domains in lipid metabolic enzymes.

  12. 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

  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. 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.

  15. Diurnal rhythmicity in biological processes involved in bioavailability of functional food factors.

    PubMed

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

    2013-05-01

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

  16. Advances and Computational Tools towards Predictable Design in Biological Engineering

    PubMed Central

    2014-01-01

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

  17. A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

    PubMed

    Huertas, Ismael; Oldehinkel, Marianne; van Oort, Erik S B; Garcia-Solis, David; Mir, Pablo; Beckmann, Christian F; Marquand, Andre F

    2017-11-01

    The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach

  18. Contextuality and Wigner-function negativity in qubit quantum computation

    NASA Astrophysics Data System (ADS)

    Raussendorf, Robert; Browne, Dan E.; Delfosse, Nicolas; Okay, Cihan; Bermejo-Vega, Juan

    2017-05-01

    We describe schemes of quantum computation with magic states on qubits for which contextuality and negativity of the Wigner function are necessary resources possessed by the magic states. These schemes satisfy a constraint. Namely, the non-negativity of Wigner functions must be preserved under all available measurement operations. Furthermore, we identify stringent consistency conditions on such computational schemes, revealing the general structure by which negativity of Wigner functions, hardness of classical simulation of the computation, and contextuality are connected.

  19. Biological pathways and genetic mechanisms involved in social functioning.

    PubMed

    Ordoñana, Juan R; Bartels, Meike; Boomsma, Dorret I; Cella, David; Mosing, Miriam; Oliveira, Joao R; Patrick, Donald L; Veenhoven, Ruut; Wagner, Gert G; Sprangers, Mirjam A G

    2013-08-01

    To describe the major findings in the literature regarding associations between biological and genetic factors and social functioning, paying special attention to: (1) heritability studies on social functioning and related concepts; (2) hypothesized biological pathways and genetic variants that could be involved in social functioning, and (3) the implications of these results for quality-of-life research. A search of Web of Science and PubMed databases was conducted using combinations of the following keywords: genetics, twins, heritability, social functioning, social adjustment, social interaction, and social dysfunction. Variability in the definitions and measures of social functioning was extensive. Moderate to high heritability was reported for social functioning and related concepts, including prosocial behavior, loneliness, and extraversion. Disorders characterized by impairments in social functioning also show substantial heritability. Genetic variants hypothesized to be involved in social functioning are related to the network of brain structures and processes that are known to affect social cognition and behavior. Better knowledge and understanding about the impact of genetic factors on social functioning is needed to help us to attain a more comprehensive view of health-related quality-of-life (HRQOL) and will ultimately enhance our ability to identify those patients who are vulnerable to poor social functioning.

  20. 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

  1. Biological modelling of a computational spiking neural network with neuronal avalanches

    NASA Astrophysics Data System (ADS)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

  2. Biologic variability and correlation of platelet function testing in healthy dogs.

    PubMed

    Blois, Shauna L; Lang, Sean T; Wood, R Darren; Monteith, Gabrielle

    2015-12-01

    Platelet function tests are influenced by biologic variability, including inter-individual (CVG ) and intra-individual (CVI ), as well as analytic (CVA ) variability. Variability in canine platelet function testing is unknown, but if excessive, would make it difficult to interpret serial results. Additionally, the correlation between platelet function tests is poor in people, but not well described in dogs. The aims were to: (1) identify the effect of variation in preanalytic factors (venipuncture, elapsed time until analysis) on platelet function tests; (2) calculate analytic and biologic variability of adenosine diphosphate (ADP) and arachidonic acid (AA)-induced thromboelastograph platelet mapping (TEG-PM), ADP-, AA-, and collagen-induced whole blood platelet aggregometry (WBA), and collagen/ADP and collagen/epinephrine platelet function analysis (PFA-CADP, PFA-CEPI); and (3) determine the correlation between these variables. In this prospective observational trial, platelet function was measured once every 7 days, for 4 consecutive weeks, in 9 healthy dogs. In addition, CBC, TEG-PM, WBA, and PFA were performed. Overall coefficients of variability ranged from 13.3% to 87.8% for the platelet function tests. Biologic variability was highest for AA-induced maximum amplitude generated during TEG-PM (MAAA; CVG = 95.3%, CVI = 60.8%). Use of population-based reference intervals (RI) was determined appropriate only for PFA-CADP (index of individuality = 10.7). There was poor correlation between most platelet function tests. Use of population-based RI appears inappropriate for most platelet function tests, and tests poorly correlate with one another. Future studies on biologic variability and correlation of platelet function tests should be performed in dogs with platelet dysfunction and those treated with antiplatelet therapy. © 2015 American Society for Veterinary Clinical Pathology.

  3. Instructive Biologic Scaffold for Functional Tissue Regeneration Following Trauma to the Extremities

    DTIC Science & Technology

    2015-09-01

    Award Number: W81XWH-12-2-0128 TITLE: Instructive Biologic Scaffold for Functional Tissue Regeneration Following Trauma to the Extremities...2014 - 29 Aug 2015 4. TITLE AND SUBTITLE Instructive Biologic Scaffold for Functional Tissue Regeneration Following Trauma to the Extremities 5a...effectiveness of a regenerative scaffold for the restoration of functional musculotendinous tissue , including the restoration of blood supply and innervation

  4. Dynamic Biological Functioning Important for Simulating and Stabilizing Ocean Biogeochemistry

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  5. 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

  6. 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.

  7. 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.)

  8. Composite Structural Motifs of Binding Sites for Delineating Biological Functions of Proteins

    PubMed Central

    Kinjo, Akira R.; Nakamura, Haruki

    2012-01-01

    Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures. PMID:22347478

  9. Computing with motile bio-agents

    NASA Astrophysics Data System (ADS)

    Nicolau, Dan V., Jr.; Burrage, Kevin; Nicolau, Dan V.

    2007-12-01

    We describe a model of computation of the parallel type, which we call 'computing with bio-agents', based on the concept that motions of biological objects such as bacteria or protein molecular motors in confined spaces can be regarded as computations. We begin with the observation that the geometric nature of the physical structures in which model biological objects move modulates the motions of the latter. Consequently, by changing the geometry, one can control the characteristic trajectories of the objects; on the basis of this, we argue that such systems are computing devices. We investigate the computing power of mobile bio-agent systems and show that they are computationally universal in the sense that they are capable of computing any Boolean function in parallel. We argue also that using appropriate conditions, bio-agent systems can solve NP-complete problems in probabilistic polynomial time.

  10. 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.

  11. Biologically plausible particulate air pollution mortality concentration-response functions.

    PubMed Central

    Roberts, Steven

    2004-01-01

    In this article I introduce an alternative method for estimating particulate air pollution mortality concentration-response functions. This method constrains the particulate air pollution mortality concentration-response function to be biologically plausible--that is, a non-decreasing function of the particulate air pollution concentration. Using time-series data from Cook County, Illinois, the proposed method yields more meaningful particulate air pollution mortality concentration-response function estimates with an increase in statistical accuracy. PMID:14998745

  12. Derivation and computation of discrete-delay and continuous-delay SDEs in mathematical biology.

    PubMed

    Allen, Edward J

    2014-06-01

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

  13. 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...

  14. Biological modelling of a computational spiking neural network with neuronal avalanches.

    PubMed

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-06-28

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. © 2017 The Author(s).

  15. 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.

  16. 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. Copyright © 2016 The Author. Published by Elsevier Inc. All rights reserved.

  17. Biological Sensors Using DNA Functionalized Multiwalled Carbon Nanotubes

    DTIC Science & Technology

    2009-10-01

    BIOLOGICAL SENSORS USING DNA FUNCTIONALIZED MULTIWALLED CARBON NANOTUBES S. Ganguly, A. Leela Mohana Reddy and S. Ramaprabhu Alternative...Braun, E. Science 2003, 302, 1380. 9. Leela Mohana Reddy, A.; Shaijumon, M. M.; Ramaprabhu, S. Nanotechnology 2006, 17, 5299. 10. Kovtyukhova, N

  18. Efficient and Flexible Computation of Many-Electron Wave Function Overlaps.

    PubMed

    Plasser, Felix; Ruckenbauer, Matthias; Mai, Sebastian; Oppel, Markus; Marquetand, Philipp; González, Leticia

    2016-03-08

    A new algorithm for the computation of the overlap between many-electron wave functions is described. This algorithm allows for the extensive use of recurring intermediates and thus provides high computational efficiency. Because of the general formalism employed, overlaps can be computed for varying wave function types, molecular orbitals, basis sets, and molecular geometries. This paves the way for efficiently computing nonadiabatic interaction terms for dynamics simulations. In addition, other application areas can be envisaged, such as the comparison of wave functions constructed at different levels of theory. Aside from explaining the algorithm and evaluating the performance, a detailed analysis of the numerical stability of wave function overlaps is carried out, and strategies for overcoming potential severe pitfalls due to displaced atoms and truncated wave functions are presented.

  19. Occurrence, Functions and Biological Significance of Arginine-Rich Proteins.

    PubMed

    Chandana, Thimmegowda; Venkatesh, Yeldur P

    2016-01-01

    Arginine, the most basic among the 20 amino acids, occurs less frequently than lysine in proteins despite being coded by six codons. Only a few important proteins of biological significance have been found to be abundant in arginine. It has been established that these arginine-rich proteins have been assigned important roles in the biological systems. Arginine-rich cationic proteins are known to stabilize macromolecular structures by establishing appropriate interactions (salt bridges, hydrogen bonds and cation-π interactions). These proteins are also known to be the key members of many regulatory pathways such as gene expression, chromatin stability, expurgation of introns from naïve mRNA, mRNA splicing, membrane-penetrating activity and pathogenesis-related defense, to name a few. Further, arginine occurs in various combinations with other amino acids (serine, lysine, proline, tryptophan, valine, glycine and glutamic acid) which diversify the potential functions of arginine-rich proteins. Arginine-rich proteins known till date from dietary sources have been described in terms of their structure and functional properties. A variety of activities such as bactericidal, membrane-penetrating, antimicrobial, anti-hypertensive, pro-angiogenic and others have been reported for arginine-rich proteins. This review attempts to collate the occurrence, functions and the biological significance of this unique class of proteins rich in arginine.

  20. 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)

  1. Applications of systems biology towards microbial fuel production.

    PubMed

    Gowen, Christopher M; Fong, Stephen S

    2011-10-01

    Harnessing the immense natural diversity of biological functions for economical production of fuel has enormous potential benefits. Inevitably, however, the native capabilities for any given organism must be modified to increase the productivity or efficiency of a biofuel bioprocess. From a broad perspective, the challenge is to sufficiently understand the details of cellular functionality to be able to prospectively predict and modify the cellular function of a microorganism. Recent advances in experimental and computational systems biology approaches can be used to better understand cellular level function and guide future experiments. With pressure to quickly develop viable, renewable biofuel processes a balance must be maintained between obtaining depth of biological knowledge and applying that knowledge. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Evans function computation for the stability of travelling waves

    NASA Astrophysics Data System (ADS)

    Barker, B.; Humpherys, J.; Lyng, G.; Lytle, J.

    2018-04-01

    In recent years, the Evans function has become an important tool for the determination of stability of travelling waves. This function, a Wronskian of decaying solutions of the eigenvalue equation, is useful both analytically and computationally for the spectral analysis of the linearized operator about the wave. In particular, Evans-function computation allows one to locate any unstable eigenvalues of the linear operator (if they exist); this allows one to establish spectral stability of a given wave and identify bifurcation points (loss of stability) as model parameters vary. In this paper, we review computational aspects of the Evans function and apply it to multidimensional detonation waves. This article is part of the theme issue `Stability of nonlinear waves and patterns and related topics'.

  3. Recent advances in food biopeptides: production, biological functionalities and therapeutic applications.

    PubMed

    Saadi, Sami; Saari, Nazamid; Anwar, Farooq; Abdul Hamid, Azizah; Ghazali, Hasanah Mohd

    2015-01-01

    The growing momentum of several common life-style diseases such as myocardial infarction, cardiovascular disorders, stroke, hypertension, diabetes, and atherosclerosis has become a serious global concern. Recent developments in the field of proteomics offering promising solutions to solving such health problems stimulates the uses of biopeptides as one of the therapeutic agents to alleviate disease-related risk factors. Functional peptides are typically produced from protein via enzymatic hydrolysis under in vitro or in vivo conditions using different kinds of proteolytic enzymes. An array of biological activities, including antioxidative, antihypertensive, antidiabetic and immunomodulating has been ascribed to different types of biopeptides derived from various food sources. In fact, biopeptides are nutritionally and functionally important for regulating some physiological functions in the body; however, these are yet to be extensively addressed with regard to their production through advance strategies, mechanisms of action and multiple biological functionalities. This review mainly focuses on recent biotechnological advances that are being made in the field of production in addition to covering the mode of action and biological activities, medicinal health functions and therapeutic applications of biopeptides. State-of-the-art strategies that can ameliorate the efficacy, bioavailability, and functionality of biopeptides along with their future prospects are likewise discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. 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.

  5. 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.

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

    PubMed

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

    2015-12-15

    In animal and fungal model organisms, the complexities of cell biology have been analyzed in exquisite detail and much is known about how these organisms function at the cellular level. However, the model organisms cell biologists generally use include only a tiny fraction of the true diversity of eukaryotic cellular forms. The divergent cellular processes observed in these more distant lineages are still largely unknown in the general scientific community. Despite the relative obscurity of these organisms, comparative studies of them across eukaryotic diversity have had profound implications for our understanding of fundamental cell biology in all species and have revealed the evolution and origins of previously observed cellular processes. In this Perspective, we will discuss the complexity of cell biology found across the eukaryotic tree, and three specific examples of where studies of divergent cell biology have altered our understanding of key functional aspects of mitochondria, plastids, and membrane trafficking. © 2015 Richardson et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  7. Instructive Biologic Scaffold for Functional Tissue Regeneration Following Trauma to the Extremities

    DTIC Science & Technology

    2016-10-01

    Award Number: W81XWH-12-2-0128 TITLE: Instructive Biologic Scaffold for Functional Tissue Regeneration Following Trauma to the Extremities...SUBTITLE Instructive Biologic Scaffold for Functional Tissue Regeneration Following Trauma to the Extremities 5a. CONTRACT NUMBER 5b. GRANT NUMBER...identification of cell phenotype, extracellular 5 matrix characterization, and histomorphometric analysis. The main endpoint of this study was to

  8. Chemically-functionalized microcantilevers for detection of chemical, biological and explosive material

    DOEpatents

    Pinnaduwage, Lal A [Knoxville, TN; Thundat, Thomas G [Knoxville, TN; Brown, Gilbert M [Knoxville, TN; Hawk, John Eric [Olive Branch, MS; Boiadjiev, Vassil I [Knoxville, TN

    2007-04-24

    A chemically functionalized cantilever system has a cantilever coated on one side thereof with a reagent or biological species which binds to an analyte. The system is of particular value when the analyte is a toxic chemical biological warfare agent or an explosive.

  9. Functional annotation of chemical libraries across diverse biological processes.

    PubMed

    Piotrowski, Jeff S; Li, Sheena C; Deshpande, Raamesh; Simpkins, Scott W; Nelson, Justin; Yashiroda, Yoko; Barber, Jacqueline M; Safizadeh, Hamid; Wilson, Erin; Okada, Hiroki; Gebre, Abraham A; Kubo, Karen; Torres, Nikko P; LeBlanc, Marissa A; Andrusiak, Kerry; Okamoto, Reika; Yoshimura, Mami; DeRango-Adem, Eva; van Leeuwen, Jolanda; Shirahige, Katsuhiko; Baryshnikova, Anastasia; Brown, Grant W; Hirano, Hiroyuki; Costanzo, Michael; Andrews, Brenda; Ohya, Yoshikazu; Osada, Hiroyuki; Yoshida, Minoru; Myers, Chad L; Boone, Charles

    2017-09-01

    Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

  10. Management and Analysis of Biological and Clinical Data: How Computer Science May Support Biomedical and Clinical Research

    NASA Astrophysics Data System (ADS)

    Veltri, Pierangelo

    The use of computer based solutions for data management in biology and clinical science has contributed to improve life-quality and also to gather research results in shorter time. Indeed, new algorithms and high performance computation have been using in proteomics and genomics studies for curing chronic diseases (e.g., drug designing) as well as supporting clinicians both in diagnosis (e.g., images-based diagnosis) and patient curing (e.g., computer based information analysis on information gathered from patient). In this paper we survey on examples of computer based techniques applied in both biology and clinical contexts. The reported applications are also results of experiences in real case applications at University Medical School of Catanzaro and also part of experiences of the National project Staywell SH 2.0 involving many research centers and companies aiming to study and improve citizen wellness.

  11. Computational and theoretical approaches for studies of a lipid recognition protein on biological membranes

    PubMed Central

    Yamamoto, Eiji

    2017-01-01

    Many cellular functions, including cell signaling and related events, are regulated by the association of peripheral membrane proteins (PMPs) with biological membranes containing anionic lipids, e.g., phosphatidylinositol phosphate (PIP). This association is often mediated by lipid recognition modules present in many PMPs. Here, I summarize computational and theoretical approaches to investigate the molecular details of the interactions and dynamics of a lipid recognition module, the pleckstrin homology (PH) domain, on biological membranes. Multiscale molecular dynamics simulations using combinations of atomistic and coarse-grained models yielded results comparable to those of actual experiments and could be used to elucidate the molecular mechanisms of the formation of protein/lipid complexes on membrane surfaces, which are often difficult to obtain using experimental techniques. Simulations revealed some modes of membrane localization and interactions of PH domains with membranes in addition to the canonical binding mode. In the last part of this review, I address the dynamics of PH domains on the membrane surface. Local PIP clusters formed around the proteins exhibit anomalous fluctuations. This dynamic change in protein-lipid interactions cause temporally fluctuating diffusivity of proteins, i.e., the short-term diffusivity of the bound protein changes substantially with time, and may in turn contribute to the formation/dissolution of protein complexes in membranes. PMID:29159013

  12. 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.

  13. Dynamics of biological systems: role of systems biology in medical research.

    PubMed

    Assmus, Heike E; Herwig, Ralf; Cho, Kwang-Hyun; Wolkenhauer, Olaf

    2006-11-01

    Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.

  14. Computational Predictions Provide Insights into the Biology of TAL Effector Target Sites

    PubMed Central

    Grau, Jan; Wolf, Annett; Reschke, Maik; Bonas, Ulla; Posch, Stefan; Boch, Jens

    2013-01-01

    Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library

  15. Computational genomic identification and functional reconstitution of plant natural product biosynthetic pathways

    PubMed Central

    2016-01-01

    Covering: 2003 to 2016 The last decade has seen the first major discoveries regarding the genomic basis of plant natural product biosynthetic pathways. Four key computationally driven strategies have been developed to identify such pathways, which make use of physical clustering, co-expression, evolutionary co-occurrence and epigenomic co-regulation of the genes involved in producing a plant natural product. Here, we discuss how these approaches can be used for the discovery of plant biosynthetic pathways encoded by both chromosomally clustered and non-clustered genes. Additionally, we will discuss opportunities to prioritize plant gene clusters for experimental characterization, and end with a forward-looking perspective on how synthetic biology technologies will allow effective functional reconstitution of candidate pathways using a variety of genetic systems. PMID:27321668

  16. diffuStats: an R package to compute diffusion-based scores on biological networks.

    PubMed

    Picart-Armada, Sergio; Thompson, Wesley K; Buil, Alfonso; Perera-Lluna, Alexandre

    2018-02-01

    Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritizing novel candidates in the area of study. There are several choices in conceiving the diffusion process-involving the graph kernel, the score definitions and the presence of a posterior statistical normalization-which have an impact on the results. This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalized scores, that eases the computation of the scores and their benchmarking for an optimal choice. The R package diffuStats is publicly available in Bioconductor, https://bioconductor.org, under the GPL-3 license. sergi.picart@upc.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  17. Computational strategies for the Riemann zeta function

    NASA Astrophysics Data System (ADS)

    Borwein, Jonathan M.; Bradley, David M.; Crandall, Richard E.

