A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design

PubMed Central

Alford, Rebecca F.; Dolan, Erin L.

2017-01-01

Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology. PMID:29216185

A cyber-linked undergraduate research experience in computational biomolecular structure prediction and design.

PubMed

Alford, Rebecca F; Leaver-Fay, Andrew; Gonzales, Lynda; Dolan, Erin L; Gray, Jeffrey J

2017-12-01

Computational biology is an interdisciplinary field, and many computational biology research projects involve distributed teams of scientists. To accomplish their work, these teams must overcome both disciplinary and geographic barriers. Introducing new training paradigms is one way to facilitate research progress in computational biology. Here, we describe a new undergraduate program in biomolecular structure prediction and design in which students conduct research at labs located at geographically-distributed institutions while remaining connected through an online community. This 10-week summer program begins with one week of training on computational biology methods development, transitions to eight weeks of research, and culminates in one week at the Rosetta annual conference. To date, two cohorts of students have participated, tackling research topics including vaccine design, enzyme design, protein-based materials, glycoprotein modeling, crowd-sourced science, RNA processing, hydrogen bond networks, and amyloid formation. Students in the program report outcomes comparable to students who participate in similar in-person programs. These outcomes include the development of a sense of community and increases in their scientific self-efficacy, scientific identity, and science values, all predictors of continuing in a science research career. Furthermore, the program attracted students from diverse backgrounds, which demonstrates the potential of this approach to broaden the participation of young scientists from backgrounds traditionally underrepresented in computational biology.

Challenges in structural approaches to cell modeling

PubMed Central

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

2016-01-01

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

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

PubMed Central

2010-01-01

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

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

PubMed

D'Onofrio, David J; An, Gary

2010-01-21

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

Impact of computational structure-based methods on drug discovery.

PubMed

Reynolds, Charles H

2014-01-01

Structure-based drug design has become an indispensible tool in drug discovery. The emergence of structure-based design is due to gains in structural biology that have provided exponential growth in the number of protein crystal structures, new computational algorithms and approaches for modeling protein-ligand interactions, and the tremendous growth of raw computer power in the last 30 years. Computer modeling and simulation have made major contributions to the discovery of many groundbreaking drugs in recent years. Examples are presented that highlight the evolution of computational structure-based design methodology, and the impact of that methodology on drug discovery.

Teaching Bioinformatics in Concert

PubMed Central

Goodman, Anya L.; Dekhtyar, Alex

2014-01-01

Can biology students without programming skills solve problems that require computational solutions? They can if they learn to cooperate effectively with computer science students. The goal of the in-concert teaching approach is to introduce biology students to computational thinking by engaging them in collaborative projects structured around the software development process. Our approach emphasizes development of interdisciplinary communication and collaboration skills for both life science and computer science students. PMID:25411792

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

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...

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

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.

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…

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.

Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets

PubMed Central

McKinney, Bill; Meyer, Peter A.; Crosas, Mercè; Sliz, Piotr

2016-01-01

Access to experimental X-ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof-of-concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open-source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension—functionality supporting preservation of filesystem structure within Dataverse—which is essential for both in-place computation and supporting non-http data transfers. PMID:27862010

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. PMID:23721297

Challenges in structural approaches to cell modeling.

PubMed

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

2016-07-31

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

Pyramidal neurovision architecture for vision machines

NASA Astrophysics Data System (ADS)

Gupta, Madan M.; Knopf, George K.

1993-08-01

The vision system employed by an intelligent robot must be active; active in the sense that it must be capable of selectively acquiring the minimal amount of relevant information for a given task. An efficient active vision system architecture that is based loosely upon the parallel-hierarchical (pyramidal) structure of the biological visual pathway is presented in this paper. Although the computational architecture of the proposed pyramidal neuro-vision system is far less sophisticated than the architecture of the biological visual pathway, it does retain some essential features such as the converging multilayered structure of its biological counterpart. In terms of visual information processing, the neuro-vision system is constructed from a hierarchy of several interactive computational levels, whereupon each level contains one or more nonlinear parallel processors. Computationally efficient vision machines can be developed by utilizing both the parallel and serial information processing techniques within the pyramidal computing architecture. A computer simulation of a pyramidal vision system for active scene surveillance is presented.