    2000-09-01

    We provide a compendium of evaluation methods for the Riemann zeta function, presenting formulae ranging from historical attempts to recently found convergent series to curious oddities old and new. We concentrate primarily on practical computational issues, such issues depending on the domain of the argument, the desired speed of computation, and the incidence of what we call "value recycling".

  18. Computation of the Complex Probability Function

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

    Trainer, Amelia Jo; Ledwith, Patrick John

    The complex probability function is important in many areas of physics and many techniques have been developed in an attempt to compute it for some z quickly and e ciently. Most prominent are the methods that use Gauss-Hermite quadrature, which uses the roots of the n th degree Hermite polynomial and corresponding weights to approximate the complex probability function. This document serves as an overview and discussion of the use, shortcomings, and potential improvements on the Gauss-Hermite quadrature for the complex probability function.

  19. Computational modeling of heterogeneity and function of CD4+ T cells

    PubMed Central

    Carbo, Adria; Hontecillas, Raquel; Andrew, Tricity; Eden, Kristin; Mei, Yongguo; Hoops, Stefan; Bassaganya-Riera, Josep

    2014-01-01

    The immune system is composed of many different cell types and hundreds of intersecting molecular pathways and signals. This large biological complexity requires coordination between distinct pro-inflammatory and regulatory cell subsets to respond to infection while maintaining tissue homeostasis. CD4+ T cells play a central role in orchestrating immune responses and in maintaining a balance between pro- and anti- inflammatory responses. This tight balance between regulatory and effector reactions depends on the ability of CD4+ T cells to modulate distinct pathways within large molecular networks, since dysregulated CD4+ T cell responses may result in chronic inflammatory and autoimmune diseases. The CD4+ T cell differentiation process comprises an intricate interplay between cytokines, their receptors, adaptor molecules, signaling cascades and transcription factors that help delineate cell fate and function. Computational modeling can help to describe, simulate, analyze, and predict some of the behaviors in this complicated differentiation network. This review provides a comprehensive overview of existing computational immunology methods as well as novel strategies used to model immune responses with a particular focus on CD4+ T cell differentiation. PMID:25364738

  20. In silico experiment system for testing hypothesis on gene functions using three condition specific biological networks.

    PubMed

    Lee, Chai-Jin; Kang, Dongwon; Lee, Sangseon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

    2018-05-25

    Determining functions of a gene requires time consuming, expensive biological experiments. Scientists can speed up this experimental process if the literature information and biological networks can be adequately provided. In this paper, we present a web-based information system that can perform in silico experiments of computationally testing hypothesis on the function of a gene. A hypothesis that is specified in English by the user is converted to genes using a literature and knowledge mining system called BEST. Condition-specific TF, miRNA and PPI (protein-protein interaction) networks are automatically generated by projecting gene and miRNA expression data to template networks. Then, an in silico experiment is to test how well the target genes are connected from the knockout gene through the condition-specific networks. The test result visualizes path from the knockout gene to the target genes in the three networks. Statistical and information-theoretic scores are provided on the resulting web page to help scientists either accept or reject the hypothesis being tested. Our web-based system was extensively tested using three data sets, such as E2f1, Lrrk2, and Dicer1 knockout data sets. We were able to re-produce gene functions reported in the original research papers. In addition, we comprehensively tested with all disease names in MalaCards as hypothesis to show the effectiveness of our system. Our in silico experiment system can be very useful in suggesting biological mechanisms which can be further tested in vivo or in vitro. http://biohealth.snu.ac.kr/software/insilico/. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. Computing life: Add logos to biology and bios to physics.

    PubMed

    Kolodkin, Alexey; Simeonidis, Evangelos; Westerhoff, Hans V

    2013-04-01

    This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose approaches to scale emergence according to the degree of state-dependency of the system's component properties. Since the component properties of biological systems are state-dependent to a high extent, biological emergence should be considered as very strong emergence - i.e. its reconstruction would require a lot of information about state-dependency of its component properties. However, due to its complexity and volume, this information cannot be handled in the naked human brain, or on the back of an envelope. To solve this problem, biological emergence can be reconstructed in silico based on experimentally determined rate laws and parameter values of the living cell. According to some rough calculations, the silicon human might comprise the mathematical descriptions of around 10(5) interactions. This is not a small number, but taking into account the exponentially increase of computational power, it should not prove to be our principal limitation. The bigger challenges will be located in different areas. For example they may be related to the observer effect - the limitation to measuring a system's component properties without affecting the system. Another obstacle may be hidden in the tradition of "shaving away" all "unnecessary" assumptions (the so-called Occam's razor) that, in fact, reflects the intention to model the system as simply as possible and thus to deem the emergence to be less strong than it possibly is. We argue here that that Occam's razor should be replaced with the law of completeness. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Laminar fMRI and computational theories of brain function.

    PubMed

    Stephan, K E; Petzschner, F H; Kasper, L; Bayer, J; Wellstein, K V; Stefanics, G; Pruessmann, K P; Heinzle, J

    2017-11-02

    Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. 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.

  4. Dispensing Processes Impact Apparent Biological Activity as Determined by Computational and Statistical Analyses

    PubMed Central

    Ekins, Sean; Olechno, Joe; Williams, Antony J.

    2013-01-01

    Dispensing and dilution processes may profoundly influence estimates of biological activity of compounds. Published data show Ephrin type-B receptor 4 IC50 values obtained via tip-based serial dilution and dispensing versus acoustic dispensing with direct dilution differ by orders of magnitude with no correlation or ranking of datasets. We generated computational 3D pharmacophores based on data derived by both acoustic and tip-based transfer. The computed pharmacophores differ significantly depending upon dispensing and dilution methods. The acoustic dispensing-derived pharmacophore correctly identified active compounds in a subsequent test set where the tip-based method failed. Data from acoustic dispensing generates a pharmacophore containing two hydrophobic features, one hydrogen bond donor and one hydrogen bond acceptor. This is consistent with X-ray crystallography studies of ligand-protein interactions and automatically generated pharmacophores derived from this structural data. In contrast, the tip-based data suggest a pharmacophore with two hydrogen bond acceptors, one hydrogen bond donor and no hydrophobic features. This pharmacophore is inconsistent with the X-ray crystallographic studies and automatically generated pharmacophores. In short, traditional dispensing processes are another important source of error in high-throughput screening that impacts computational and statistical analyses. These findings have far-reaching implications in biological research. PMID:23658723

  5. Revision history aware repositories of computational models of biological systems.

    PubMed

    Miller, Andrew K; Yu, Tommy; Britten, Randall; Cooling, Mike T; Lawson, James; Cowan, Dougal; Garny, Alan; Halstead, Matt D B; Hunter, Peter J; Nickerson, David P; Nunns, Geo; Wimalaratne, Sarala M; Nielsen, Poul M F

    2011-01-14

    Building repositories of computational models of biological systems ensures that published models are available for both education and further research, and can provide a source of smaller, previously verified models to integrate into a larger model. One problem with earlier repositories has been the limitations in facilities to record the revision history of models. Often, these facilities are limited to a linear series of versions which were deposited in the repository. This is problematic for several reasons. Firstly, there are many instances in the history of biological systems modelling where an 'ancestral' model is modified by different groups to create many different models. With a linear series of versions, if the changes made to one model are merged into another model, the merge appears as a single item in the history. This hides useful revision history information, and also makes further merges much more difficult, as there is no record of which changes have or have not already been merged. In addition, a long series of individual changes made outside of the repository are also all merged into a single revision when they are put back into the repository, making it difficult to separate out individual changes. Furthermore, many earlier repositories only retain the revision history of individual files, rather than of a group of files. This is an important limitation to overcome, because some types of models, such as CellML 1.1 models, can be developed as a collection of modules, each in a separate file. The need for revision history is widely recognised for computer software, and a lot of work has gone into developing version control systems and distributed version control systems (DVCSs) for tracking the revision history. However, to date, there has been no published research on how DVCSs can be applied to repositories of computational models of biological systems. We have extended the Physiome Model Repository software to be fully revision history aware

  6. Synthetic biology expands chemical control of microorganisms.

    PubMed

    Ford, Tyler J; Silver, Pamela A

    2015-10-01

    The tools of synthetic biology allow researchers to change the ways engineered organisms respond to chemical stimuli. Decades of basic biology research and new efforts in computational protein and RNA design have led to the development of small molecule sensors that can be used to alter organism function. These new functions leap beyond the natural propensities of the engineered organisms. They can range from simple fluorescence or growth reporting to pathogen killing, and can involve metabolic coordination among multiple cells or organisms. Herein, we discuss how synthetic biology alters microorganisms' responses to chemical stimuli resulting in the development of microbes as toxicity sensors, disease treatments, and chemical factories. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. 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…

  8. 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 ...

  9. Social inclusion enhances biological motion processing: A functional near-infrared spectroscopy study

    PubMed Central

    Bolling, Danielle Z.; Pelphrey, Kevin A.; Kaiser, Martha D.

    2012-01-01

    Humans are especially tuned to the movements of other people. Neural correlates of this social attunement have been proposed to lie in and around the right posterior superior temporal sulcus (STS) region, which robustly responds to biological motion in contrast to a variety of non-biological motions. This response persists even when no form information is provided, as in point-light displays (PLDs). The aim of the current study was to assess the ability of functional near-infrared spectroscopy (fNIRS) to reliably measure brain responses to PLDs of biological motion, and determine the sensitivity of these responses to interpersonal contextual factors. To establish reliability, we measured brain activation to biological motion with fNIRS and functional magnetic resonance imaging (fMRI) during two separate sessions in an identical group of 12 participants. To establish sensitivity, brain responses to biological motion measured with fNIRS were subjected to an additional social manipulation where participants were either socially included or excluded before viewing PLDs of biological motion. Results revealed comparable brain responses to biological motion using fMRI and fNIRS in the right supramarginal gyrus. Further, social inclusion increased brain responses to biological motion in right supramarginal gyrus and posterior STS. Thus, fNIRS can reliably measure brain responses to biological motion and can detect social experience-dependent modulations of these brain responses. PMID:22941501

  10. 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.

  11. Constructing biological pathway models with hybrid functional petri nets.

    PubMed

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

    2011-01-01

    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.

  12. A biomimetic functionalization approach to integration of carbon nanoutbes into biological systems

    NASA Astrophysics Data System (ADS)

    Chen, Xing; Tam, Un Chong; Bertozzi, Carolyn; Zettl, Alex

    2006-03-01

    Due to their remarkable structural, electrical, and mechanical properties, carbon nanotubes (CNTs) have potential applications in biology ranging from imaging and tissue engineering. To realize these applications, however, new strategies for controlling the interaction between CNTs and biological systems such as proteins and cells are required. Here we describe a biomimetic approach to functionalize CNTs and therefore render them biocompatibility in order to facilitate their integration into biological systems. CNTs were coated with synthetic gycopolymers that mimic cell surface mucin gycoproteins. The functionalized CNTs were soluble in water, resisted non-specific protein binding and bound specifically to biomolecules. The coated CNTs could then be integrated onto mammalian cell surface by virtue of glycan-receptor interactions. Furthermore, the functionalized CNTs are non-toxic to cells. This strategy offers new opportunities for development of biosensor to probe biological processes. References: 1. X. Chen, G. S. Lee, A. Zettl, C. R. Bertozzi, Angewandte Chemie-International Edition 43, 6111 (2004). 2. X. Chen, U. C. Tam, J. L. Czlapanski, G. S. Lee, D. Rabuka, A. Zettl, C. R. Bertozzi, submitted.

  13. 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.

  14. Some computational techniques for estimating human operator describing functions

    NASA Technical Reports Server (NTRS)

    Levison, W. H.

    1986-01-01

    Computational procedures for improving the reliability of human operator describing functions are described. Special attention is given to the estimation of standard errors associated with mean operator gain and phase shift as computed from an ensemble of experimental trials. This analysis pertains to experiments using sum-of-sines forcing functions. Both open-loop and closed-loop measurement environments are considered.

  15. Construction of Biologically Functional Bacterial Plasmids In Vitro

    PubMed Central

    Cohen, Stanley N.; Chang, Annie C. Y.; Boyer, Herbert W.; Helling, Robert B.

    1973-01-01

    The construction of new plasmid DNA species by in vitro joining of restriction endonuclease-generated fragments of separate plasmids is described. Newly constructed plasmids that are inserted into Escherichia coli by transformation are shown to be biologically functional replicons that possess genetic properties and nucleotide base sequences from both of the parent DNA molecules. Functional plasmids can be obtained by reassociation of endonuclease-generated fragments of larger replicons, as well as by joining of plasmid DNA molecules of entirely different origins. Images PMID:4594039

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

    PubMed

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

    2018-01-15

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

  17. 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.

  18. A Theoretical Mechanism of Szilard Engine Function in Nucleic Acids and the Implications for Quantum Coherence in Biological Systems

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

    Matthew Mihelic, F.

    2010-12-22

    Nucleic acids theoretically possess a Szilard engine function that can convert the energy associated with the Shannon entropy of molecules for which they have coded recognition, into the useful work of geometric reconfiguration of the nucleic acid molecule. This function is logically reversible because its mechanism is literally and physically constructed out of the information necessary to reduce the Shannon entropy of such molecules, which means that this information exists on both sides of the theoretical engine, and because information is retained in the geometric degrees of freedom of the nucleic acid molecule, a quantum gate is formed through whichmore » multi-state nucleic acid qubits can interact. Entangled biophotons emitted as a consequence of symmetry breaking nucleic acid Szilard engine (NASE) function can be used to coordinate relative positioning of different nucleic acid locations, both within and between cells, thus providing the potential for quantum coherence of an entire biological system. Theoretical implications of understanding biological systems as such 'quantum adaptive systems' include the potential for multi-agent based quantum computing, and a better understanding of systemic pathologies such as cancer, as being related to a loss of systemic quantum coherence.« less

  19. A Theoretical Mechanism of Szilard Engine Function in Nucleic Acids and the Implications for Quantum Coherence in Biological Systems

    NASA Astrophysics Data System (ADS)

    Matthew Mihelic, F.

    2010-12-01

    Nucleic acids theoretically possess a Szilard engine function that can convert the energy associated with the Shannon entropy of molecules for which they have coded recognition, into the useful work of geometric reconfiguration of the nucleic acid molecule. This function is logically reversible because its mechanism is literally and physically constructed out of the information necessary to reduce the Shannon entropy of such molecules, which means that this information exists on both sides of the theoretical engine, and because information is retained in the geometric degrees of freedom of the nucleic acid molecule, a quantum gate is formed through which multi-state nucleic acid qubits can interact. Entangled biophotons emitted as a consequence of symmetry breaking nucleic acid Szilard engine (NASE) function can be used to coordinate relative positioning of different nucleic acid locations, both within and between cells, thus providing the potential for quantum coherence of an entire biological system. Theoretical implications of understanding biological systems as such "quantum adaptive systems" include the potential for multi-agent based quantum computing, and a better understanding of systemic pathologies such as cancer, as being related to a loss of systemic quantum coherence.

  20. Effects of Computer Animation Instructional Package on Students' Achievement in Practical Biology

    ERIC Educational Resources Information Center

    Hamzat, Abdulrasaq; Bello, Ganiyu; Abimbola, Isaac Olakanmi

    2017-01-01

    This study examined the effects of computer animation instructional package on secondary school students' achievement in practical biology in Ilorin, Nigeria. The study adopted a pre-test, post-test, control group, non-randomised and nonequivalent quasi-experimental design, with a 2x2x3 factorial design. Two intact classes from two secondary…

  1. Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE).

    PubMed

    Le Novère, Nicolas; Hucka, Michael; Anwar, Nadia; Bader, Gary D; Demir, Emek; Moodie, Stuart; Sorokin, Anatoly

    2011-11-30

    The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner.

  2. Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE)

    PubMed Central

    Le Novère, Nicolas; Hucka, Michael; Anwar, Nadia; Bader, Gary D; Demir, Emek; Moodie, Stuart; Sorokin, Anatoly

    2011-01-01

    The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner. PMID:22180826

  3. 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

  4. International Conference in Computational Cell Biology: From the Past to the Future

    DTIC Science & Technology

    2016-09-12

    24061 -0001 ABSTRACT Number of Papers published in peer -reviewed journals: Number of Papers published in non peer -reviewed journals: Final Report...present their latest research and discussed challenges in computational cell biology research and education. (a) Papers published in peer -reviewed...List the papers, including journal references, in the following categories: (b) Papers published in non- peer -reviewed journals (N/A for none) (c

  5. Marine Carotenoids: Biological Functions and Commercial Applications

    PubMed Central

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

    2011-01-01

    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. PMID:21556162

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

    PubMed

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

    2017-03-01

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

  7. Biological materials by design.

    PubMed

    Qin, Zhao; Dimas, Leon; Adler, David; Bratzel, Graham; Buehler, Markus J

    2014-02-19

    In this topical review we discuss recent advances in the use of physical insight into the way biological materials function, to design novel engineered materials 'from scratch', or from the level of fundamental building blocks upwards and by using computational multiscale methods that link chemistry to material function. We present studies that connect advances in multiscale hierarchical material structuring with material synthesis and testing, review case studies of wood and other biological materials, and illustrate how engineered fiber composites and bulk materials are designed, modeled, and then synthesized and tested experimentally. The integration of experiment and simulation in multiscale design opens new avenues to explore the physics of materials from a fundamental perspective, and using complementary strengths from models and empirical techniques. Recent developments in this field illustrate a new paradigm by which complex material functionality is achieved through hierarchical structuring in spite of simple material constituents.

  8. 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.

  9. Biology Today. Ah, Sweet Mysteries of Life.

    ERIC Educational Resources Information Center

    Flannery, Maura C.

    1991-01-01

    Mysteries of the biological past that paleontologists are trying to solve are discussed. Topics include first seeds, fossils and computers, packrat middens, charcoal clues, soft parts, Burgess shale, halkieriids, toe count, whales with feet, long necks, and changing functions. (KR)

  10. 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.

  11. G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

    PubMed

    Lee, Hui Sun; Im, Wonpil

    2016-04-01

    Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. © 2016 The Protein Society.

  12. Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

    PubMed

    Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

    2018-01-01

    Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

  13. Biology Notes.

    ERIC Educational Resources Information Center

    School Science Review, 1982

    1982-01-01

    Describes laboratory procedures, demonstrations, and classroom activities/materials, including use of dwarf cichlids (fishes) in secondary school biology, teaching edge effects on stomatal diffusion, computer program on effects of selection on gene frequencies, biological oxidation/reduction reactions, short cuts with Drosophila, computer program…

  14. Proteins of unknown function in the Protein Data Bank (PDB): an inventory of true uncharacterized proteins and computational tools for their analysis.

    PubMed

    Nadzirin, Nurul; Firdaus-Raih, Mohd

    2012-10-08

    Proteins of uncharacterized functions form a large part of many of the currently available biological databases and this situation exists even in the Protein Data Bank (PDB). Our analysis of recent PDB data revealed that only 42.53% of PDB entries (1084 coordinate files) that were categorized under "unknown function" are true examples of proteins of unknown function at this point in time. The remainder 1465 entries also annotated as such appear to be able to have their annotations re-assessed, based on the availability of direct functional characterization experiments for the protein itself, or for homologous sequences or structures thus enabling computational function inference.

  15. 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

  16. 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.

  17. Systems biology for organotypic cell cultures.

    PubMed

    Grego, Sonia; Dougherty, Edward R; Alexander, Francis J; Auerbach, Scott S; Berridge, Brian R; Bittner, Michael L; Casey, Warren; Cooley, Philip C; Dash, Ajit; Ferguson, Stephen S; Fennell, Timothy R; Hawkins, Brian T; Hickey, Anthony J; Kleensang, Andre; Liebman, Michael N J; Martin, Florian; Maull, Elizabeth A; Paragas, Jason; Qiao, Guilin Gary; Ramaiahgari, Sreenivasa; Sumner, Susan J; Yoon, Miyoung

    2017-01-01

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

  18. Biological network motif detection and evaluation

    PubMed Central

    2011-01-01

    Background Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. Results We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. Conclusion We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks. PMID:22784624

  19. Rapid functional analysis of computationally complex rare human IRF6 gene variants using a novel zebrafish model.

    PubMed

    Li, Edward B; Truong, Dawn; Hallett, Shawn A; Mukherjee, Kusumika; Schutte, Brian C; Liao, Eric C

    2017-09-01

    Large-scale sequencing efforts have captured a rapidly growing catalogue of genetic variations. However, the accurate establishment of gene variant pathogenicity remains a central challenge in translating personal genomics information to clinical decisions. Interferon Regulatory Factor 6 (IRF6) gene variants are significant genetic contributors to orofacial clefts. Although approximately three hundred IRF6 gene variants have been documented, their effects on protein functions remain difficult to interpret. Here, we demonstrate the protein functions of human IRF6 missense gene variants could be rapidly assessed in detail by their abilities to rescue the irf6 -/- phenotype in zebrafish through variant mRNA microinjections at the one-cell stage. The results revealed many missense variants previously predicted by traditional statistical and computational tools to be loss-of-function and pathogenic retained partial or full protein function and rescued the zebrafish irf6 -/- periderm rupture phenotype. Through mRNA dosage titration and analysis of the Exome Aggregation Consortium (ExAC) database, IRF6 missense variants were grouped by their abilities to rescue at various dosages into three functional categories: wild type function, reduced function, and complete loss-of-function. This sensitive and specific biological assay was able to address the nuanced functional significances of IRF6 missense gene variants and overcome many limitations faced by current statistical and computational tools in assigning variant protein function and pathogenicity. Furthermore, it unlocked the possibility for characterizing yet undiscovered human IRF6 missense gene variants from orofacial cleft patients, and illustrated a generalizable functional genomics paradigm in personalized medicine.

  20. ENFIN--A European network for integrative systems biology.

    PubMed

    Kahlem, Pascal; Clegg, Andrew; Reisinger, Florian; Xenarios, Ioannis; Hermjakob, Henning; Orengo, Christine; Birney, Ewan

    2009-11-01

    Integration of biological data of various types and the development of adapted bioinformatics tools represent critical objectives to enable research at the systems level. The European Network of Excellence ENFIN is engaged in developing an adapted infrastructure to connect databases, and platforms to enable both the generation of new bioinformatics tools and the experimental validation of computational predictions. With the aim of bridging the gap existing between standard wet laboratories and bioinformatics, the ENFIN Network runs integrative research projects to bring the latest computational techniques to bear directly on questions dedicated to systems biology in the wet laboratory environment. The Network maintains internally close collaboration between experimental and computational research, enabling a permanent cycling of experimental validation and improvement of computational prediction methods. The computational work includes the development of a database infrastructure (EnCORE), bioinformatics analysis methods and a novel platform for protein function analysis FuncNet.

  1. From video to computation of biological fluid-structure interaction problems

    NASA Astrophysics Data System (ADS)

    Dillard, Seth I.; Buchholz, James H. J.; Udaykumar, H. S.

    2016-04-01

    This work deals with the techniques necessary to obtain a purely Eulerian procedure to conduct CFD simulations of biological systems with moving boundary flow phenomena. Eulerian approaches obviate difficulties associated with mesh generation to describe or fit flow meshes to body surfaces. The challenges associated with constructing embedded boundary information, body motions and applying boundary conditions on the moving bodies for flow computation are addressed in the work. The overall approach is applied to the study of a fluid-structure interaction problem, i.e., the hydrodynamics of swimming of an American eel, where the motion of the eel is derived from video imaging. It is shown that some first-blush approaches do not work, and therefore, careful consideration of appropriate techniques to connect moving images to flow simulations is necessary and forms the main contribution of the paper. A combination of level set-based active contour segmentation with optical flow and image morphing is shown to enable the image-to-computation process.

  2. Computational Study of a Functionally Graded Ceramic-Metallic Armor

    DTIC Science & Technology

    2006-12-15

    UNCLAS –Dist. A - Approved for public release Computational Study of a Functionally Graded Ceramic-Metallic Armor Douglas W. Templeton1, Tara J...efficiency of a postulated FGM ceramic-metallic armor system composed of aluminum nitride (AlN) and aluminum. The study had two primary...2006 2. REPORT TYPE N/ A 3. DATES COVERED - 4. TITLE AND SUBTITLE Computational Study of a Functionally Graded Ceramic-Metallic Armor 5a

  3. Electromagnetic fields as structure-function zeitgebers in biological systems: environmental orchestrations of morphogenesis and consciousness.

    PubMed

    Rouleau, Nicolas; Dotta, Blake T

    2014-01-01

    Within a cell system structure dictates function. Any interaction between cells, or a cell and its environment, has the potential to have long term implications on the function of a given cell and emerging cell aggregates. The structure and function of cells are continuously subjected to modification by electrical and chemical stimuli. However, biological systems are also subjected to an ever-present influence: the electromagnetic (EM) environment. Biological systems have the potential to be influenced by subtle energies which are exchanged at atomic and subatomic scales as EM phenomena. These energy exchanges have the potential to manifest at higher orders of discourse and affect the output (behavior) of a biological system. Here we describe theoretical and experimental evidence of EM influence on cells and the integration of whole systems. Even weak interactions between EM energies and biological systems display the potential to affect a developing system. We suggest the growing literature of EM effects on biological systems has significant implications to the cell and its functional aggregates.

  4. Modular biological function is most effectively captured by combining molecular interaction data types.

    PubMed

    Ames, Ryan M; Macpherson, Jamie I; Pinney, John W; Lovell, Simon C; Robertson, David L

    2013-01-01

    Large-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the

  5. Passive dendrites enable single neurons to compute linearly non-separable functions.

    PubMed

    Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

    2013-01-01

    Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non

  6. Passive Dendrites Enable Single Neurons to Compute Linearly Non-separable Functions

    PubMed Central

    Cazé, Romain Daniel; Humphries, Mark; Gutkin, Boris

    2013-01-01

    Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non

  7. Identification of potential drug targets based on a computational biology algorithm for venous thromboembolism.

    PubMed

    Xie, Ruiqiang; Li, Lei; Chen, Lina; Li, Wan; Chen, Binbin; Jiang, Jing; Huang, Hao; Li, Yiran; He, Yuehan; Lv, Junjie; He, Weiming

    2017-02-01

    Venous thromboembolism (VTE) is a common, fatal and frequently recurrent disease. Changes in the activity of different coagulation factors serve as a pathophysiological basis for the recurrent risk of VTE. Systems biology approaches provide a better understanding of the pathological mechanisms responsible for recurrent VTE. In this study, a novel computational method was presented to identify the recurrent risk modules (RRMs) based on the integration of expression profiles and human signaling network, which hold promise for achieving new and deeper insights into the mechanisms responsible for VTE. The results revealed that the RRMs had good classification performance to discriminate patients with recurrent VTE. The functional annotation analysis demonstrated that the RRMs played a crucial role in the pathogenesis of VTE. Furthermore, a variety of approved drug targets in the RRM M5 were related to VTE. Thus, the M5 may be applied to select potential drug targets for combination therapy and the extended treatment of VTE.

  8. Persistent Memory in Single Node Delay-Coupled Reservoir Computing.

    PubMed

    Kovac, André David; Koall, Maximilian; Pipa, Gordon; Toutounji, Hazem

    2016-01-01

    Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.

  9. Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy?

    PubMed Central

    Piergiorge, Rafael Mina; de Miranda, Antonio Basílio; Catanho, Marcos

    2017-01-01

    Abstract Since enzymes catalyze almost all chemical reactions that occur in living organisms, it is crucial that genes encoding such activities are correctly identified and functionally characterized. Several studies suggest that the fraction of enzymatic activities in which multiple events of independent origin have taken place during evolution is substantial. However, this topic is still poorly explored, and a comprehensive investigation of the occurrence, distribution, and implications of these events has not been done so far. Fundamental questions, such as how analogous enzymes originate, why so many events of independent origin have apparently occurred during evolution, and what are the reasons for the coexistence in the same organism of distinct enzymatic forms catalyzing the same reaction, remain unanswered. Also, several isofunctional enzymes are still not recognized as nonhomologous, even with substantial evidence indicating different evolutionary histories. In this work, we begin to investigate the biological significance of the cooccurrence of nonhomologous isofunctional enzymes in human metabolism, characterizing functional analogous enzymes identified in metabolic pathways annotated in the human genome. Our hypothesis is that the coexistence of multiple enzymatic forms might not be interpreted as functional redundancy. Instead, these enzymatic forms may be implicated in distinct (and probably relevant) biological roles. PMID:28854631

  10. hackseq: Catalyzing collaboration between biological and computational scientists via hackathon.

    PubMed

    2017-01-01

    hackseq ( http://www.hackseq.com) was a genomics hackathon with the aim of bringing together a diverse set of biological and computational scientists to work on collaborative bioinformatics projects. In October 2016, 66 participants from nine nations came together for three days for hackseq and collaborated on nine projects ranging from data visualization to algorithm development. The response from participants was overwhelmingly positive with 100% (n = 54) of survey respondents saying they would like to participate in future hackathons. We detail key steps for others interested in organizing a successful hackathon and report excerpts from each project.

  11. hackseq: Catalyzing collaboration between biological and computational scientists via hackathon

    PubMed Central

    2017-01-01

    hackseq ( http://www.hackseq.com) was a genomics hackathon with the aim of bringing together a diverse set of biological and computational scientists to work on collaborative bioinformatics projects. In October 2016, 66 participants from nine nations came together for three days for hackseq and collaborated on nine projects ranging from data visualization to algorithm development. The response from participants was overwhelmingly positive with 100% (n = 54) of survey respondents saying they would like to participate in future hackathons. We detail key steps for others interested in organizing a successful hackathon and report excerpts from each project. PMID:28417000

  12. Bioactive components and functional properties of biologically activated cereal grains: A bibliographic review.

    PubMed

    Singh, Arashdeep; Sharma, Savita

    2017-09-22

    Whole grains provide energy, nutrients, fibers, and bioactive compounds that may synergistically contribute to their protective effects. A wide range of these compounds is affected by germination. While some compounds, such as β-glucans are degraded, others, like antioxidants and total phenolics are increased by means of biological activation of grains. The water and oil absorption capacity as well as emulsion and foaming capacity of biologically activated grains are also improved. Application of biological activation of grains is of emerging interest, which may significantly enhance the nutritional, functional, and bioactive content of grains, as well as improve palatability of grain foods in a natural way. Therefore, biological activation of cereals can be a way to produce food grains enriched with health-promoting compounds and enhanced functional attributes.

  13. Function Package for Computing Quantum Resource Measures

    NASA Astrophysics Data System (ADS)

    Huang, Zhiming

    2018-05-01

    In this paper, we present a function package for to calculate quantum resource measures and dynamics of open systems. Our package includes common operators and operator lists, frequently-used functions for computing quantum entanglement, quantum correlation, quantum coherence, quantum Fisher information and dynamics in noisy environments. We briefly explain the functions of the package and illustrate how to use the package with several typical examples. We expect that this package is a useful tool for future research and education.

  14. 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.

  15. Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project.

    PubMed

    Hucka, M; Finney, A; Bornstein, B J; Keating, S M; Shapiro, B E; Matthews, J; Kovitz, B L; Schilstra, M J; Funahashi, A; Doyle, J C; Kitano, H

    2004-06-01

    Biologists are increasingly recognising that computational modelling is crucial for making sense of the vast quantities of complex experimental data that are now being collected. The systems biology field needs agreed-upon information standards if models are to be shared, evaluated and developed cooperatively. Over the last four years, our team has been developing the Systems Biology Markup Language (SBML) in collaboration with an international community of modellers and software developers. SBML has become a de facto standard format for representing formal, quantitative and qualitative models at the level of biochemical reactions and regulatory networks. In this article, we summarise the current and upcoming versions of SBML and our efforts at developing software infrastructure for supporting and broadening its use. We also provide a brief overview of the many SBML-compatible software tools available today.

  16. 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

  17. Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

    PubMed

    Singh, Nitesh Kumar; Ernst, Mathias; Liebscher, Volkmar; Fuellen, Georg; Taher, Leila

    2016-10-20

    The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

  18. Spaceborne computer executive routine functional design specification. Volume 2: Computer executive design for space station/base

    NASA Technical Reports Server (NTRS)

    Kennedy, J. R.; Fitzpatrick, W. S.

    1971-01-01

    The computer executive functional system design concepts derived from study of the Space Station/Base are presented. Information Management System hardware configuration as directly influencing the executive design is reviewed. The hardware configuration and generic executive design requirements are considered in detail in a previous report (System Configuration and Executive Requirements Specifications for Reusable Shuttle and Space Station/Base, 9/25/70). This report defines basic system primitives and delineates processes and process control. Supervisor states are considered for describing basic multiprogramming and multiprocessing systems. A high-level computer executive including control of scheduling, allocation of resources, system interactions, and real-time supervisory functions is defined. The description is oriented to provide a baseline for a functional simulation of the computer executive system.

  19. Sixth International Conference on Systems Biology (ICSB 2005)

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

    Professor Andrew Murray

    2005-10-22

    combined at least two of the categories in their own research activities. We define systems biology as a widening of focus in biology from individual genes or proteins to the complex networks of these molecules that allow cells and organisms to function. In the same way that conscious thought cannot be said to reside in any single neuron in the brain, simpler biological functions such as cell division arise from the interactions among many components in a network or ‘functional module’. For us, systems biology is characterized by the recognition that a higher-order description of biological function, accompanied by quantitative methods of analysis — often borrowed from disciplines such as physics, engineering, computer science or mathematics — can lead to the identification of general principles that underlie the structure, behavior, and evolution of cells and organisms. The heart of the conference were sessions on six topics: intracellular dynamics (featuring measurements on single cells, and their interpretation); biology by design (synthetic biology); intracellular networks (signal transduction and transcriptional regulation); multicellular networks (development and pattern formation); mechanics and scale in cellular behavior (featuring work on cytoskeletal mechanics, and on scaling relationships in biology); and evolution in action (including experimental evolution, of both real and artificial life-forms). Each session had four invited speakers; 23 of the 24 invited speakers attended (see below). We have selected these speakers not only for the interest of their research, but for their skills as communicators, thereby giving us the best chance of bridging the divides mentioned above. We also made a point of including women, younger investigators and people from outside the United States among the speakers. In addition to the invited speakers, we allotted time in the program for at least five contributed talks, which were selected from the poster submissions

  20. Discovering the intelligence in molecular biology.

    PubMed

    Uberbacher, E

    1995-12-01

    The Third International Conference on Intelligent Systems in Molecular Biology was truly an outstanding event. Computational methods in molecular biology have reached a new level of maturity and utility, resulting in many high-impact applications. The success of this meeting bodes well for the rapid and continuing development of computational methods, intelligent systems and information-based approaches for the biosciences. The basic technology, originally most often applied to 'feasibility' problems, is now dealing effectively with the most difficult real-world problems. Significant progress has been made in understanding protein-structure information, structural classification, and how functional information and the relevant features of active-site geometry can be gleaned from structures by automated computational approaches. The value and limits of homology-based methods, and the ability to classify proteins by structure in the absence of homology, have reached a new level of sophistication. New methods for covariation analysis in the folding of large structures such as RNAs have shown remarkably good results, indicating the long-term potential to understand very complicated molecules and multimolecular complexes using computational means. Novel methods, such as HMMs, context-free grammars and the uses of mutual information theory, have taken center stage as highly valuable tools in our quest to represent and characterize biological information. A focus on creative uses of intelligent systems technologies and the trend toward biological application will undoubtedly continue and grow at the 1996 ISMB meeting in St Louis.

  1. [Application of microelectronics CAD tools to synthetic biology].

    PubMed

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

    2017-02-01

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

  2. Normal form from biological motion despite impaired ventral stream function.

    PubMed

    Gilaie-Dotan, S; Bentin, S; Harel, M; Rees, G; Saygin, A P

    2011-04-01

    We explored the extent to which biological motion perception depends on ventral stream integration by studying LG, an unusual case of developmental visual agnosia. LG has significant ventral stream processing deficits but no discernable structural cortical abnormality. LG's intermediate visual areas and object-sensitive regions exhibit abnormal activation during visual object perception, in contrast to area V5/MT+ which responds normally to visual motion (Gilaie-Dotan, Perry, Bonneh, Malach, & Bentin, 2009). Here, in three studies we used point light displays, which require visual integration, in adaptive threshold experiments to examine LG's ability to detect form from biological and non-biological motion cues. LG's ability to detect and discriminate form from biological motion was similar to healthy controls. In contrast, he was significantly deficient in processing form from non-biological motion. Thus, LG can rely on biological motion cues to perceive human forms, but is considerably impaired in extracting form from non-biological motion. Finally, we found that while LG viewed biological motion, activity in a network of brain regions associated with processing biological motion was functionally correlated with his V5/MT+ activity, indicating that normal inputs from V5/MT+ might suffice to activate his action perception system. These results indicate that processing of biologically moving form can dissociate from other form processing in the ventral pathway. Furthermore, the present results indicate that integrative ventral stream processing is necessary for uncompromised processing of non-biological form from motion. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Roles for text mining in protein function prediction.

    PubMed

    Verspoor, Karin M

    2014-01-01

    The Human Genome Project has provided science with a hugely valuable resource: the blueprints for life; the specification of all of the genes that make up a human. While the genes have all been identified and deciphered, it is proteins that are the workhorses of the human body: they are essential to virtually all cell functions and are the primary mechanism through which biological function is carried out. Hence in order to fully understand what happens at a molecular level in biological organisms, and eventually to enable development of treatments for diseases where some aspect of a biological system goes awry, we must understand the functions of proteins. However, experimental characterization of protein function cannot scale to the vast amount of DNA sequence data now available. Computational protein function prediction has therefore emerged as a problem at the forefront of modern biology (Radivojac et al., Nat Methods 10(13):221-227, 2013).Within the varied approaches to computational protein function prediction that have been explored, there are several that make use of biomedical literature mining. These methods take advantage of information in the published literature to associate specific proteins with specific protein functions. In this chapter, we introduce two main strategies for doing this: association of function terms, represented as Gene Ontology terms (Ashburner et al., Nat Genet 25(1):25-29, 2000), to proteins based on information in published articles, and a paradigm called LEAP-FS (Literature-Enhanced Automated Prediction of Functional Sites) in which literature mining is used to validate the predictions of an orthogonal computational protein function prediction method.

  4. Highlights from the 6th International Society for Computational Biology Student Council Symposium at the 18th Annual International Conference on Intelligent Systems for Molecular Biology

    PubMed Central

    2010-01-01

    This meeting report gives an overview of the keynote lectures and a selection of the student oral and poster presentations at the 6th International Society for Computational Biology Student Council Symposium that was held as a precursor event to the annual international conference on Intelligent Systems for Molecular Biology (ISMB). The symposium was held in Boston, MA, USA on July 9th, 2010.

  5. 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,…

  6. 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.

  7. Biological and functional relevance of CASP predictions.

    PubMed

    Liu, Tianyun; Ish-Shalom, Shirbi; Torng, Wen; Lafita, Aleix; Bock, Christian; Mort, Matthew; Cooper, David N; Bliven, Spencer; Capitani, Guido; Mooney, Sean D; Altman, Russ B

    2018-03-01

    Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template. © 2017 The Authors Proteins: Structure, Function and Bioinformatics Published by Wiley Periodicals, Inc.

  8. Motion as a source of environmental information: a fresh view on biological motion computation by insect brains

    PubMed Central

    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. PMID:25389392

  9. 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.

  10. f1: a code to compute Appell's F1 hypergeometric function

    NASA Astrophysics Data System (ADS)

    Colavecchia, F. D.; Gasaneo, G.