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

PubMed Central

Gilbert, David

2016-01-01

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

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

PubMed

Pârvu, Ovidiu; Gilbert, David

2016-01-01

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

An Introduction to Programming for Bioscientists: A Python-Based Primer

PubMed Central

Mura, Cameron

2016-01-01

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language’s usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a “variable,” the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences. PMID:27271528

An Introduction to Programming for Bioscientists: A Python-Based Primer.

PubMed

Ekmekci, Berk; McAnany, Charles E; Mura, Cameron

2016-06-01

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a "variable," the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.

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.

Rosen's (M,R) system in process algebra.

PubMed

Gatherer, Derek; Galpin, Vashti

2013-11-17

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

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets.

PubMed

McKinney, Bill; Meyer, Peter A; Crosas, Mercè; Sliz, Piotr

2017-01-01

Access to experimental X-ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof-of-concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open-source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension-functionality supporting preservation of file system structure within Dataverse-which is essential for both in-place computation and supporting non-HTTP data transfers. © 2016 New York Academy of Sciences.

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.

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.

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

Computer Simulation of Embryonic Systems: What can a virtual embryo teach us about developmental toxicity? (LA Conference on Computational Biology & Bioinformatics)

EPA Science Inventory

This presentation will cover work at EPA under the CSS program for: (1) Virtual Tissue Models built from the known biology of an embryological system and structured to recapitulate key cell signals and responses; (2) running the models with real (in vitro) or synthetic (in silico...

Ultra-Structure database design methodology for managing systems biology data and analyses

PubMed Central

Maier, Christopher W; Long, Jeffrey G; Hemminger, Bradley M; Giddings, Morgan C

2009-01-01

Background Modern, high-throughput biological experiments generate copious, heterogeneous, interconnected data sets. Research is dynamic, with frequently changing protocols, techniques, instruments, and file formats. Because of these factors, systems designed to manage and integrate modern biological data sets often end up as large, unwieldy databases that become difficult to maintain or evolve. The novel rule-based approach of the Ultra-Structure design methodology presents a potential solution to this problem. By representing both data and processes as formal rules within a database, an Ultra-Structure system constitutes a flexible framework that enables users to explicitly store domain knowledge in both a machine- and human-readable form. End users themselves can change the system's capabilities without programmer intervention, simply by altering database contents; no computer code or schemas need be modified. This provides flexibility in adapting to change, and allows integration of disparate, heterogenous data sets within a small core set of database tables, facilitating joint analysis and visualization without becoming unwieldy. Here, we examine the application of Ultra-Structure to our ongoing research program for the integration of large proteomic and genomic data sets (proteogenomic mapping). Results We transitioned our proteogenomic mapping information system from a traditional entity-relationship design to one based on Ultra-Structure. Our system integrates tandem mass spectrum data, genomic annotation sets, and spectrum/peptide mappings, all within a small, general framework implemented within a standard relational database system. General software procedures driven by user-modifiable rules can perform tasks such as logical deduction and location-based computations. The system is not tied specifically to proteogenomic research, but is rather designed to accommodate virtually any kind of biological research. Conclusion We find Ultra-Structure offers substantial benefits for biological information systems, the largest being the integration of diverse information sources into a common framework. This facilitates systems biology research by integrating data from disparate high-throughput techniques. It also enables us to readily incorporate new data types, sources, and domain knowledge with no change to the database structure or associated computer code. Ultra-Structure may be a significant step towards solving the hard problem of data management and integration in the systems biology era. PMID:19691849

WE-DE-202-02: Are Track Structure Simulations Truly Needed for Radiobiology at the Cellular and Tissue Levels?

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

Stewart, R.

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

WE-DE-202-01: Connecting Nanoscale Physics to Initial DNA Damage Through Track Structure Simulations

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

Schuemann, J.

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

A computer lab exploring evolutionary aspects of chromatin structure and dynamics for an undergraduate chromatin course*.