    2004-02-01

    In this work we present the FORTRAN code to compute the hypergeometric function F1( α, β1, β2, γ, x, y) of Appell. The program can compute the F1 function for real values of the variables { x, y}, and complex values of the parameters { α, β1, β2, γ}. The code uses different strategies to calculate the function according to the ideas outlined in [F.D. Colavecchia et al., Comput. Phys. Comm. 138 (1) (2001) 29]. Program summaryTitle of the program: f1 Catalogue identifier: ADSJ Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSJ Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Licensing provisions: none Computers: PC compatibles, SGI Origin2∗ Operating system under which the program has been tested: Linux, IRIX Programming language used: Fortran 90 Memory required to execute with typical data: 4 kbytes No. of bits in a word: 32 No. of bytes in distributed program, including test data, etc.: 52 325 Distribution format: tar gzip file External subprograms used: Numerical Recipes hypgeo [W.H. Press et al., Numerical Recipes in Fortran 77, Cambridge Univ. Press, 1996] or chyp routine of R.C. Forrey [J. Comput. Phys. 137 (1997) 79], rkf45 [L.F. Shampine and H.H. Watts, Rep. SAND76-0585, 1976]. Keywords: Numerical methods, special functions, hypergeometric functions, Appell functions, Gauss function Nature of the physical problem: Computing the Appell F1 function is relevant in atomic collisions and elementary particle physics. It is usually the result of multidimensional integrals involving Coulomb continuum states. Method of solution: The F1 function has a convergent-series definition for | x|<1 and | y|<1, and several analytic continuations for other regions of the variable space. The code tests the values of the variables and selects one of the precedent cases. In the convergence region the program uses the series definition near the origin of coordinates, and a numerical integration of the third-order differential

  11. Biologically important conformational features of DNA as interpreted by quantum mechanics and molecular mechanics computations of its simple fragments.

    PubMed

    Poltev, V; Anisimov, V M; Dominguez, V; Gonzalez, E; Deriabina, A; Garcia, D; Rivas, F; Polteva, N A

    2018-02-01

    Deciphering the mechanism of functioning of DNA as the carrier of genetic information requires identifying inherent factors determining its structure and function. Following this path, our previous DFT studies attributed the origin of unique conformational characteristics of right-handed Watson-Crick duplexes (WCDs) to the conformational profile of deoxydinucleoside monophosphates (dDMPs) serving as the minimal repeating units of DNA strand. According to those findings, the directionality of the sugar-phosphate chain and the characteristic ranges of dihedral angles of energy minima combined with the geometric differences between purines and pyrimidines determine the dependence on base sequence of the three-dimensional (3D) structure of WCDs. This work extends our computational study to complementary deoxydinucleotide-monophosphates (cdDMPs) of non-standard conformation, including those of Z-family, Hoogsteen duplexes, parallel-stranded structures, and duplexes with mispaired bases. For most of these systems, except Z-conformation, computations closely reproduce experimental data within the tolerance of characteristic limits of dihedral parameters for each conformation family. Computation of cdDMPs with Z-conformation reveals that their experimental structures do not correspond to the internal energy minimum. This finding establishes the leading role of external factors in formation of the Z-conformation. Energy minima of cdDMPs of non-Watson-Crick duplexes demonstrate different sequence-dependence features than those known for WCDs. The obtained results provide evidence that the biologically important regularities of 3D structure distinguish WCDs from duplexes having non-Watson-Crick nucleotide pairing.

  12. Computing health quality measures using Informatics for Integrating Biology and the Bedside.

    PubMed

    Klann, Jeffrey G; Murphy, Shawn N

    2013-04-19

    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. 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. 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. 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. 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 health. However, its current form might allow

  13. 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

  14. The Impact of Collective Molecular Dynamics on Physiological and Biological Functionalities of Artificial and Biological Membranes

    NASA Astrophysics Data System (ADS)

    Rheinstadter, Maikel

    2008-03-01

    We use neutron, X-ray and light scattering techniques to determine dynamical and structural properties of artificial and biological membranes. The combination of various techniques enlarges the window to length scales from the nearest-neighbor distances of lipid molecules to more than 10-6m, covering time scales from about 0.1 ps to 1 s. The main research objective is to quantify collective molecular fluctuations in these systems and to establish relationships to physiological and biological functions of the bilayers, such as transmembrane transport. The motivation for this project is twofold: 1) By understanding fundamental properties of bilayers at the microscopic and mesoscopic level, we aim to tailor membranes with specific properties such as permeability and elasticity. 2) By relating dynamical fluctuations to physiological and biological functions, we can gain a deeper understanding of the bilayers on a molecular scale that may help optimizing the transmembrane transport of certain drugs. We show how bilayer permeability, elasticity and inter protein excitations can be determined from the experiments. M.C. Rheinstädter et al., Phys. Rev. Lett. 93, 108107 (2004); Phys. Rev. Lett. 97, 048103 (2006); Phys. Rev. E 75, 011907 (2007);J. Vac. Soc. Technol. A 24, 1191 (2006).

  15. Systems Biology for Organotypic Cell Cultures

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

    Grego, Sonia; Dougherty, Edward R.; Alexander, Francis J.

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the

  16. RESEARCH STRATEGIES FOR THE APPLICATION OF THE TECHNIQUES OF COMPUTATIONAL BIOLOGICAL CHEMISTRY TO ENVIRONMENTAL PROBLEMS

    EPA Science Inventory

    On October 25 and 26, 1984, the U.S. EPA sponsored a workshop to consider the potential applications of the techniques of computational biological chemistry to problems in environmental health. Eleven extramural scientists from the various related disciplines and a similar number...

  17. Computational Psychiatry

    PubMed Central

    Wang, Xiao-Jing; Krystal, John H.

    2014-01-01

    Psychiatric disorders such as autism and schizophrenia arise from abnormalities in brain systems that underlie cognitive, emotional and social functions. The brain is enormously complex and its abundant feedback loops on multiple scales preclude intuitive explication of circuit functions. In close interplay with experiments, theory and computational modeling are essential for understanding how, precisely, neural circuits generate flexible behaviors and their impairments give rise to psychiatric symptoms. This Perspective highlights recent progress in applying computational neuroscience to the study of mental disorders. We outline basic approaches, including identification of core deficits that cut across disease categories, biologically-realistic modeling bridging cellular and synaptic mechanisms with behavior, model-aided diagnosis. The need for new research strategies in psychiatry is urgent. Computational psychiatry potentially provides powerful tools for elucidating pathophysiology that may inform both diagnosis and treatment. To achieve this promise will require investment in cross-disciplinary training and research in this nascent field. PMID:25442941

  18. Petri net modelling of biological networks.

    PubMed

    Chaouiya, Claudine

    2007-07-01

    Mathematical modelling is increasingly used to get insights into the functioning of complex biological networks. In this context, Petri nets (PNs) have recently emerged as a promising tool among the various methods employed for the modelling and analysis of molecular networks. PNs come with a series of extensions, which allow different abstraction levels, from purely qualitative to more complex quantitative models. Noteworthily, each of these models preserves the underlying graph, which depicts the interactions between the biological components. This article intends to present the basics of the approach and to foster the potential role PNs could play in the development of the computational systems biology.

  19. An online model composition tool for system biology models

    PubMed Central

    2013-01-01

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

  20. 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)

  1. Persistent Memory in Single Node Delay-Coupled Reservoir Computing

    PubMed Central

    Pipa, Gordon; Toutounji, Hazem

    2016-01-01

    Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality. PMID:27783690

  2. Biological and functional relevance of CASP predictions

    PubMed Central

    Liu, Tianyun; Ish‐Shalom, Shirbi; Torng, Wen; Lafita, Aleix; Bock, Christian; Mort, Matthew; Cooper, David N; Bliven, Spencer; Capitani, Guido; Mooney, Sean D.

    2017-01-01

    Abstract Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo‐sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo‐sites), and Ten sites containing important motifs, loops, or key residues with important disease‐associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best‐ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand‐binding sites, most prediction methods have higher performance on apo‐sites than holo‐sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein‐protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein‐protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template. PMID:28975675

  3. Flavonoids: biosynthesis, biological functions, and biotechnological applications

    PubMed Central

    Falcone Ferreyra, María L.; Rius, Sebastián P.; Casati, Paula

    2012-01-01

    Flavonoids are widely distributed secondary metabolites with different metabolic functions in plants. The elucidation of the biosynthetic pathways, as well as their regulation by MYB, basic helix-loop-helix (bHLH), and WD40-type transcription factors, has allowed metabolic engineering of plants through the manipulation of the different final products with valuable applications. The present review describes the regulation of flavonoid biosynthesis, as well as the biological functions of flavonoids in plants, such as in defense against UV-B radiation and pathogen infection, nodulation, and pollen fertility. In addition, we discuss different strategies and achievements through the genetic engineering of flavonoid biosynthesis with implication in the industry and the combinatorial biosynthesis in microorganisms by the reconstruction of the pathway to obtain high amounts of specific compounds. PMID:23060891

  4. Supporting High School Student Accomplishment of Biology Content Using Interactive Computer-Based Curricular Case Studies

    NASA Astrophysics Data System (ADS)

    Oliver, Joseph Steve; Hodges, Georgia W.; Moore, James N.; Cohen, Allan; Jang, Yoonsun; Brown, Scott A.; Kwon, Kyung A.; Jeong, Sophia; Raven, Sara P.; Jurkiewicz, Melissa; Robertson, Tom P.

    2017-11-01

    Research into the efficacy of modules featuring dynamic visualizations, case studies, and interactive learning environments is reported here. This quasi-experimental 2-year study examined the implementation of three interactive computer-based instructional modules within a curricular unit covering cellular biology concepts in an introductory high school biology course. The modules featured dynamic visualizations and focused on three processes that underlie much of cellular biology: diffusion, osmosis, and filtration. Pre-tests and post-tests were used to assess knowledge growth across the unit. A mixture Rasch model analysis of the post-test data revealed two groups of students. In both years of the study, a large proportion of the students were classified as low-achieving based on their pre-test scores. The use of the modules in the Cell Unit in year 2 was associated with a much larger proportion of the students having transitioned to the high-achieving group than in year 1. In year 2, the same teachers taught the same concepts as year 1 but incorporated the interactive computer-based modules into the cell biology unit of the curriculum. In year 2, 67% of students initially classified as low-achieving were classified as high-achieving at the end of the unit. Examination of responses to assessments embedded within the modules as well as post-test items linked transition to the high-achieving group with correct responses to items that both referenced the visualization and the contextualization of that visualization within the module. This study points to the importance of dynamic visualization within contextualized case studies as a means to support student knowledge acquisition in biology.

  5. 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.

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

    PubMed Central

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

    2016-01-01

    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

  7. 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'. © 2016 The Author(s).

  8. Functional tissue engineering of tendon: Establishing biological success criteria for improving tendon repair.

    PubMed

    Breidenbach, Andrew P; Gilday, Steven D; Lalley, Andrea L; Dyment, Nathaniel A; Gooch, Cynthia; Shearn, Jason T; Butler, David L

    2014-06-27

    Improving tendon repair using Functional Tissue Engineering (FTE) principles has been the focus of our laboratory over the last decade. Although our primary goals were initially focused only on mechanical outcomes, we are now carefully assessing the biological properties of our tissue-engineered tendon repairs so as to link biological influences with mechanics. However, given the complexities of tendon development and healing, it remains challenging to determine which aspects of tendon biology are the most important to focus on in the context of tissue engineering. To address this problem, we have formalized a strategy to identify, prioritize, and evaluate potential biological success criteria for tendon repair. We have defined numerous biological properties of normal tendon relative to cellular phenotype, extracellular matrix and tissue ultra-structure that we would like to reproduce in our tissue-engineered repairs and prioritized these biological criteria by examining their relative importance during both normal development and natural tendon healing. Here, we propose three specific biological criteria which we believe are essential for normal tendon function: (1) scleraxis-expressing cells; (2) well-organized and axially-aligned collagen fibrils having bimodal diameter distribution; and (3) a specialized tendon-to-bone insertion site. Moving forward, these biological success criteria will be used in conjunction with our already established mechanical success criteria to evaluate the effectiveness of our tissue-engineered tendon repairs. © 2013 Published by Elsevier Ltd.

  9. EpiSweep: Computationally Driven Reengineering of Therapeutic Proteins to Reduce Immunogenicity While Maintaining Function.

    PubMed

    Choi, Yoonjoo; Verma, Deeptak; Griswold, Karl E; Bailey-Kellogg, Chris

    2017-01-01

    Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics render them subject to immune surveillance within the patient's body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity.To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure-based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new protein

  10. Probing the mutational interplay between primary and promiscuous protein functions: a computational-experimental approach.

    PubMed

    Garcia-Seisdedos, Hector; Ibarra-Molero, Beatriz; Sanchez-Ruiz, Jose M

    2012-01-01

    Protein promiscuity is of considerable interest due its role in adaptive metabolic plasticity, its fundamental connection with molecular evolution and also because of its biotechnological applications. Current views on the relation between primary and promiscuous protein activities stem largely from laboratory evolution experiments aimed at increasing promiscuous activity levels. Here, on the other hand, we attempt to assess the main features of the simultaneous modulation of the primary and promiscuous functions during the course of natural evolution. The computational/experimental approach we propose for this task involves the following steps: a function-targeted, statistical coupling analysis of evolutionary data is used to determine a set of positions likely linked to the recruitment of a promiscuous activity for a new function; a combinatorial library of mutations on this set of positions is prepared and screened for both, the primary and the promiscuous activities; a partial-least-squares reconstruction of the full combinatorial space is carried out; finally, an approximation to the Pareto set of variants with optimal primary/promiscuous activities is derived. Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities. We show that this scenario can be most simply explained on the basis of the conformational diversity hypothesis, although alternative interpretations cannot be ruled out. Overall, the results reported may help clarify the mechanisms of the evolution of new functions. From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous

  11. Probing the Mutational Interplay between Primary and Promiscuous Protein Functions: A Computational-Experimental Approach

    PubMed Central

    Garcia-Seisdedos, Hector; Ibarra-Molero, Beatriz; Sanchez-Ruiz, Jose M.

    2012-01-01

    Protein promiscuity is of considerable interest due its role in adaptive metabolic plasticity, its fundamental connection with molecular evolution and also because of its biotechnological applications. Current views on the relation between primary and promiscuous protein activities stem largely from laboratory evolution experiments aimed at increasing promiscuous activity levels. Here, on the other hand, we attempt to assess the main features of the simultaneous modulation of the primary and promiscuous functions during the course of natural evolution. The computational/experimental approach we propose for this task involves the following steps: a function-targeted, statistical coupling analysis of evolutionary data is used to determine a set of positions likely linked to the recruitment of a promiscuous activity for a new function; a combinatorial library of mutations on this set of positions is prepared and screened for both, the primary and the promiscuous activities; a partial-least-squares reconstruction of the full combinatorial space is carried out; finally, an approximation to the Pareto set of variants with optimal primary/promiscuous activities is derived. Application of the approach to the emergence of folding catalysis in thioredoxin scaffolds reveals an unanticipated scenario: diverse patterns of primary/promiscuous activity modulation are possible, including a moderate (but likely significant in a biological context) simultaneous enhancement of both activities. We show that this scenario can be most simply explained on the basis of the conformational diversity hypothesis, although alternative interpretations cannot be ruled out. Overall, the results reported may help clarify the mechanisms of the evolution of new functions. From a different viewpoint, the partial-least-squares-reconstruction/Pareto-set-prediction approach we have introduced provides the computational basis for an efficient directed-evolution protocol aimed at the simultaneous

  12. EpiSweep: Computationally-driven Reengineering of Therapeutic Proteins to Reduce immunogenicity while Maintaining Function

    PubMed Central

    Choi, Yoonjoo; Verma, Deeptak; Griswold, Karl E.; Bailey-Kellogg, Chris

    2016-01-01

    Therapeutic proteins are yielding ever more advanced and efficacious new drugs, but the biological origins of these highly effective therapeutics renders them subject to immune surveillance within the patient’s body. When recognized by the immune system as a foreign agent, protein drugs elicit a coordinated response that can manifest a range of clinical complications including rapid drug clearance, loss of functionality and efficacy, delayed infusion-like allergic reactions, more serious anaphylactic shock, and even induced auto-immunity. It is thus often necessary to deimmunize an exogenous protein in order to enable its clinical application; critically, the deimmunization process must also maintain the desired therapeutic activity. To meet the growing need for effective, efficient, and broadly applicable protein deimmunization technologies, we have developed the EpiSweep suite of protein design algorithms. EpiSweep seamlessly integrates computational prediction of immunogenic T cell epitopes with sequence- or structure- based assessment of the impacts of mutations on protein stability and function, in order to select combinations of mutations that make Pareto optimal trade-offs between the competing goals of low immunogenicity and high-level function. The methods are applicable both to the design of individual functionally deimmunized variants as well as the design of combinatorial libraries enriched in functionally deimmunized variants. After validating EpiSweep in a series of retrospective case studies providing comparisons to conventional approaches to T cell epitope deletion, we have experimentally demonstrated it to be highly effective in prospective application to deimmunization of a number of different therapeutic candidates. We conclude that our broadly applicable computational protein design algorithms guide the engineer towards the most promising deimmunized therapeutic candidates, and thereby have the potential to accelerate development of new

  13. Tools for building a comprehensive modeling system for virtual screening under real biological conditions: The Computational Titration algorithm.

    PubMed

    Kellogg, Glen E; Fornabaio, Micaela; Chen, Deliang L; Abraham, Donald J; Spyrakis, Francesca; Cozzini, Pietro; Mozzarelli, Andrea

    2006-05-01

    Computational tools utilizing a unique empirical modeling system based on the hydrophobic effect and the measurement of logP(o/w) (the partition coefficient for solvent transfer between 1-octanol and water) are described. The associated force field, Hydropathic INTeractions (HINT), contains much rich information about non-covalent interactions in the biological environment because of its basis in an experiment that measures interactions in solution. HINT is shown to be the core of an evolving virtual screening system that is capable of taking into account a number of factors often ignored such as entropy, effects of solvent molecules at the active site, and the ionization states of acidic and basic residues and ligand functional groups. The outline of a comprehensive modeling system for virtual screening that incorporates these features is described. In addition, a detailed description of the Computational Titration algorithm is provided. As an example, three complexes of dihydrofolate reductase (DHFR) are analyzed with our system and these results are compared with the experimental free energies of binding.

  14. 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.

  15. Rational protein design: developing next-generation biological therapeutics and nanobiotechnological tools.

    PubMed

    Wilson, Corey J

    2015-01-01

    Proteins are the most functionally diverse macromolecules observed in nature, participating in a broad array of catalytic, biosensing, transport, scaffolding, and regulatory functions. Fittingly, proteins have become one of the most promising nanobiotechnological tools to date, and through the use of recombinant DNA and other laboratory methods we have produced a vast number of biological therapeutics derived from human genes. Our emerging ability to rationally design proteins (e.g., via computational methods) holds the promise of significantly expanding the number and diversity of protein therapies and has opened the gateway to realizing true and uncompromised personalized medicine. In the last decade computational protein design has been transformed from a set of fundamental strategies to stringently test our understanding of the protein structure-function relationship, to practical tools for developing useful biological processes, nano-devices, and novel therapeutics. As protein design strategies improve (i.e., in terms of accuracy and efficiency) clinicians will be able to leverage individual genetic data and biological metrics to develop and deliver personalized protein therapeutics with minimal delay. © 2014 Wiley Periodicals, Inc.

  16. 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.

  17. Positive Wigner functions render classical simulation of quantum computation efficient.

    PubMed

    Mari, A; Eisert, J

    2012-12-07

    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.