PubMed

Eirín-López, José M

2013-01-01

The study of chromatin constitutes one of the most active research fields in life sciences, being subject to constant revisions that continuously redefine the state of the art in its knowledge. As every other rapidly changing field, chromatin biology requires clear and straightforward educational strategies able to efficiently translate such a vast body of knowledge to the classroom. With this aim, the present work describes a multidisciplinary computer lab designed to introduce undergraduate students to the dynamic nature of chromatin, within the context of the one semester course "Chromatin: Structure, Function and Evolution." This exercise is organized in three parts including (a) molecular evolutionary biology of histone families (using the H1 family as example), (b) histone structure and variation across different animal groups, and (c) effect of histone diversity on nucleosome structure and chromatin dynamics. By using freely available bioinformatic tools that can be run on common computers, the concept of chromatin dynamics is interactively illustrated from a comparative/evolutionary perspective. At the end of this computer lab, students are able to translate the bioinformatic information into a biochemical context in which the relevance of histone primary structure on chromatin dynamics is exposed. During the last 8 years this exercise has proven to be a powerful approach for teaching chromatin structure and dynamics, allowing students a higher degree of independence during the processes of learning and self-assessment. Copyright © 2013 International Union of Biochemistry and Molecular Biology, Inc.

[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.

Evaluation of electrical fields inside a biological structure.

PubMed Central

Drago, G. P.; Ridella, S.

1982-01-01

A digital computer simulation has been carried out of exposure of a cell, modelled as a multilayered spherical structure, to an alternating electrical field. Electrical quantities of possible biological interest can be evaluated everywhere inside the cell. A strong frequency selective behaviour in the range 0-10 MHz has been obtained. PMID:6279135

Node fingerprinting: an efficient heuristic for aligning biological networks.

PubMed

Radu, Alex; Charleston, Michael

2014-10-01

With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

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…

Open source bioimage informatics for cell biology.

PubMed

Swedlow, Jason R; Eliceiri, Kevin W

2009-11-01

Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery.

Integrative structure modeling with the Integrative Modeling Platform.

PubMed

Webb, Benjamin; Viswanath, Shruthi; Bonomi, Massimiliano; Pellarin, Riccardo; Greenberg, Charles H; Saltzberg, Daniel; Sali, Andrej

2018-01-01

Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use. © 2017 The Protein Society.

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

PubMed

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

2018-06-05

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

Report of the matrix of biological knowledge workshop

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

Morowitz, H.J.; Smith, T.

1987-10-30

Current understanding of biology involves complex relationships rooted in enormous amounts of data. These data include entries from biochemistry, ecology, genetics, human and veterinary medicine, molecular structure studies, agriculture, embryology, systematics, and many other disciplines. The present wealth of biological data goes beyond past accumulations now include new understandings from molecular biology. Several important biological databases are currently being supported, and more are planned; however, major problems of interdatabase communication and management efficiency abound. Few scientists are currently capable of keeping up with this ever-increasing wealth of knowledge, let alone searching it efficiently for new or unsuspected links and importantmore » analogies. Yet this is what is required if the continued rapid generation of such data is to lead most effectively to the major conceptual, medical, and agricultural advances anticipated over the coming decades in the United States. The opportunity exists to combine the potential of modern computer science, database management, and artificial intelligence in a major effort to organize the vast wealth of biological and clinical data. The time is right because the amount of data is still manageable even in its current highly-fragmented form; important hardware and computer science tools have been greatly improved; and there have been recent fundamental advances in our comprehension of biology. This latter is particularly true at the molecular level where the information for nearly all higher structure and function is encoded. The organization of all biological experimental data coordinately within a structure incorporating our current understanding - the Matrix of Biological Knowledge - will provide the data and structure for the major advances foreseen in the years ahead.« less

RSRE: RNA structural robustness evaluator

PubMed Central

Shu, Wenjie; Zheng, Zhiqiang; Wang, Shengqi

2007-01-01

Biological robustness, defined as the ability to maintain stable functioning in the face of various perturbations, is an important and fundamental topic in current biology, and has become a focus of numerous studies in recent years. Although structural robustness has been explored in several types of RNA molecules, the origins of robustness are still controversial. Computational analysis results are needed to make up for the lack of evidence of robustness in natural biological systems. The RNA structural robustness evaluator (RSRE) web server presented here provides a freely available online tool to quantitatively evaluate the structural robustness of RNA based on the widely accepted definition of neutrality. Several classical structure comparison methods are employed; five randomization methods are implemented to generate control sequences; sub-optimal predicted structures can be optionally utilized to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate analysis. The RSRE will be helpful in the wide exploration of RNA structural robustness and will catalyze our understanding of RNA evolution. The RSRE web server is freely available at http://biosrv1.bmi.ac.cn/RSRE/ or http://biotech.bmi.ac.cn/RSRE/. PMID:17567615

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

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

NONE

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

Open source bioimage informatics for cell biology

PubMed Central

Swedlow, Jason R.; Eliceiri, Kevin W.