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

    PubMed Central

    2011-01-01

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

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

    PubMed

    Quinn, Terrance; Sinkala, Zachariah

    2014-01-01

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

  20. PARPs and ADP-ribosylation: recent advances linking molecular functions to biological outcomes

    PubMed Central

    Gupte, Rebecca; Liu, Ziying; Kraus, W. Lee

    2017-01-01

    The discovery of poly(ADP-ribose) >50 years ago opened a new field, leading the way for the discovery of the poly(ADP-ribose) polymerase (PARP) family of enzymes and the ADP-ribosylation reactions that they catalyze. Although the field was initially focused primarily on the biochemistry and molecular biology of PARP-1 in DNA damage detection and repair, the mechanistic and functional understanding of the role of PARPs in different biological processes has grown considerably of late. This has been accompanied by a shift of focus from enzymology to a search for substrates as well as the first attempts to determine the functional consequences of site-specific ADP-ribosylation on those substrates. Supporting these advances is a host of methodological approaches from chemical biology, proteomics, genomics, cell biology, and genetics that have propelled new discoveries in the field. New findings on the diverse roles of PARPs in chromatin regulation, transcription, RNA biology, and DNA repair have been complemented by recent advances that link ADP-ribosylation to stress responses, metabolism, viral infections, and cancer. These studies have begun to reveal the promising ways in which PARPs may be targeted therapeutically for the treatment of disease. In this review, we discuss these topics and relate them to the future directions of the field. PMID:28202539

  1. Carotenoids from Marine Organisms: Biological Functions and Industrial Applications

    PubMed Central

    Galasso, Christian; Corinaldesi, Cinzia; Sansone, Clementina

    2017-01-01

    As is the case for terrestrial organisms, carotenoids represent the most common group of pigments in marine environments. They are generally biosynthesized by all autotrophic marine organisms, such as bacteria and archaea, algae and fungi. Some heterotrophic organisms also contain carotenoids probably accumulated from food or partly modified through metabolic reactions. These natural pigments are divided into two chemical classes: carotenes (such as lycopene and α- and β-carotene) that are composed of hydrogen and carbon; xanthophylls (such as astaxanthin, fucoxanthin and lutein), which are constituted by hydrogen, carbon and oxygen. Carotenoids, as antioxidant compounds, assume a key role in the protection of cells. In fact, quenching of singlet oxygen, light capture and photosynthesis protection are the most relevant biological functions of carotenoids. The present review aims at describing (i) the biological functions of carotenoids and their benefits for human health, (ii) the most common carotenoids from marine organisms and (iii) carotenoids having large success in pharmaceutical, nutraceutical and cosmeceutical industries, highlighting the scientific progress in marine species cultivation for natural pigments production. PMID:29168774

  2. 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

  3. On computing special functions in marine engineering

    NASA Astrophysics Data System (ADS)

    Constantinescu, E.; Bogdan, M.

    2015-11-01

    Important modeling applications in marine engineering conduct us to a special class of solutions for difficult differential equations with variable coefficients. In order to be able to solve and implement such models (in wave theory, in acoustics, in hydrodynamics, in electromagnetic waves, but also in many other engineering fields), it is necessary to compute so called special functions: Bessel functions, modified Bessel functions, spherical Bessel functions, Hankel functions. The aim of this paper is to develop numerical solutions in Matlab for the above mentioned special functions. Taking into account the main properties for Bessel and modified Bessel functions, we shortly present analytically solutions (where possible) in the form of series. Especially it is studied the behavior of these special functions using Matlab facilities: numerical solutions and plotting. Finally, it will be compared the behavior of the special functions and point out other directions for investigating properties of Bessel and spherical Bessel functions. The asymptotic forms of Bessel functions and modified Bessel functions allow determination of important properties of these functions. The modified Bessel functions tend to look more like decaying and growing exponentials.

  4. Neurotrophin Propeptides: Biological Functions and Molecular Mechanisms.

    PubMed

    Rafieva, Lola M; Gasanov, Eugene V

    2016-01-01

    Neurotrophins constitute a family of growth factors that play a key role in the regulation of the development and function of the central and peripheral nervous systems. A common feature of all the neurotrophins is their synthesis in cells as long precursors (pre-pro-neurotrophins) that contain an N-terminal signal peptide, a following propeptide and the mature neurotrophin. Although the signal peptide functions have been well studied, the role of neurotrophin propeptides is not so clear. Here, we briefly summarize the biochemistry of neurotrophin propeptides, including their role as folding-assistants for the mature factor and their role in processing and in secretion of neurotrophins. In the main part of the review we summarize our current state of knowledge of the biological activity of neurotrophin propeptides, their possible mechanisms of action, and their potential influence on the activity of the mature neurotrophins.

  5. A large-scale evaluation of computational protein function prediction

    PubMed Central

    Radivojac, Predrag; Clark, Wyatt T; Ronnen Oron, Tal; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kassner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Böhm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo

    2013-01-01

    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools. PMID:23353650

  6. 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

  7. 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.

  8. Biological and Environmental Research Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Biological and Environmental Research, March 28-31, 2016, Rockville, Maryland

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

    Arkin, Adam; Bader, David C.; Coffey, Richard

    Understanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOEmore » began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.« less

  9. Next Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational, and Systems Biology (Final Report)

    EPA Science Inventory

    EPA announced the release of the final report, Next Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational, and Systems Biology. This report describes new approaches that are faster, less resource intensive, and more robust that can help ...

  10. Re-evaluating concepts of biological function in clinical medicine: towards a new naturalistic theory of disease.

    PubMed

    Chin-Yee, Benjamin; Upshur, Ross E G

    2017-08-01

    Naturalistic theories of disease appeal to concepts of biological function, and use the notion of dysfunction as the basis of their definitions. Debates in the philosophy of biology demonstrate how attributing functions in organisms and establishing the function-dysfunction distinction is by no means straightforward. This problematization of functional ascription has undermined naturalistic theories and led some authors to abandon the concept of dysfunction, favoring instead definitions based in normative criteria or phenomenological approaches. Although this work has enhanced our understanding of disease and illness, we need not necessarily abandon naturalistic concepts of function and dysfunction in the disease debate. This article attempts to move towards a new naturalistic theory of disease that overcomes the limitations of previous definitions and offers advantages in the clinical setting. Our approach involves a re-evaluation of concepts of biological function employed by naturalistic theories. Drawing on recent insights from the philosophy of biology, we develop a contextual and evaluative account of function that is better suited to clinical medicine and remains consistent with contemporary naturalism. We also show how an updated naturalistic view shares important affinities with normativist and phenomenological positions, suggesting a possibility for consilience in the disease debate.

  11. High resolution computational on-chip imaging of biological samples using sparsity constraint (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Rivenson, Yair; Wu, Chris; Wang, Hongda; Zhang, Yibo; Ozcan, Aydogan

    2017-03-01

    Microscopic imaging of biological samples such as pathology slides is one of the standard diagnostic methods for screening various diseases, including cancer. These biological samples are usually imaged using traditional optical microscopy tools; however, the high cost, bulkiness and limited imaging throughput of traditional microscopes partially restrict their deployment in resource-limited settings. In order to mitigate this, we previously demonstrated a cost-effective and compact lens-less on-chip microscopy platform with a wide field-of-view of >20-30 mm^2. The lens-less microscopy platform has shown its effectiveness for imaging of highly connected biological samples, such as pathology slides of various tissue samples and smears, among others. This computational holographic microscope requires a set of super-resolved holograms acquired at multiple sample-to-sensor distances, which are used as input to an iterative phase recovery algorithm and holographic reconstruction process, yielding high-resolution images of the samples in phase and amplitude channels. Here we demonstrate that in order to reconstruct clinically relevant images with high resolution and image contrast, we require less than 50% of the previously reported nominal number of holograms acquired at different sample-to-sensor distances. This is achieved by incorporating a loose sparsity constraint as part of the iterative holographic object reconstruction. We demonstrate the success of this sparsity-based computational lens-less microscopy platform by imaging pathology slides of breast cancer tissue and Papanicolaou (Pap) smears.

  12. Computer vision in cell biology.

    PubMed

    Danuser, Gaudenz

    2011-11-23

    Computer vision refers to the theory and implementation of artificial systems that extract information from images to understand their content. Although computers are widely used by cell biologists for visualization and measurement, interpretation of image content, i.e., the selection of events worth observing and the definition of what they mean in terms of cellular mechanisms, is mostly left to human intuition. This Essay attempts to outline roles computer vision may play and should play in image-based studies of cellular life. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Computer-based fluorescence quantification: a novel approach to study nucleolar biology

    PubMed Central

    2011-01-01

    Background Nucleoli are composed of possibly several thousand different proteins and represent the most conspicuous compartments in the nucleus; they play a crucial role in the proper execution of many cellular processes. As such, nucleoli carry out ribosome biogenesis and sequester or associate with key molecules that regulate cell cycle progression, tumorigenesis, apoptosis and the stress response. Nucleoli are dynamic compartments that are characterized by a constant flux of macromolecules. Given the complex and dynamic composition of the nucleolar proteome, it is challenging to link modifications in nucleolar composition to downstream effects. Results In this contribution, we present quantitative immunofluorescence methods that rely on computer-based image analysis. We demonstrate the effectiveness of these techniques by monitoring the dynamic association of proteins and RNA with nucleoli under different physiological conditions. Thus, the protocols described by us were employed to study stress-dependent changes in the nucleolar concentration of endogenous and GFP-tagged proteins. Furthermore, our methods were applied to measure de novo RNA synthesis that is associated with nucleoli. We show that the techniques described here can be easily combined with automated high throughput screening (HTS) platforms, making it possible to obtain large data sets and analyze many of the biological processes that are located in nucleoli. Conclusions Our protocols set the stage to analyze in a quantitative fashion the kinetics of shuttling nucleolar proteins, both at the single cell level as well as for a large number of cells. Moreover, the procedures described here are compatible with high throughput image acquisition and analysis using HTS automated platforms, thereby providing the basis to quantify nucleolar components and activities for numerous samples and experimental conditions. Together with the growing amount of information obtained for the nucleolar proteome

  14. The relationship between oral health status and biological and psychosocial function in the bedridden elderly.

    PubMed

    Hanada, N; Tada, A

    2001-01-01

    The present study was conducted in order to determine what item of biological and psychosocial function is related to oral health status in the bedridden elderly. The subjects were 94 elderly individuals (30 males, 64 females) who had been admitted to a nursing home in Chiba city, Japan. We assessed the number of remaining teeth and the number of functional teeth as oral health status variables. Biological and psychosocial function levels were determined using the functional independence measure method developed by the State University of New York at Buffalo. More than 70% of subjects had less than ten remaining teeth. Almost all subjects needed prosthesis treatment. More than 50% of subjects had 27 or less functional teeth. Mann-Whitney U-test and logistic regression models showed that 'expression' was concerned with the number of remaining teeth and 'bladder management', 'locomotion', 'transfers' were related to the number of functional teeth. These data suggest close relation between oral health status and biological and psychosocial function levels in the bedridden elderly.

  15. Deep hierarchies in the primate visual cortex: what can we learn for computer vision?

    PubMed

    Krüger, Norbert; Janssen, Peter; Kalkan, Sinan; Lappe, Markus; Leonardis, Ales; Piater, Justus; Rodríguez-Sánchez, Antonio J; Wiskott, Laurenz

    2013-08-01

    Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision-based navigation and manipulation. This paper reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchical processing in the primate visual system is characterized by a sequence of different levels of processing (on the order of 10) that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today's mainstream computer vision. We hope that the functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.

  16. Advanced techniques in placental biology -- workshop report.

    PubMed

    Nelson, D M; Sadovsky, Y; Robinson, J M; Croy, B A; Rice, G; Kniss, D A

    2006-04-01

    Major advances in placental biology have been realized as new technologies have been developed and existing methods have been refined in many areas of biological research. Classical anatomy and whole-organ physiology tools once used to analyze placental structure and function have been supplanted by more sophisticated techniques adapted from molecular biology, proteomics, and computational biology and bioinformatics. In addition, significant refinements in morphological study of the placenta and its constituent cell types have improved our ability to assess form and function in highly integrated manner. To offer an overview of modern technologies used by investigators to study the placenta, this workshop: Advanced techniques in placental biology, assembled experts who discussed fundamental principles and real time examples of four separate methodologies. Y. Sadovsky presented the principles of microRNA function as an endogenous mechanism of gene regulation. J. Robinson demonstrated the utility of correlative microscopy in which light-level and transmission electron microscopy are combined to provide cellular and subcellular views of placental cells. A. Croy provided a lecture on the use of microdissection techniques which are invaluable for isolating very small subsets of cell types for molecular analysis. Finally, G. Rice presented an overview methods on profiling of complex protein mixtures within tissue and/or fluid samples that, when refined, will offer databases that will underpin a systems approach to modern trophoblast biology.

  17. 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

  18. 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…

  19. Paul Langerhans: a prilgrim "traveling" from functional histology to marine biology.

    PubMed

    Raica, Marius; Cimpean, Anca Maria

    2017-06-01

    The nineteenth century was the time of a real revolution in science and medicine. A lot of seminal discoveries in medicine and biology were done in this time, and many of them were coincident with the introduction of the compound microscope by Hermann van Deijl and the standard histological technique by Paul Ehrlich. The main tissue types and individual cells were characterized and originally classified more than hundred years ago, although less attention was paid to their basic functions. This was mainly due to the modality of tissue specimen processing that allowed particularly detailed descriptive studies. Even so, we can notice some attempts to correlate the structure with the function. The German scientist Paul Langerhans, well-known for the discovery of Langerhans islets of the pancreas and Langerhans cells from the epidermis, tried to change the conventional fate of morphological studies introducing in his works functional hypothesis based on traditional microscopic observations even from the beginning of his scientific career. Paul Langerhans was a complex personality of the second half of the nineteenth century, not only in medicine, but also in other fields of biology. In the present review, presented is the life and research activity of Paul Langerhans, not only because of the importance of his discoveries, but also for perspectives that were opened by these findings in unexpected fields of medicine and biology.

  20. 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)

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

    PubMed

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

    2015-05-01

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

  2. Knowledge base and functionality of concepts of some Filipino biology teachers in five biology topics

    NASA Astrophysics Data System (ADS)

    Barquilla, Manuel B.

    2018-01-01

    This mixed research, is a snapshot of some Filipino Biology teachers' knowledge structure and how their concepts of the five topics in Biology (Photosynthesis, Cellular Respiration, human reproductive system, Mendelian genetics and NonMendelian genetics) functions and develops inside a biology classroom. The study focuses on the six biology teachers and a total of 222 students in their respective classes. Of the Six (6) teachers, three (3) are under the Science curriculum and the other three (3) are under regular curriculum in both public and private schools in Iligan city and Lanao del Norte, Philippines. The study utilized classroom discourses, concept maps, interpretative case-study method, bracketing method, and concept analysis for qualitative part; the quantitative part uses a nonparametric statistical tool, Kendall's tau Coefficient for determining relationship and congruency while measures of central tendencies and dispersion (mean, and standard deviation) for concept maps scores interpretation. Knowledge Base of Biology teachers were evaluated by experts in field of specialization having a doctorate program (e.g. PhD in Genetics) and PhD Biology candidates. The data collection entailed seven (7) months immersion: one (1) month for preliminary phase for the researcher to gain teachers' and students' confidence and the succeeding six (6) months for main observation and data collection. The evaluation of teachers' knowledge base by experts indicated that teachers' knowledge of (65%) is lower than the minimum (75%) recommended by ABD-el-Khalick and Boujaoude (1997). Thus, the experts believe that content knowledge of the teachers is hardly adequate for their teaching assignment. Moreover, the teachers in this study do not systematically use reallife situation to apply the concepts they teach. They can identify concepts too abstract for their student; however, they seldom use innovative ways to bring the discussion to their students' level of readiness and

  3. 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

  4. How to Make a Synthetic Multicellular Computer

    PubMed Central

    Macia, Javier; Sole, Ricard

    2014-01-01

    Biological systems perform computations at multiple scales and they do so in a robust way. Engineering metaphors have often been used in order to provide a rationale for modeling cellular and molecular computing networks and as the basis for their synthetic design. However, a major constraint in this mapping between electronic and wet computational circuits is the wiring problem. Although wires are identical within electronic devices, they must be different when using synthetic biology designs. Moreover, in most cases the designed molecular systems cannot be reused for other functions. A new approximation allows us to simplify the problem by using synthetic cellular consortia where the output of the computation is distributed over multiple engineered cells. By evolving circuits in silico, we can obtain the minimal sets of Boolean units required to solve the given problem at the lowest cost using cellular consortia. Our analysis reveals that the basic set of logic units is typically non-standard. Among the most common units, the so called inverted IMPLIES (N-Implies) appears to be one of the most important elements along with the NOT and AND functions. Although NOR and NAND gates are widely used in electronics, evolved circuits based on combinations of these gates are rare, thus suggesting that the strategy of combining the same basic logic gates might be inappropriate in order to easily implement synthetic computational constructs. The implications for future synthetic designs, the general view of synthetic biology as a standard engineering domain, as well as potencial drawbacks are outlined. PMID:24586222

  5. 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

  6. Biological growth functions describe published site index curves for Lake States timber species.

    Treesearch

    Allen L. Lundgren; William A. Dolid

    1970-01-01

    Two biological growth functions, an exponential-monomolecular function and a simple monomolecular function, have been fit to published site index curves for 11 Lake States tree species: red, jack, and white pine, balsam fir, white and black spruce, tamarack, white-cedar, aspen, red oak, and paper birch. Both functions closely fit all published curves except those for...

  7. Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology.

    PubMed

    Burrowes, Kelly S; Iravani, Amin; Kang, Wendy

    2018-01-12

    The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. End-to-end automated microfluidic platform for synthetic biology: from design to functional analysis

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

    Linshiz, Gregory; Jensen, Erik; Stawski, Nina

    Synthetic biology aims to engineer biological systems for desired behaviors. The construction of these systems can be complex, often requiring genetic reprogramming, extensive de novo DNA synthesis, and functional screening. Here, we present a programmable, multipurpose microfluidic platform and associated software and apply the platform to major steps of the synthetic biology research cycle: design, construction, testing, and analysis. We show the platform’s capabilities for multiple automated DNA assembly methods, including a new method for Isothermal Hierarchical DNA Construction, and for Escherichia coli and Saccharomyces cerevisiae transformation. The platform enables the automated control of cellular growth, gene expression induction, andmore » proteogenic and metabolic output analysis. Finally, taken together, we demonstrate the microfluidic platform’s potential to provide end-to-end solutions for synthetic biology research, from design to functional analysis.« less

  9. End-to-end automated microfluidic platform for synthetic biology: from design to functional analysis

    DOE PAGES

    Linshiz, Gregory; Jensen, Erik; Stawski, Nina; ...

    2016-02-02

    Synthetic biology aims to engineer biological systems for desired behaviors. The construction of these systems can be complex, often requiring genetic reprogramming, extensive de novo DNA synthesis, and functional screening. Here, we present a programmable, multipurpose microfluidic platform and associated software and apply the platform to major steps of the synthetic biology research cycle: design, construction, testing, and analysis. We show the platform’s capabilities for multiple automated DNA assembly methods, including a new method for Isothermal Hierarchical DNA Construction, and for Escherichia coli and Saccharomyces cerevisiae transformation. The platform enables the automated control of cellular growth, gene expression induction, andmore » proteogenic and metabolic output analysis. Finally, taken together, we demonstrate the microfluidic platform’s potential to provide end-to-end solutions for synthetic biology research, from design to functional analysis.« less

  10. Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions.

    PubMed

    Xie, Hongbo; Vucetic, Slobodan; Iakoucheva, Lilia M; Oldfield, Christopher J; Dunker, A Keith; Uversky, Vladimir N; Obradovic, Zoran

    2007-05-01

    Identifying relationships between function, amino acid sequence, and protein structure represents a major challenge. In this study, we propose a bioinformatics approach that identifies functional keywords in the Swiss-Prot database that correlate with intrinsic disorder. A statistical evaluation is employed to rank the significance of these correlations. Protein sequence data redundancy and the relationship between protein length and protein structure were taken into consideration to ensure the quality of the statistical inferences. Over 200,000 proteins from the Swiss-Prot database were analyzed using this approach. The predictions of intrinsic disorder were carried out using PONDR VL3E predictor of long disordered regions that achieves an accuracy of above 86%. Overall, out of the 710 Swiss-Prot functional keywords that were each associated with at least 20 proteins, 238 were found to be strongly positively correlated with predicted long intrinsically disordered regions, whereas 302 were strongly negatively correlated with such regions. The remaining 170 keywords were ambiguous without strong positive or negative correlation with the disorder predictions. These functions cover a large variety of biological activities and imply that disordered regions are characterized by a wide functional repertoire. Our results agree well with literature findings, as we were able to find at least one illustrative example of functional disorder or order shown experimentally for the vast majority of keywords showing the strongest positive or negative correlation with intrinsic disorder. This work opens a series of three papers, which enriches the current view of protein structure-function relationships, especially with regards to functionalities of intrinsically disordered proteins, and provides researchers with a novel tool that could be used to improve the understanding of the relationships between protein structure and function. The first paper of the series describes our

  11. Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

    PubMed Central

    Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

    2017-01-01

    Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

  12. Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

    PubMed

    Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

    2017-05-01

    Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

  13. 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.

  14. [Recent advances of synthetic biology for production of functional ingredients in Chinese materia medica].