2009-01-01

Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery. PMID:19833518

Computer support for physiological cell modelling using an ontology on cell physiology.

PubMed

Takao, Shimayoshi; Kazuhiro, Komurasaki; Akira, Amano; Takeshi, Iwashita; Masanori, Kanazawa; Tetsuya, Matsuda

2006-01-01

The development of electrophysiological whole cell models to support the understanding of biological mechanisms is increasing rapidly. Due to the complexity of biological systems, comprehensive cell models, which are composed of many imported sub-models of functional elements, can get quite complicated as well, making computer modification difficult. Here, we propose a computer support to enhance structural changes of cell models, employing the markup languages CellML and our original PMSML (physiological model structure markup language), in addition to a new ontology for cell physiological modelling. In particular, a method to make references from CellML files to the ontology and a method to assist manipulation of model structures using markup languages together with the ontology are reported. Using these methods three software utilities, including a graphical model editor, are implemented. Experimental results proved that these methods are effective for the modification of electrophysiological models.

A combined computational and structural model of the full-length human prolactin receptor

PubMed Central

Bugge, Katrine; Papaleo, Elena; Haxholm, Gitte W.; Hopper, Jonathan T. S.; Robinson, Carol V.; Olsen, Johan G.; Lindorff-Larsen, Kresten; Kragelund, Birthe B.

2016-01-01

The prolactin receptor is an archetype member of the class I cytokine receptor family, comprising receptors with fundamental functions in biology as well as key drug targets. Structurally, each of these receptors represent an intriguing diversity, providing an exceptionally challenging target for structural biology. Here, we access the molecular architecture of the monomeric human prolactin receptor by combining experimental and computational efforts. We solve the NMR structure of its transmembrane domain in micelles and collect structural data on overlapping fragments of the receptor with small-angle X-ray scattering, native mass spectrometry and NMR spectroscopy. Along with previously published data, these are integrated by molecular modelling to generate a full receptor structure. The result provides the first full view of a class I cytokine receptor, exemplifying the architecture of more than 40 different receptor chains, and reveals that the extracellular domain is merely the tip of a molecular iceberg. PMID:27174498

Computational analysis of conserved RNA secondary structure in transcriptomes and genomes.

PubMed

Eddy, Sean R

2014-01-01

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

Protein Engineering: Development of a Metal Ion Dependent Switch

DTIC Science & Technology

2017-05-22

Society of Chemistry Royal Society of Chemistry Biochemistry PNAS Escherichia coli Journal of Biotechnology Biochemistry Nature Protocols Journal of...Molecular Biology Biochemistry Royal Society of Chemistry Proteins: Structure, Function, and Bioinformatics Journal of Molecular Biology Biophysical...Biophysical Journal Protein Science Journal of Computational Chemistry Current Opinion in Chemical Biology Royal Society of Chemistry

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

PubMed

Huiskonen, Juha T

2018-02-08

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

Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System

EPA Science Inventory

Computational toxicology combines data from high-throughput test methods, chemical structure analyses and other biological domains (e.g., genes, proteins, cells, tissues) with the goals of predicting and understanding the underlying mechanistic causes of chemical toxicity and for...

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.

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. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

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 management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu. PMID:18509477

A New Era for Cancer Target Therapies: Applying Systems Biology and Computer-Aided Drug Design to Cancer Therapies.

PubMed

Wong, Yung-Hao; Chiu, Chia-Chiun; Lin, Chih-Lung; Chen, Ting-Shou; Jheng, Bo-Ren; Lee, Yu-Ching; Chen, Jeremy; Chen, Bor-Sen

In recent years, many systems biology approaches have been used with various cancers. The materials described here can be used to build bases to discover novel cancer therapy targets in connection with computer-aided drug design (CADD). A deeper understanding of the mechanisms of cancer will provide more choices and correct strategies in the development of multiple target drug therapies, which is quite different from the traditional cancer single target therapy. Targeted therapy is one of the most powerful strategies against cancer and can also be applied to other diseases. Due to the large amount of progress in computer hardware and the theories of computational chemistry and physics, CADD has been the main strategy for developing novel drugs for cancer therapy. In contrast to traditional single target therapies, in this review we will emphasize the future direction of the field, i.e., multiple target therapies. Structure-based and ligand-based drug designs are the two main topics of CADD. The former needs both 3D protein structures and ligand structures, while the latter only needs ligand structures. Ordinarily it is estimated to take more than 14 years and 800 million dollars to develop a new drug. Many new CADD software programs and techniques have been developed in recent decades. We conclude with an example where we combined and applied systems biology and CADD to the core networks of four cancers and successfully developed a novel cocktail for drug therapy that treats multiple targets.