    PubMed

    Su, Xin-Yao; Xue, Jian-Ping; Wang, Cai-Xia

    2016-11-01

    The functional ingredients in Chinese materia medica are the main active substance for traditional Chinese medicine and most of them are secondary metabolites derivatives. Until now,the main method to obtain those functional ingredients is through direct extraction from the Chinese materia medica. However, the income is very low because of the high extraction costs and the decreased medicinal plants. Synthetic biology technology, as a new and microbial approach, can be able to carry out large-scale production of functional ingredients and greatly ease the shortage of traditional Chinese medicine ingredients. This review mainly focused on the recent advances in synthetic biology for the functional ingredients production. Copyright© by the Chinese Pharmaceutical Association.

  15. The Layer-Oriented Approach to Declarative Languages for Biological Modeling

    PubMed Central

    Raikov, Ivan; De Schutter, Erik

    2012-01-01

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

  16. The layer-oriented approach to declarative languages for biological modeling.

    PubMed

    Raikov, Ivan; De Schutter, Erik

    2012-01-01

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

  17. The structure of a gene co-expression network reveals biological functions underlying eQTLs.

    PubMed

    Villa-Vialaneix, Nathalie; Liaubet, Laurence; Laurent, Thibault; Cherel, Pierre; Gamot, Adrien; SanCristobal, Magali

    2013-01-01

    What are the commonalities between genes, whose expression level is partially controlled by eQTL, especially with regard to biological functions? Moreover, how are these genes related to a phenotype of interest? These issues are particularly difficult to address when the genome annotation is incomplete, as is the case for mammalian species. Moreover, the direct link between gene expression and a phenotype of interest may be weak, and thus difficult to handle. In this framework, the use of a co-expression network has proven useful: it is a robust approach for modeling a complex system of genetic regulations, and to infer knowledge for yet unknown genes. In this article, a case study was conducted with a mammalian species. It showed that the use of a co-expression network based on partial correlation, combined with a relevant clustering of nodes, leads to an enrichment of biological functions of around 83%. Moreover, the use of a spatial statistics approach allowed us to superimpose additional information related to a phenotype; this lead to highlighting specific genes or gene clusters that are related to the network structure and the phenotype. Three main results are worth noting: first, key genes were highlighted as a potential focus for forthcoming biological experiments; second, a set of biological functions, which support a list of genes under partial eQTL control, was set up by an overview of the global structure of the gene expression network; third, pH was found correlated with gene clusters, and then with related biological functions, as a result of a spatial analysis of the network topology.

  18. Functional annotation of the vlinc class of non-coding RNAs using systems biology approach

    PubMed Central

    Laurent, Georges St.; Vyatkin, Yuri; Antonets, Denis; Ri, Maxim; Qi, Yao; Saik, Olga; Shtokalo, Dmitry; de Hoon, Michiel J.L.; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Arner, Erik; Forrest, Alistair R.R.; Nicolas, Estelle; McCaffrey, Timothy A.; Carninci, Piero; Hayashizaki, Yoshihide; Wahlestedt, Claes; Kapranov, Philipp

    2016-01-01

    Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlincRNAs genes likely function in cis to activate nearby genes. This effect while most pronounced in closely spaced vlincRNA–gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlincRNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs. PMID:27001520

  19. 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.

  20. Mediating objects: scientific and public functions of models in nineteenth-century biology.

    PubMed

    Ludwig, David

    2013-01-01

    The aim of this article is to examine the scientific and public functions of two- and three-dimensional models in the context of three episodes from nineteenth-century biology. I argue that these models incorporate both data and theory by presenting theoretical assumptions in the light of concrete data or organizing data through theoretical assumptions. Despite their diverse roles in scientific practice, they all can be characterized as mediators between data and theory. Furthermore, I argue that these different mediating functions often reflect their different audiences that included specialized scientists, students, and the general public. In this sense, models in nineteenth-century biology can be understood as mediators between theory, data, and their diverse audiences.

  1. G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

    PubMed Central

    2016-01-01

    Abstract Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G‐LoSA. G‐LoSA aligns protein local structures in a sequence order independent way and provides a GA‐score, a chemical feature‐based and size‐independent structure similarity score. Our benchmark validation shows the robust performance of G‐LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure‐centric comparative biology studies. In particular, G‐LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G‐LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer‐aided drug design. We hope that G‐LoSA can be a useful computational method for exploring interesting biological problems through large‐scale comparison of protein local structures and facilitating drug discovery research and development. G‐LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. PMID:26813336

  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. Computational adaptive optics for broadband optical interferometric tomography of biological tissue.

    PubMed

    Adie, Steven G; Graf, Benedikt W; Ahmad, Adeel; Carney, P Scott; Boppart, Stephen A

    2012-05-08

    Aberrations in optical microscopy reduce image resolution and contrast, and can limit imaging depth when focusing into biological samples. Static correction of aberrations may be achieved through appropriate lens design, but this approach does not offer the flexibility of simultaneously correcting aberrations for all imaging depths, nor the adaptability to correct for sample-specific aberrations for high-quality tomographic optical imaging. Incorporation of adaptive optics (AO) methods have demonstrated considerable improvement in optical image contrast and resolution in noninterferometric microscopy techniques, as well as in optical coherence tomography. Here we present a method to correct aberrations in a tomogram rather than the beam of a broadband optical interferometry system. Based on Fourier optics principles, we correct aberrations of a virtual pupil using Zernike polynomials. When used in conjunction with the computed imaging method interferometric synthetic aperture microscopy, this computational AO enables object reconstruction (within the single scattering limit) with ideal focal-plane resolution at all depths. Tomographic reconstructions of tissue phantoms containing subresolution titanium-dioxide particles and of ex vivo rat lung tissue demonstrate aberration correction in datasets acquired with a highly astigmatic illumination beam. These results also demonstrate that imaging with an aberrated astigmatic beam provides the advantage of a more uniform depth-dependent signal compared to imaging with a standard gaussian beam. With further work, computational AO could enable the replacement of complicated and expensive optical hardware components with algorithms implemented on a standard desktop computer, making high-resolution 3D interferometric tomography accessible to a wider group of users and nonspecialists.

  4. Computational studies of Ras and PI3K

    NASA Technical Reports Server (NTRS)

    Ren, Lei; Cucinotta, Francis A.

    2004-01-01

    Until recently, experimental techniques in molecular cell biology have been the primary means to investigate biological risk upon space radiation. However, computational modeling provides an alternative theoretical approach, which utilizes various computational tools to simulate proteins, nucleotides, and their interactions. In this study, we are focused on using molecular mechanics (MM) and molecular dynamics (MD) to study the mechanism of protein-protein binding and to estimate the binding free energy between proteins. Ras is a key element in a variety of cell processes, and its activation of phosphoinositide 3-kinase (PI3K) is important for survival of transformed cells. Different computational approaches for this particular study are presented to calculate the solvation energies and binding free energies of H-Ras and PI3K. The goal of this study is to establish computational methods to investigate the roles of different proteins played in the cellular responses to space radiation, including modification of protein function through gene mutation, and to support the studies in molecular cell biology and theoretical kinetics models for our risk assessment project.

  5. Biological response on a titanium implant-grade surface functionalized with modular peptides☆

    PubMed Central

    Yazici, H.; Fong, H.; Wilson, B.; Oren, E.E.; Amos, F.A.; Zhang, H.; Evans, J.S.; Snead, M.L.; Sarikaya, M.; Tamerler, C.

    2015-01-01

    Titanium (Ti) and its alloys are among the most successful implantable materials for dental and orthopedic applications. The combination of excellent mechanical and corrosion resistance properties makes them highly desirable as endosseous implants that can withstand a demanding biomechanical environment. Yet, the success of the implant depends on its osteointegration, which is modulated by the biological reactions occurring at the interface of the implant. A recent development for improving biological responses on the Ti-implant surface has been the realization that bifunctional peptides can impart material binding specificity not only because of their molecular recognition of the inorganic material surface, but also through their self-assembly and ease of biological conjugation properties. To assess peptide-based functionalization on bioactivity, the present authors generated a set of peptides for implant-grade Ti, using cell surface display methods. Out of 60 unique peptides selected by this method, two of the strongest titanium binding peptides, TiBP1 and TiBP2, were further characterized for molecular structure and adsorption properties. These two peptides demonstrated unique, but similar molecular conformations different from that of a weak binder peptide, TiBP60. Adsorption measurements on a Ti surface revealed that their disassociation constants were 15-fold less than TiBP60. Their flexible and modular use in biological surface functionalization were demonstrated by conjugating them with an integrin recognizing peptide motif, RGDS. The functionalization of the Ti surface by the selected peptides significantly enhanced the bioactivity of osteoblast and fibroblast cells on implant-grade materials. PMID:23159566

  6. Molecular Characteristics and Biological Functions of Surface-Active and Surfactant Proteins.

    PubMed

    Sunde, Margaret; Pham, Chi L L; Kwan, Ann H

    2017-06-20

    Many critical biological processes take place at hydrophobic:hydrophilic interfaces, and a wide range of organisms produce surface-active proteins and peptides that reduce surface and interfacial tension and mediate growth and development at these boundaries. Microorganisms produce both small lipid-associated peptides and amphipathic proteins that allow growth across water:air boundaries, attachment to surfaces, predation, and improved bioavailability of hydrophobic substrates. Higher-order organisms produce surface-active proteins with a wide variety of functions, including the provision of protective foam environments for vulnerable reproductive stages, evaporative cooling, and gas exchange across airway membranes. In general, the biological functions supported by these diverse polypeptides require them to have an amphipathic nature, and this is achieved by a diverse range of molecular structures, with some proteins undergoing significant conformational change or intermolecular association to generate the structures that are surface active.

  7. Computational challenges of structure-based approaches applied to HIV.

    PubMed

    Forli, Stefano; Olson, Arthur J

    2015-01-01

    Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.

  8. COMPUTATIONAL TOXICOLOGY

    EPA Science Inventory

    Over the last several years, there has been increased pressure to utilize novel technologies derived from computational chemistry, molecular biology and systems biology in toxicological risk assessment. This new area has been referred to as "Computational Toxicology". Our resear...

  9. Variance computations for functional of absolute risk estimates.

    PubMed

    Pfeiffer, R M; Petracci, E

    2011-07-01

    We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.

  10. Variance computations for functional of absolute risk estimates

    PubMed Central

    Pfeiffer, R.M.; Petracci, E.

    2011-01-01

    We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates. PMID:21643476

  11. Workshop Report: Systems Biology for Organotypic Cell Cultures

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

    Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

  12. Workshop Report: Systems Biology for Organotypic Cell Cultures

    DOE PAGES

    Grego, Sonia; Dougherty, Edward R.; Alexander, Francis Joseph; ...

    2016-11-14

    Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomicmore » data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.« less

  13. Graduate Training at the Interface of Computational and Experimental Biology: An Outcome Report from a Partnership of Volunteers between a University and a National Laboratory.

    PubMed

    von Arnim, Albrecht G; Missra, Anamika

    2017-01-01

    Leading voices in the biological sciences have called for a transformation in graduate education leading to the PhD degree. One area commonly singled out for growth and innovation is cross-training in computational science. In 1998, the University of Tennessee (UT) founded an intercollegiate graduate program called the UT-ORNL Graduate School of Genome Science and Technology in partnership with the nearby Oak Ridge National Laboratory. Here, we report outcome data that attest to the program's effectiveness in graduating computationally enabled biologists for diverse careers. Among 77 PhD graduates since 2003, the majority came with traditional degrees in the biological sciences, yet two-thirds moved into computational or hybrid (computational-experimental) positions. We describe the curriculum of the program and how it has changed. We also summarize how the program seeks to establish cohesion between computational and experimental biologists. This type of program can respond flexibly and dynamically to unmet training needs. In conclusion, this study from a flagship, state-supported university may serve as a reference point for creating a stable, degree-granting, interdepartmental graduate program in computational biology and allied areas. © 2017 A. G. von Arnim and A. Missra. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  14. Functional annotation of the vlinc class of non-coding RNAs using systems biology approach.

    PubMed

    St Laurent, Georges; Vyatkin, Yuri; Antonets, Denis; Ri, Maxim; Qi, Yao; Saik, Olga; Shtokalo, Dmitry; de Hoon, Michiel J L; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Arner, Erik; Forrest, Alistair R R; Nicolas, Estelle; McCaffrey, Timothy A; Carninci, Piero; Hayashizaki, Yoshihide; Wahlestedt, Claes; Kapranov, Philipp

    2016-04-20

    Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlinc RNAs genes likely function in cisto activate nearby genes. This effect while most pronounced in closely spaced vlinc RNA-gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlinc RNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  15. 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.

  16. A Computational Framework for Bioimaging Simulation.

    PubMed

    Watabe, Masaki; Arjunan, Satya N V; Fukushima, Seiya; Iwamoto, Kazunari; Kozuka, Jun; Matsuoka, Satomi; Shindo, Yuki; Ueda, Masahiro; Takahashi, Koichi

    2015-01-01

    Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.

  17. A Computational Framework for Bioimaging Simulation

    PubMed Central

    Watabe, Masaki; Arjunan, Satya N. V.; Fukushima, Seiya; Iwamoto, Kazunari; Kozuka, Jun; Matsuoka, Satomi; Shindo, Yuki; Ueda, Masahiro; Takahashi, Koichi

    2015-01-01

    Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units. PMID:26147508

  18. Algebraic Functions, Computer Programming, and the Challenge of Transfer

    ERIC Educational Resources Information Center

    Schanzer, Emmanuel Tanenbaum

    2015-01-01

    Students' struggles with algebra are well documented. Prior to the introduction of functions, mathematics is typically focused on applying a set of arithmetic operations to compute an answer. The introduction of functions, however, marks the point at which mathematics begins to focus on building up abstractions as a way to solve complex problems.…

  19. Biological interpretation of genome-wide association studies using predicted gene functions.

    PubMed

    Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude

    2015-01-19

    The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

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

    PubMed Central

    Thompson, Paul; Nawaz, Raheel; McNaught, John; Kell, Douglas B.

    2015-01-01

    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. PMID:24907365

  1. 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...

  2. Systems biology and mechanics of growth.

    PubMed

    Eskandari, Mona; Kuhl, Ellen

    2015-01-01

    In contrast to inert systems, living biological systems have the advantage to adapt to their environment through growth and evolution. This transfiguration is evident during embryonic development, when the predisposed need to grow allows form to follow function. Alterations in the equilibrium state of biological systems breed disease and mutation in response to environmental triggers. The need to characterize the growth of biological systems to better understand these phenomena has motivated the continuum theory of growth and stimulated the development of computational tools in systems biology. Biological growth in development and disease is increasingly studied using the framework of morphoelasticity. Here, we demonstrate the potential for morphoelastic simulations through examples of volume, area, and length growth, inspired by tumor expansion, chronic bronchitis, brain development, intestine formation, plant shape, and myopia. We review the systems biology of living systems in light of biochemical and optical stimuli and classify different types of growth to facilitate the design of growth models for various biological systems within this generic framework. Exploring the systems biology of growth introduces a new venue to control and manipulate embryonic development, disease progression, and clinical intervention. © 2015 Wiley Periodicals, Inc.

  3. 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

  4. 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.

  5. Mammalian synthetic biology for studying the cell

    PubMed Central

    Mathur, Melina; Xiang, Joy S.

    2017-01-01

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

  6. Mammalian synthetic biology for studying the cell.

    PubMed

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

    2017-01-02

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

  7. Functional Anthology of Intrinsic Disorder. I. Biological Processes and Functions of Proteins with Long Disordered Regions

    PubMed Central

    Xie, Hongbo; Vucetic, Slobodan; Iakoucheva, Lilia M.; Oldfield, Christopher J.; Dunker, A. Keith; Uversky, Vladimir N.; Obradovic, Zoran

    2008-01-01

    Identifying relationships between function, amino acid sequence and protein structure represents a major challenge. In this study we propose a bioinformatics approach that identifies functional keywords in the Swiss-Prot database that correlate with intrinsic disorder. A statistical evaluation is employed to rank the significance of these correlations. Protein sequence data redundancy and the relationship between protein length and protein structure were taken into consideration to ensure the quality of the statistical inferences. Over 200,000 proteins from Swiss-Prot database were analyzed using this approach. The predictions of intrinsic disorder were carried out using PONDR VL3E predictor of long disordered regions that achieves an accuracy of above 86%. Overall, out of the 710 Swiss-Prot functional keywords that were each associated with at least 20 proteins, 238 were found to be strongly positively correlated with predicted long intrinsically disordered regions, whereas 302 were strongly negatively correlated with such regions. The remaining 170 keywords were ambiguous without strong positive or negative correlation with the disorder predictions. These functions cover a large variety of biological activities and imply that disordered regions are characterized by a wide functional repertoire. Our results agree well with literature findings, as we were able to find at least one illustrative example of functional disorder or order shown experimentally for the vast majority of keywords showing the strongest positive or negative correlation with intrinsic disorder. This work opens a series of three papers, which enriches the current view of protein structure-function relationships, especially with regards to functionalities of intrinsically disordered proteins and provides researchers with a novel tool that could be used to improve the understanding of the relationships between protein structure and function. The first paper of the series describes our statistical

  8. Computing Legacy Software Behavior to Understand Functionality and Security Properties: An IBM/370 Demonstration

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

    Linger, Richard C; Pleszkoch, Mark G; Prowell, Stacy J

    Organizations maintaining mainframe legacy software can benefit from code modernization and incorporation of security capabilities to address the current threat environment. Oak Ridge National Laboratory is developing the Hyperion system to compute the behavior of software as a means to gain understanding of software functionality and security properties. Computation of functionality is critical to revealing security attributes, which are in fact specialized functional behaviors of software. Oak Ridge is collaborating with MITRE Corporation to conduct a demonstration project to compute behavior of legacy IBM Assembly Language code for a federal agency. The ultimate goal is to understand functionality and securitymore » vulnerabilities as a basis for code modernization. This paper reports on the first phase, to define functional semantics for IBM Assembly instructions and conduct behavior computation experiments.« less

  9. Chaste: An Open Source C++ Library for Computational Physiology and Biology

    PubMed Central

    Mirams, Gary R.; Arthurs, Christopher J.; Bernabeu, Miguel O.; Bordas, Rafel; Cooper, Jonathan; Corrias, Alberto; Davit, Yohan; Dunn, Sara-Jane; Fletcher, Alexander G.; Harvey, Daniel G.; Marsh, Megan E.; Osborne, James M.; Pathmanathan, Pras; Pitt-Francis, Joe; Southern, James; Zemzemi, Nejib; Gavaghan, David J.

    2013-01-01

    Chaste — Cancer, Heart And Soft Tissue Environment — is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to ‘re-invent the wheel’ with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials. PMID:23516352

  10. [The biological reaction of inflammation, methylglyoxal of blood plasma, functional and structural alterations in elastic type arteries at the early stage of hypertension disease].

    PubMed

    Titov, V N; Dmitriev, V A; Oshchepkov, E V; Balakhonova, T V; Tripoten', M I; Shiriaeva, Iu K

    2012-08-01

    The article deals with studying of the relationship between biologic reaction of inflammation with glycosylation reaction and content of methylglyoxal in blood serum. The positive correlation between pulse wave velocity and content of methylglyoxal, C-reactive protein in intercellular medium and malleolar brachial index value was established. This data matches the experimental results concerning involvement of biological reaction of inflammation into structural changes of elastic type arteries under hypertension disease, formation of arteries' rigidity and increase of pulse wave velocity. The arterial blood pressure is a biological reaction of hydrodynamic pressure which is used in vivo by several biological functions: biological function of homeostasis, function of endoecology, biological function of adaptation and function of locomotion. The biological reaction of hydrodynamic (hydraulic) pressure is a mode of compensation of derangement of several biological functions which results in the very high rate of hypertension disease in population. As a matter of fact, hypertension disease is a syndrome of lingering pathological compensation by higher arterial blood pressure of the biological functions derangements occurring in the distal section at the level of paracrine cenoses of cells. The arterial blood pressure is a kind of in vivo integral indicator of deranged metabolism. The essential hypertension disease pathogenically is a result of the derangement of three biological functions: biological function of homeostasis, biological function of trophology - nutrition (biological reaction of external feeding - exotrophia) and biological function of endoecology. In case of "littering" of intercellular medium in vivo with nonspecific endogenic flogogens a phylogenetically earlier activation of biological reactions of excretion, inflammation and hydrodynamic arterial blood pressure occur. In case of derangement of biological function of homeostasis, decreasing of

  11. Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease.

    PubMed

    Torres, Matthew P; Dewhurst, Henry; Sundararaman, Niveda

    2016-11-01

    Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable

  12. Biological interpretation of genome-wide association studies using predicted gene functions

    PubMed Central

    Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S.N.; Hirschhorn, Joel N.; Franke, Lude

    2015-01-01

    The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. PMID:25597830

  13. Rule-based programming paradigm: a formal basis for biological, chemical and physical computation.

    PubMed

    Krishnamurthy, V; Krishnamurthy, E V

    1999-03-01

    A rule-based programming paradigm is described as a formal basis for biological, chemical and physical computations. In this paradigm, the computations are interpreted as the outcome arising out of interaction of elements in an object space. The interactions can create new elements (or same elements with modified attributes) or annihilate old elements according to specific rules. Since the interaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the elements evolve toward an equilibrium or unstable or chaotic state. Such an evolution may retain certain invariant properties of the attributes of the elements. The object space resembles Gibbsian ensemble that corresponds to a distribution of points in the space of positions and momenta (called phase space). It permits the introduction of probabilities in rule applications. As each element of the ensemble changes over time, its phase point is carried into a new phase point. The evolution of this probability cloud in phase space corresponds to a distributed probabilistic computation. Thus, this paradigm can handle tor deterministic exact computation when the initial conditions are exactly specified and the trajectory of evolution is deterministic. Also, it can handle probabilistic mode of computation if we want to derive macroscopic or bulk properties of matter. We also explain how to support this rule-based paradigm using relational-database like query processing and transactions.

  14. Neural-like computing with populations of superparamagnetic basis functions.

    PubMed

    Mizrahi, Alice; Hirtzlin, Tifenn; Fukushima, Akio; Kubota, Hitoshi; Yuasa, Shinji; Grollier, Julie; Querlioz, Damien

    2018-04-18

    In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. Here, we show that nanoscale magnetic tunnel junctions can instead be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.

  15. Hidden Markov models and other machine learning approaches in computational molecular biology

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

    Baldi, P.

    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. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less

  16. The functional biology of human milk oligosaccharides.

    PubMed

    Bode, Lars

    2015-11-01

    Human milk oligosaccharides (HMOs) are a group of complex sugars that are highly abundant in human milk, but currently not present in infant formula. More than a hundred different HMOs have been identified so far. The amount and composition of HMOs are highly variable between women, and each structurally defined HMO might have a distinct functionality. HMOs are not digested by the infant and serve as metabolic substrates for select microbes, contributing to shape the infant gut microbiome. HMOs act as soluble decoy receptors that block the attachment of viral, bacterial or protozoan parasite pathogens to epithelial cell surface sugars, which may help prevent infectious diseases in the gut and also the respiratory and urinary tracts. HMOs are also antimicrobials that act as bacteriostatic or bacteriocidal agents. In addition, HMOs alter host epithelial and immune cell responses with potential benefits for the neonate. The article reviews current knowledge as well as future challenges and opportunities related to the functional biology of HMOs. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  17. Evaluating Computational Gene Ontology Annotations.

    PubMed

    Škunca, Nives; Roberts, Richard J; Steffen, Martin

    2017-01-01

    Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

  18. The synergistic use of computation, chemistry and biology to discover novel peptide-based drugs: the time is right.

    PubMed

    Audie, J; Boyd, C

    2010-01-01

    The case for peptide-based drugs is compelling. Due to their chemical, physical and conformational diversity, and relatively unproblematic toxicity and immunogenicity, peptides represent excellent starting material for drug discovery. Nature has solved many physiological and pharmacological problems through the use of peptides, polypeptides and proteins. If nature could solve such a diversity of challenging biological problems through the use of peptides, it seems reasonable to infer that human ingenuity will prove even more successful. And this, indeed, appears to be the case, as a number of scientific and methodological advances are making peptides and peptide-based compounds ever more promising pharmacological agents. Chief among these advances are powerful chemical and biological screening technologies for lead identification and optimization, methods for enhancing peptide in vivo stability, bioavailability and cell-permeability, and new delivery technologies. Other advances include the development and experimental validation of robust computational methods for peptide lead identification and optimization. Finally, scientific analysis, biology and chemistry indicate the prospect of designing relatively small peptides to therapeutically modulate so-called 'undruggable' protein-protein interactions. Taken together a clear picture is emerging: through the synergistic use of the scientific imagination and the computational, chemical and biological methods that are currently available, effective peptide therapeutics for novel targets can be designed that surpass even the proven peptidic designs of nature.

  19. Dynamics of biomolecules, ligand binding & biological functions

    NASA Astrophysics Data System (ADS)

    Yi, Myunggi

    Proteins are flexible and dynamic. One static structure alone does not often completely explain biological functions of the protein, and some proteins do not even have high resolution structures. In order to provide better understanding to the biological functions of nicotinic acetylcholine receptor, Diphtheria toxin repressor and M2 proton channel, the dynamics of these proteins are investigated using molecular modeling and molecular dynamics (MD) simulations. With absence of high resolution structure of alpha7 receptor, the homology models of apo and cobra toxin bound forms have been built. From the MD simulations of these model structures, we observed one subunit of apo simulation moved away from other four subunits. With local movement of flexible loop regions, the whole subunit tilted clockwise. These conformational changes occurred spontaneously, and were strongly correlated with the conformational change when the channel is activated by agonists. Unlike other computational studies, we directly compared our model of open conformation with the experimental data. However, the subunits of toxin bound form were stable, and conformational change is restricted by the bound cobra toxin. These results provide activation and inhibition mechanisms of alpha7 receptors and a possible explanation for intermediate conductance of the channel. Intramolecular complex of SH3-like domain with a proline-rich (Pr) peptide segment in Diphtheria toxin repressor (DtxR) is stabilized in inactive state. Upon activation of DtxR by transition metal binding, this intramolecular complex should be dissociated. The dynamics of this intramolecular complex is investigated using MD simulations and NMR spectroscopy. We observed spontaneous opening and closing motions of the Pr segment binding pockets in both Pr-SH3 and SH3 simulations. The MD simulation results and NMR relaxation data suggest that the Pr segment exhibits a binding ↔ unbinding equilibrium. Despite a wealth of experimental

  20. Spaceborne computer executive routine functional design specification. Volume 1: Functional design of a flight computer executive program for the reusable shuttle

    NASA Technical Reports Server (NTRS)

    Curran, R. T.

    1971-01-01

    A flight computer functional executive design for the reusable shuttle is presented. The design is given in the form of functional flowcharts and prose description. Techniques utilized in the regulation of process flow to accomplish activation, resource allocation, suspension, termination, and error masking based on process primitives are considered. Preliminary estimates of main storage utilization by the Executive are furnished. Conclusions and recommendations for timely, effective software-hardware integration in the reusable shuttle avionics system are proposed.

  1. Probing the Xenopus laevis inner ear transcriptome for biological function

    PubMed Central

    2012-01-01

    Background The senses of hearing and balance depend upon mechanoreception, a process that originates in the inner ear and shares features across species. Amphibians have been widely used for physiological studies of mechanotransduction by sensory hair cells. In contrast, much less is known of the genetic basis of auditory and vestibular function in this class of animals. Among amphibians, the genus Xenopus is a well-characterized genetic and developmental model that offers unique opportunities for inner ear research because of the amphibian capacity for tissue and organ regeneration. For these reasons, we implemented a functional genomics approach as a means to undertake a large-scale analysis of the Xenopus laevis inner ear transcriptome through microarray analysis. Results Microarray analysis uncovered genes within the X. laevis inner ear transcriptome associated with inner ear function and impairment in other organisms, thereby supporting the inclusion of Xenopus in cross-species genetic studies of the inner ear. The use of gene categories (inner ear tissue; deafness; ion channels; ion transporters; transcription factors) facilitated the assignment of functional significance to probe set identifiers. We enhanced the biological relevance of our microarray data by using a variety of curation approaches to increase the annotation of the Affymetrix GeneChip® Xenopus laevis Genome array. In addition, annotation analysis revealed the prevalence of inner ear transcripts represented by probe set identifiers that lack functional characterization. Conclusions We identified an abundance of targets for genetic analysis of auditory and vestibular function. The orthologues to human genes with known inner ear function and the highly expressed transcripts that lack annotation are particularly interesting candidates for future analyses. We used informatics approaches to impart biologically relevant information to the Xenopus inner ear transcriptome, thereby addressing the

  2. Analysis and selection of optimal function implementations in massively parallel computer

    DOEpatents

    Archer, Charles Jens [Rochester, MN; Peters, Amanda [Rochester, MN; Ratterman, Joseph D [Rochester, MN

    2011-05-31

    An apparatus, program product and method optimize the operation of a parallel computer system by, in part, collecting performance data for a set of implementations of a function capable of being executed on the parallel computer system based upon the execution of the set of implementations under varying input parameters in a plurality of input dimensions. The collected performance data may be used to generate selection program code that is configured to call selected implementations of the function in response to a call to the function under varying input parameters. The collected performance data may be used to perform more detailed analysis to ascertain the comparative performance of the set of implementations of the function under the varying input parameters.

  3. Fundamental and functional aspects of mesoscopic architectures with examples in physics, cell biology, and chemistry.

    PubMed

    Kalay, Ziya

    2011-08-01

    How small can a macroscopic object be made without losing its intended function? Obviously, the smallest possible size is determined by the size of an atom, but it is not so obvious how many atoms are required to assemble an object so small, and yet that performs the same function as its macroscopic counterpart. In this review, we are concerned with objects of intermediate nature, lying between the microscopic and the macroscopic world. In physics and chemistry literature, this regime in-between is often called mesoscopic, and is known to bear interesting and counterintuitive features. After a brief introduction to the concept of mesoscopic systems from the perspective of physics, we discuss the functional aspects of mesoscopic architectures in cell biology, and supramolecular chemistry through many examples from the literature. We argue that the biochemistry of the cell is largely regulated by mesoscopic functional architectures; however, the significance of mesoscopic phenomena seems to be quite underappreciated in biological sciences. With this motivation, one of our main purposes here is to emphasize the critical role that mesoscopic structures play in cell biology and biochemistry.

  4. Report from the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast.

    PubMed

    Emmert-Streib, Frank; Zhang, Shu-Dong; Hamilton, Peter

    2014-12-01

    In this paper, we present a meeting report for the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast. We describe the organization of the summer school, its underlying concept and student feedback we received after the completion of the summer school.

  5. A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology.

    PubMed

    Chang, Tzu-Hao; Wu, Shih-Lin; Wang, Wei-Jen; Horng, Jorng-Tzong; Chang, Cheng-Wei

    2014-01-01

    Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.

  6. Interacting domain-specific languages with biological problem solving environments

    NASA Astrophysics Data System (ADS)

    Cickovski, Trevor M.

    Iteratively developing a biological model and verifying results with lab observations has become standard practice in computational biology. This process is currently facilitated by biological Problem Solving Environments (PSEs), multi-tiered and modular software frameworks which traditionally consist of two layers: a computational layer written in a high level language using design patterns, and a user interface layer which hides its details. Although PSEs have proven effective, they still enforce some communication overhead between biologists refining their models through repeated comparison with experimental observations in vitro or in vivo, and programmers actually implementing model extensions and modifications within the computational layer. I illustrate the use of biological Domain-Specific Languages (DSLs) as a middle-level PSE tier to ameliorate this problem by providing experimentalists with the ability to iteratively test and develop their models using a higher degree of expressive power compared to a graphical interface, while saving the requirement of general purpose programming knowledge. I develop two radically different biological DSLs: XML-based BIOLOGO will model biological morphogenesis using a cell-centered stochastic cellular automaton and translate into C++ modules for an object-oriented PSE C OMPUCELL3D, and MDLab will provide a set of high-level Python libraries for running molecular dynamics simulations, using wrapped functionality from the C++ PSE PROTOMOL. I describe each language in detail, including its its roles within the larger PSE and its expressibility in terms of representable phenomena, and a discussion of observations from users of the languages. Moreover I will use these studies to draw general conclusions about biological DSL development, including dependencies upon the goals of the corresponding PSE, strategies, and tradeoffs.

  7. Effects of Physical Exercise on Cognitive Functioning and Wellbeing: Biological and Psychological Benefits

    PubMed Central

    Mandolesi, Laura; Polverino, Arianna; Montuori, Simone; Foti, Francesca; Ferraioli, Giampaolo; Sorrentino, Pierpaolo; Sorrentino, Giuseppe

    2018-01-01

    Much evidence shows that physical exercise (PE) is a strong gene modulator that induces structural and functional changes in the brain, determining enormous benefit on both cognitive functioning and wellbeing. PE is also a protective factor for neurodegeneration. However, it is unclear if such protection is granted through modifications to the biological mechanisms underlying neurodegeneration or through better compensation against attacks. This concise review addresses the biological and psychological positive effects of PE describing the results obtained on brain plasticity and epigenetic mechanisms in animal and human studies, in order to clarify how to maximize the positive effects of PE while avoiding negative consequences, as in the case of exercise addiction. PMID:29755380

  8. Structural and Chemical Biology of Terpenoid Cyclases

    PubMed Central

    2017-01-01

    The year 2017 marks the twentieth anniversary of terpenoid cyclase structural biology: a trio of terpenoid cyclase structures reported together in 1997 were the first to set the foundation for understanding the enzymes largely responsible for the exquisite chemodiversity of more than 80000 terpenoid natural products. Terpenoid cyclases catalyze the most complex chemical reactions in biology, in that more than half of the substrate carbon atoms undergo changes in bonding and hybridization during a single enzyme-catalyzed cyclization reaction. The past two decades have witnessed structural, functional, and computational studies illuminating the modes of substrate activation that initiate the cyclization cascade, the management and manipulation of high-energy carbocation intermediates that propagate the cyclization cascade, and the chemical strategies that terminate the cyclization cascade. The role of the terpenoid cyclase as a template for catalysis is paramount to its function, and protein engineering can be used to reprogram the cyclization cascade to generate alternative and commercially important products. Here, I review key advances in terpenoid cyclase structural and chemical biology, focusing mainly on terpenoid cyclases and related prenyltransferases for which X-ray crystal structures have informed and advanced our understanding of enzyme structure and function. PMID:28841019

  9. 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.

  10. SED-ED, a workflow editor for computational biology experiments written in SED-ML.

    PubMed

    Adams, Richard R

    2012-04-15

    The simulation experiment description markup language (SED-ML) is a new community data standard to encode computational biology experiments in a computer-readable XML format. Its widespread adoption will require the development of software support to work with SED-ML files. Here, we describe a software tool, SED-ED, to view, edit, validate and annotate SED-ML documents while shielding end-users from the underlying XML representation. SED-ED supports modellers who wish to create, understand and further develop a simulation description provided in SED-ML format. SED-ED is available as a standalone Java application, as an Eclipse plug-in and as an SBSI (www.sbsi.ed.ac.uk) plug-in, all under an MIT open-source license. Source code is at https://sed-ed-sedmleditor.googlecode.com/svn. The application itself is available from https://sourceforge.net/projects/jlibsedml/files/SED-ED/.

  11. Clinical, Functional, and Biological Correlates of Cognitive Dimensions in Major Depressive Disorder - Rationale, Design, and Characteristics of the Cognitive Function and Mood Study (CoFaM-Study).

    PubMed

    Baune, Bernhard T; Air, Tracy

    2016-01-01

    Cross-sectional and longitudinal studies exploring clinical, functional, and biological correlates of major depressive disorder are frequent. In this type of research, depression is most commonly defined as a categorical diagnosis based on studies using diagnostic instruments. Given the phenotypic and biological heterogeneity of depression, we chose to focus the phenotypic assessments on three cognitive dimensions of depression including (a) cognitive performance, (b) emotion processing, and (c) social cognitive functioning. Hence, the overall aim of the study is to investigate the long-term clinical course of these cognitive dimensions in depression and its functional (psychosocial) correlates. We also aim to identify biological "genomic" correlates of these three cognitive dimensions of depression. To address the above overall aim, we created the Cognition and Mood Study (CoFaMS) with the key objective to investigate the clinical, functional, and biological correlates of cognitive dimensions of depression by employing a prospective study design and including a healthy control group. The study commenced in April 2015, including patients with a primary diagnosis of a major depressive episode of major depressive disorder or bipolar disorder according to DSM-IV-TR criteria. The assessments cover the three cognitive dimensions of depression (cognitive performance, emotion processing, and social cognition), cognitive function screening instrument, plus functional scales to assess general, work place, and psychosocial function, depression symptom scales, and clinical course of illness. Blood is collected for comprehensive genomic discovery analyses of biological correlates of cognitive dimensions of depression. The CoFaM-Study represents an innovative approach focusing on cognitive dimensions of depression and its functional and biological "genomic" correlates. The CoFaMS team welcomes collaborations with both national and international researchers.

  12. Algebraic properties of automata associated to Petri nets and applications to computation in biological systems.

    PubMed

    Egri-Nagy, Attila; Nehaniv, Chrystopher L

    2008-01-01

    Biochemical and genetic regulatory networks are often modeled by Petri nets. We study the algebraic structure of the computations carried out by Petri nets from the viewpoint of algebraic automata theory. Petri nets comprise a formalized graphical modeling language, often used to describe computation occurring within biochemical and genetic regulatory networks, but the semantics may be interpreted in different ways in the realm of automata. Therefore, there are several different ways to turn a Petri net into a state-transition automaton. Here, we systematically investigate different conversion methods and describe cases where they may yield radically different algebraic structures. We focus on the existence of group components of the corresponding transformation semigroups, as these reflect symmetries of the computation occurring within the biological system under study. Results are illustrated by applications to the Petri net modelling of intermediary metabolism. Petri nets with inhibition are shown to be computationally rich, regardless of the particular interpretation method. Along these lines we provide a mathematical argument suggesting a reason for the apparent all-pervasiveness of inhibitory connections in living systems.

  13. Congruence Between Pulmonary Function and Computed Tomography Imaging Assessment of Cystic Fibrosis Severity.

    PubMed

    Rybacka, Anna; Goździk-Spychalska, Joanna; Rybacki, Adam; Piorunek, Tomasz; Batura-Gabryel, Halina; Karmelita-Katulska, Katarzyna

    2018-05-04

    In cystic fibrosis, pulmonary function tests (PFTs) and computed tomography are used to assess lung function and structure, respectively. Although both techniques of assessment are congruent there are lingering doubts about which PFTs variables show the best congruence with computed tomography scoring. In this study we addressed the issue by reinvestigating the association between PFTs variables and the score of changes seen in computed tomography scans in patients with cystic fibrosis with and without pulmonary exacerbation. This retrospective study comprised 40 patients in whom PFTs and computed tomography were performed no longer than 3 weeks apart. Images (inspiratory: 0.625 mm slice thickness, 0.625 mm interval; expiratory: 1.250 mm slice thickness, 10 mm interval) were evaluated with the Bhalla scoring system. The most frequent structural abnormality found in scans were bronchiectases and peribronchial thickening. The strongest relationship was found between the Bhalla sore and forced expiratory volume in 1 s (FEV1). The Bhalla sore also was related to forced vital capacity (FVC), FEV1/FVC ratio, residual volume (RV), and RV/total lung capacity (TLC) ratio. We conclude that lung structural data obtained from the computed tomography examination are highly congruent to lung function data. Thus, computed tomography imaging may supersede functional assessment in cases of poor compliance with spirometry procedures in the lederly or children. Computed tomography also seems more sensitive than PFTs in the assessment of cystic fibrosis progression. Moreover, in early phases of cystic fibrosis, computed tomography, due to its excellent resolution, may be irreplaceable in monitoring pulmonary damage.

  14. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials

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

    Winkler, David A., E-mail: dave.winkler@csiro.au

    2016-05-15

    Nanomaterials research is one of the fastest growing contemporary research areas. The unprecedented properties of these materials have meant that they are being incorporated into products very quickly. Regulatory agencies are concerned they cannot assess the potential hazards of these materials adequately, as data on the biological properties of nanomaterials are still relatively limited and expensive to acquire. Computational modelling methods have much to offer in helping understand the mechanisms by which toxicity may occur, and in predicting the likelihood of adverse biological impacts of materials not yet tested experimentally. This paper reviews the progress these methods, particularly those QSAR-based,more » have made in understanding and predicting potentially adverse biological effects of nanomaterials, and also the limitations and pitfalls of these methods. - Highlights: • Nanomaterials regulators need good information to make good decisions. • Nanomaterials and their interactions with biology are very complex. • Computational methods use existing data to predict properties of new nanomaterials. • Statistical, data driven modelling methods have been successfully applied to this task. • Much more must be learnt before robust toolkits will be widely usable by regulators.« less

  15. Schematic and realistic biological motion identification in children with high-functioning autism spectrum disorder

    PubMed Central

    Wright, Kristyn; Kelley, Elizabeth; Poulin-Dubois, Diane

    2014-01-01

    Research investigating biological motion perception in children with ASD has revealed conflicting findings concerning whether impairments in biological motion perception exist. The current study investigated how children with high-functioning ASD (HF-ASD) performed on two tasks of biological motion identification: a novel schematic motion identification task and a point-light biological motion identification task. Twenty-two HFASD children were matched with 21 TD children on gender, non-verbal mental, and chronological, age (M years = 6.72). On both tasks, HF-ASD children performed with similar accuracy as TD children. Across groups, children performed better on animate than on inanimate trials of both tasks. These findings suggest that HF-ASD children's identification of both realistic and schematic biological motion identification is unimpaired. PMID:25395988

  16. Surface functionalization of thin-film diamond for highly stable and selective biological interfaces

    PubMed Central

    Stavis, Courtney; Clare, Tami Lasseter; Butler, James E.; Radadia, Adarsh D.; Carr, Rogan; Zeng, Hongjun; King, William P.; Carlisle, John A.; Aksimentiev, Aleksei; Bashir, Rashid; Hamers, Robert J.

    2011-01-01

    Carbon is an extremely versatile family of materials with a wide range of mechanical, optical, and mechanical properties, but many similarities in surface chemistry. As one of the most chemically stable materials known, carbon provides an outstanding platform for the development of highly tunable molecular and biomolecular interfaces. Photochemical grafting of alkenes has emerged as an attractive method for functionalizing surfaces of diamond, but many aspects of the surface chemistry and impact on biological recognition processes remain unexplored. Here we report investigations of the interaction of functionalized diamond surfaces with proteins and biological cells using X-ray photoelectron spectroscopy (XPS), atomic force microscopy, and fluorescence methods. XPS data show that functionalization of diamond with short ethylene glycol oligomers reduces the nonspecific binding of fibrinogen below the detection limit of XPS, estimated as > 97% reduction over H-terminated diamond. Measurements of different forms of diamond with different roughness are used to explore the influence of roughness on nonspecific binding onto H-terminated and ethylene glycol (EG)-terminated surfaces. Finally, we use XPS to characterize the chemical stability of Escherichia coli K12 antibodies on the surfaces of diamond and amine-functionalized glass. Our results show that antibody-modified diamond surfaces exhibit increased stability in XPS and that this is accompanied by retention of biological activity in cell-capture measurements. Our results demonstrate that surface chemistry on diamond and other carbon-based materials provides an excellent platform for biomolecular interfaces with high stability and high selectivity. PMID:20884854

  17. Biological roles and functional mechanisms of arenavirus Z protein in viral replication.

    PubMed

    Wang, Jialong; Danzy, Shamika; Kumar, Naveen; Ly, Hinh; Liang, Yuying

    2012-09-01

    Arenaviruses can cause severe hemorrhagic fever diseases in humans, with limited prophylactic or therapeutic measures. A small RING-domain viral protein Z has been shown to mediate the formation of virus-like particles and to inhibit viral RNA synthesis, although its biological roles in an infectious viral life cycle have not been directly addressed. By taking advantage of the available reverse genetics system for a model arenavirus, Pichinde virus (PICV), we provide the direct evidence for the essential biological roles of the Z protein's conserved residues, including the G2 myristylation site, the conserved C and H residues of RING domain, and the poorly characterized C-terminal L79 and P80 residues. Dicodon substitutions within the late (L) domain (PSAPPYEP) of the PICV Z protein, although producing viable mutant viruses, have significantly reduced virus growth, a finding suggestive of an important role for the intact L domain in viral replication. Further structure-function analyses of both PICV and Lassa fever virus Z proteins suggest that arenavirus Z proteins have similar molecular mechanisms in mediating their multiple functions, with some interesting variations, such as the role of the G2 residue in blocking viral RNA synthesis. In summary, our studies have characterized the biological roles of the Z protein in an infectious arenavirus system and have shed important light on the distinct functions of its domains in virus budding and viral RNA regulation, the knowledge of which may lead to the development of novel antiviral drugs.

  18. A review on data mining and continuous optimization applications in computational biology and medicine.

    PubMed

    Weber, Gerhard-Wilhelm; Ozöğür-Akyüz, Süreyya; Kropat, Erik

    2009-06-01

    An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology. It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetics. Given the data from DNA microarray experiments and environmental measurements, we extract nonlinear ordinary differential equations which contain parameters that are to be determined. This is done by a generalized Chebychev approximation and generalized semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. In addition, we analyze the topological landscape of gene-environment networks in terms of structural stability. As a second strategy, we will review recent model selection and kernel learning methods for binary classification which can be used to classify microarray data for cancerous cells or for discrimination of other kind of diseases. This review is practically motivated and theoretically elaborated; it is devoted to a contribution to better health care, progress in medicine, a better education, and more healthy living conditions.

  19. Computer-Assisted Microscopy in Science Teaching and Research.

    ERIC Educational Resources Information Center

    Radice, Gary P.

    1997-01-01

    Describes a technological approach to teaching the relationships between biological form and function. Computer-assisted image analysis was integrated into a microanatomy course. Students spend less time memorizing and more time observing, measuring, and interpreting, building technical and analytical skills. Appendices list hardware and software…

  20. Computer/gaming station use in youth: Correlations among use, addiction and functional impairment

    PubMed Central

    Baer, Susan; Saran, Kelly; Green, David A

    2012-01-01

    OBJECTIVE: Computer/gaming station use is ubiquitous in the lives of youth today. Overuse is a concern, but it remains unclear whether problems arise from addictive patterns of use or simply excessive time spent on use. The goal of the present study was to evaluate computer/gaming station use in youth and to examine the relationship between amounts of use, addictive features of use and functional impairment. METHOD: A total of 110 subjects (11 to 17 years of age) from local schools participated. Time spent on television, video gaming and non-gaming recreational computer activities was measured. Addictive features of computer/gaming station use were ascertained, along with emotional/behavioural functioning. Multiple linear regressions were used to understand how youth functioning varied with time of use and addictive features of use. RESULTS: Mean (± SD) total screen time was 4.5±2.4 h/day. Addictive features of use were consistently correlated with functional impairment across multiple measures and informants, whereas time of use, after controlling for addiction, was not. CONCLUSIONS: Youth are spending many hours each day in front of screens. In the absence of addictive features of computer/gaming station use, time spent is not correlated with problems; however, youth with addictive features of use show evidence of poor emotional/ behavioural functioning. PMID:24082802

  1. A computer system for processing data from routine pulmonary function tests.

    PubMed Central

    Pack, A I; McCusker, R; Moran, F

    1977-01-01

    In larger pulmonary function laboratories there is a need for computerised techniques of data processing. A flexible computer system, which is used routinely, is described. The system processes data from a relatively large range of tests. Two types of output are produced--one for laboratory purposes, and one for return to the referring physician. The system adds an automatic interpretative report for each set of results. In developing the interpretative system it has been necessary to utilise a number of arbitrary definitions. The present terminology for reporting pulmonary function tests has limitations. The computer interpretation system affords the opportunity to take account of known interaction between measurements of function and different pathological states. Images PMID:329462

  2. A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2018-04-03

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

  4. Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease*

    PubMed Central

    Dewhurst, Henry; Sundararaman, Niveda

    2016-01-01

    Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable

  5. Computational Approaches to Phenotyping

    PubMed Central

    Lussier, Yves A.; Liu, Yang

    2007-01-01

    The recent completion of the Human Genome Project has made possible a high-throughput “systems approach” for accelerating the elucidation of molecular underpinnings of human diseases, and subsequent derivation of molecular-based strategies to more effectively prevent, diagnose, and treat these diseases. Although altered phenotypes are among the most reliable manifestations of altered gene functions, research using systematic analysis of phenotype relationships to study human biology is still in its infancy. This article focuses on the emerging field of high-throughput phenotyping (HTP) phenomics research, which aims to capitalize on novel high-throughput computation and informatics technology developments to derive genomewide molecular networks of genotype–phenotype associations, or “phenomic associations.” The HTP phenomics research field faces the challenge of technological research and development to generate novel tools in computation and informatics that will allow researchers to amass, access, integrate, organize, and manage phenotypic databases across species and enable genomewide analysis to associate phenotypic information with genomic data at different scales of biology. Key state-of-the-art technological advancements critical for HTP phenomics research are covered in this review. In particular, we highlight the power of computational approaches to conduct large-scale phenomics studies. PMID:17202287

  6. Computational modeling of neural plasticity for self-organization of neural networks.

    PubMed

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  7. Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

    PubMed Central

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

    2009-01-01

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

  8. Computation of turbulent boundary layers employing the defect wall-function method. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Brown, Douglas L.

    1994-01-01

    In order to decrease overall computational time requirements of spatially-marching parabolized Navier-Stokes finite-difference computer code when applied to turbulent fluid flow, a wall-function methodology, originally proposed by R. Barnwell, was implemented. This numerical effort increases computational speed and calculates reasonably accurate wall shear stress spatial distributions and boundary-layer profiles. Since the wall shear stress is analytically determined from the wall-function model, the computational grid near the wall is not required to spatially resolve the laminar-viscous sublayer. Consequently, a substantially increased computational integration step size is achieved resulting in a considerable decrease in net computational time. This wall-function technique is demonstrated for adiabatic flat plate test cases from Mach 2 to Mach 8. These test cases are analytically verified employing: (1) Eckert reference method solutions, (2) experimental turbulent boundary-layer data of Mabey, and (3) finite-difference computational code solutions with fully resolved laminar-viscous sublayers. Additionally, results have been obtained for two pressure-gradient cases: (1) an adiabatic expansion corner and (2) an adiabatic compression corner.

  9. ACToR-AGGREGATED COMPUTATIONAL TOXICOLOGY ...

    EPA Pesticide Factsheets

    One goal of the field of computational toxicology is to predict chemical toxicity by combining computer models with biological and toxicological data. predict chemical toxicity by combining computer models with biological and toxicological data

  10. Computational Study on Atomic Structures, Electronic Properties, and Chemical Reactions at Surfaces and Interfaces and in Biomaterials

    NASA Astrophysics Data System (ADS)

    Takano, Yu; Kobayashi, Nobuhiko; Morikawa, Yoshitada

    2018-06-01

    Through computer simulations using atomistic models, it is becoming possible to calculate the atomic structures of localized defects or dopants in semiconductors, chemically active sites in heterogeneous catalysts, nanoscale structures, and active sites in biological systems precisely. Furthermore, it is also possible to clarify physical and chemical properties possessed by these nanoscale structures such as electronic states, electronic and atomic transport properties, optical properties, and chemical reactivity. It is sometimes quite difficult to clarify these nanoscale structure-function relations experimentally and, therefore, accurate computational studies are indispensable in materials science. In this paper, we review recent studies on the relation between local structures and functions for inorganic, organic, and biological systems by using atomistic computer simulations.

  11. Lateral organization, bilayer asymmetry, and inter-leaflet coupling of biological membranes

    DOE PAGES

    Smith, Jeremy C.; Cheng, Xiaolin; Nickels, Jonathan D.

    2015-07-29

    Understanding of cell membrane organization has evolved significantly from the classic fluid mosaic model. It is now recognized that biological membranes are highly organized structures, with differences in lipid compositions between inner and outer leaflets and in lateral structures within the bilayer plane, known as lipid rafts. These organizing principles are important for protein localization and function as well as cellular signaling. However, the mechanisms and biophysical basis of lipid raft formation, structure, dynamics and function are not clearly understood. One key question, which we focus on in this review, is how lateral organization and leaflet compositional asymmetry are coupled.more » Detailed information elucidating this question has been sparse because of the small size and transient nature of rafts and the experimental challenges in constructing asymmetric bilayers. Resolving this mystery will require advances in both experimentation and modeling. We discuss here the preparation of model systems along with experimental and computational approaches that have been applied in efforts to address this key question in membrane biology. Furthermore, we seek to place recent and future advances in experimental and computational techniques in context, providing insight into in-plane and transverse organization of biological membranes.« less

  12. Analog Computation by DNA Strand Displacement Circuits.

    PubMed

    Song, Tianqi; Garg, Sudhanshu; Mokhtar, Reem; Bui, Hieu; Reif, John

    2016-08-19

    DNA circuits have been widely used to develop biological computing devices because of their high programmability and versatility. Here, we propose an architecture for the systematic construction of DNA circuits for analog computation based on DNA strand displacement. The elementary gates in our architecture include addition, subtraction, and multiplication gates. The input and output of these gates are analog, which means that they are directly represented by the concentrations of the input and output DNA strands, respectively, without requiring a threshold for converting to Boolean signals. We provide detailed domain designs and kinetic simulations of the gates to demonstrate their expected performance. On the basis of these gates, we describe how DNA circuits to compute polynomial functions of inputs can be built. Using Taylor Series and Newton Iteration methods, functions beyond the scope of polynomials can also be computed by DNA circuits built upon our architecture.

  13. Computational Modeling in Liver Surgery

    PubMed Central

    Christ, Bruno; Dahmen, Uta; Herrmann, Karl-Heinz; König, Matthias; Reichenbach, Jürgen R.; Ricken, Tim; Schleicher, Jana; Ole Schwen, Lars; Vlaic, Sebastian; Waschinsky, Navina

    2017-01-01

    The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery. PMID:29249974

  14. Constructing Smart Protocells with Built-In DNA Computational Core to Eliminate Exogenous Challenge.

    PubMed

    Lyu, Yifan; Wu, Cuichen; Heinke, Charles; Han, Da; Cai, Ren; Teng, I-Ting; Liu, Yuan; Liu, Hui; Zhang, Xiaobing; Liu, Qiaoling; Tan, Weihong

    2018-06-06

    A DNA reaction network is like a biological algorithm that can respond to "molecular input signals", such as biological molecules, while the artificial cell is like a microrobot whose function is powered by the encapsulated DNA reaction network. In this work, we describe the feasibility of using a DNA reaction network as the computational core of a protocell, which will perform an artificial immune response in a concise way to eliminate a mimicked pathogenic challenge. Such a DNA reaction network (RN)-powered protocell can realize the connection of logical computation and biological recognition due to the natural programmability and biological properties of DNA. Thus, the biological input molecules can be easily involved in the molecular computation and the computation process can be spatially isolated and protected by artificial bilayer membrane. We believe the strategy proposed in the current paper, i.e., using DNA RN to power artificial cells, will lay the groundwork for understanding the basic design principles of DNA algorithm-based nanodevices which will, in turn, inspire the construction of artificial cells, or protocells, that will find a place in future biomedical research.

  15. Quantifying electron transfer reactions in biological systems: what interactions play the major role?

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  16. Graduate Training at the Interface of Computational and Experimental Biology: An Outcome Report from a Partnership of Volunteers between a University and a National Laboratory

    PubMed Central

    von Arnim, Albrecht G.; Missra, Anamika

    2017-01-01

    Leading voices in the biological sciences have called for a transformation in graduate education leading to the PhD degree. One area commonly singled out for growth and innovation is cross-training in computational science. In 1998, the University of Tennessee (UT) founded an intercollegiate graduate program called the UT-ORNL Graduate School of Genome Science and Technology in partnership with the nearby Oak Ridge National Laboratory. Here, we report outcome data that attest to the program’s effectiveness in graduating computationally enabled biologists for diverse careers. Among 77 PhD graduates since 2003, the majority came with traditional degrees in the biological sciences, yet two-thirds moved into computational or hybrid (computational–experimental) positions. We describe the curriculum of the program and how it has changed. We also summarize how the program seeks to establish cohesion between computational and experimental biologists. This type of program can respond flexibly and dynamically to unmet training needs. In conclusion, this study from a flagship, state-supported university may serve as a reference point for creating a stable, degree-granting, interdepartmental graduate program in computational biology and allied areas. PMID:29167223

  17. Community-driven development for computational biology at Sprints, Hackathons and Codefests.

    PubMed

    Möller, Steffen; Afgan, Enis; Banck, Michael; Bonnal, Raoul J P; Booth, Timothy; Chilton, John; Cock, Peter J A; Gumbel, Markus; Harris, Nomi; Holland, Richard; Kalaš, Matúš; Kaján, László; Kibukawa, Eri; Powel, David R; Prins, Pjotr; Quinn, Jacqueline; Sallou, Olivier; Strozzi, Francesco; Seemann, Torsten; Sloggett, Clare; Soiland-Reyes, Stian; Spooner, William; Steinbiss, Sascha; Tille, Andreas; Travis, Anthony J; Guimera, Roman; Katayama, Toshiaki; Chapman, Brad A

    2014-01-01

    Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together. This report summarises our experience with computational biology Codefests, Hackathons and Sprints, which are interactive developer meetings. They are structured to reduce the limitations of traditional scientific meetings described above by strengthening the interaction among peers and letting the participants determine the schedule and topics. These meetings are commonly run as loosely scheduled "unconferences" (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and organize contributors, followed by intensive collaborative coding sessions. We summarise some prominent achievements of those meetings and describe differences in how

  18. Effects of computer-based training on procedural modifications to standard functional analyses.

    PubMed

    Schnell, Lauren K; Sidener, Tina M; DeBar, Ruth M; Vladescu, Jason C; Kahng, SungWoo

    2018-01-01

    Few studies have evaluated methods for training decision-making when functional analysis data are undifferentiated. The current study evaluated computer-based training to teach 20 graduate students to arrange functional analysis conditions, analyze functional analysis data, and implement procedural modifications. Participants were exposed to training materials using interactive software during a 1-day session. Following the training, mean scores on the posttest, novel cases probe, and maintenance probe increased for all participants. These results replicate previous findings during a 1-day session and include a measure of participant acceptability of the training. Recommendations for future research on computer-based training and functional analysis are discussed. © 2017 Society for the Experimental Analysis of Behavior.

  19. Distributed computation: the new wave of synthetic biology devices.

    PubMed

    Macía, Javier; Posas, Francesc; Solé, Ricard V

    2012-06-01

    Synthetic biology (SB) offers a unique opportunity for designing complex molecular circuits able to perform predefined functions. But the goal of achieving a flexible toolbox of reusable molecular components has been shown to be limited due to circuit unpredictability, incompatible parts or random fluctuations. Many of these problems arise from the challenges posed by engineering the molecular circuitry: multiple wires are usually difficult to implement reliably within one cell and the resulting systems cannot be reused in other modules. These problems are solved by means of a nonstandard approach to single cell devices, using cell consortia and allowing the output signal to be distributed among different cell types, which can be combined in multiple, reusable and scalable ways. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Reversing DNA Methylation: Mechanisms, Genomics, and Biological Functions

    PubMed Central

    Wu, Hao; Zhang, Yi

    2014-01-01

    Methylation of cytosines in the mammalian genome represents a key epigenetic modification and is dynamically regulated during development. Compelling evidence now suggests that dynamic regulation of DNA methylation is mainly achieved through a cyclic enzymatic cascade comprised of cytosine methylation, iterative oxidation of methyl group by TET dioxygenases, and restoration of unmodified cytosines by either replication-dependent dilution or DNA glycosylase-initiated base excision repair. In this review, we discuss the mechanism and function of DNA demethylation in mammalian genomes, focusing particularly on how developmental modulation of the cytosine-modifying pathway is coupled to active reversal of DNA methylation in diverse biological processes. PMID:24439369