Statistical molecular design of balanced compound libraries for QSAR modeling.

PubMed

Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

2010-01-01

A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

The disruptive effect of lysozyme on the bacterial cell wall explored by an in-silico structural outlook.

PubMed

Primo, Emiliano D; Otero, Lisandro H; Ruiz, Francisco; Klinke, Sebastián; Giordano, Walter

2018-01-01

The bacterial cell wall, a structural unit of peptidoglycan polymer comprised of glycan strands consisting of a repeating disaccharide motif [N-acetylglucosamine (NAG) and N-acetylmuramylpentapeptide (NAM pentapeptide)], encases bacteria and provides structural integrity and protection. Lysozymes are enzymes that break down the bacterial cell wall and disrupt the bacterial life cycle by cleaving the linkage between the NAG and NAM carbohydrates. Lab exercises focused on the effects of lysozyme on the bacterial cell wall are frequently incorporated in biochemistry classes designed for undergraduate students in diverse fields as biology, microbiology, chemistry, agronomy, medicine, and veterinary medicine. Such exercises typically do not include structural data. We describe here a sequence of computer tasks designed to illustrate and reinforce both physiological and structural concepts involved in lysozyme effects on the bacterial cell-wall structure. This lab class usually lasts 3.5 hours. First, the instructor presents introductory concepts of the bacterial cell wall and the effect of lysozyme on its structure. Then, students are taught to use computer modeling to visualize the three-dimensional structure of a lysozyme in complex with bacterial cell-wall fragments. Finally, the lysozyme inhibitory effect on a bacterial culture is optionally proposed as a simple microbiological assay. The computer lab exercises described here give students a realistic understanding of the disruptive effect of lysozymes on the bacterial cell wall, a crucial component in bacterial survival. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):83-90, 2018. © 2017 The International Union of Biochemistry and Molecular Biology.

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

John Wooley; Herbert S. Lin

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

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.

Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion

PubMed Central

Rieffel, John A.; Valero-Cuevas, Francisco J.; Lipson, Hod

2010-01-01

Traditional engineering approaches strive to avoid, or actively suppress, nonlinear dynamic coupling among components. Biological systems, in contrast, are often rife with these dynamics. Could there be, in some cases, a benefit to high degrees of dynamical coupling? Here we present a distributed robotic control scheme inspired by the biological phenomenon of tensegrity-based mechanotransduction. This emergence of morphology-as-information-conduit or ‘morphological communication’, enabled by time-sensitive spiking neural networks, presents a new paradigm for the decentralized control of large, coupled, modular systems. These results significantly bolster, both in magnitude and in form, the idea of morphological computation in robotic control. Furthermore, they lend further credence to ideas of embodied anatomical computation in biological systems, on scales ranging from cellular structures up to the tendinous networks of the human hand. PMID:19776146

Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction.

PubMed

Kuhn, Stefan; Egert, Björn; Neumann, Steffen; Steinbeck, Christoph

2008-09-25

Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

New strategy for protein interactions and application to structure-based drug design

NASA Astrophysics Data System (ADS)

Zou, Xiaoqin

One of the greatest challenges in computational biophysics is to predict interactions between biological molecules, which play critical roles in biological processes and rational design of therapeutic drugs. Biomolecular interactions involve delicate interplay between multiple interactions, including electrostatic interactions, van der Waals interactions, solvent effect, and conformational entropic effect. Accurate determination of these complex and subtle interactions is challenging. Moreover, a biological molecule such as a protein usually consists of thousands of atoms, and thus occupies a huge conformational space. The large degrees of freedom pose further challenges for accurate prediction of biomolecular interactions. Here, I will present our development of physics-based theory and computational modeling on protein interactions with other molecules. The major strategy is to extract microscopic energetics from the information embedded in the experimentally-determined structures of protein complexes. I will also present applications of the methods to structure-based therapeutic design. Supported by NSF CAREER Award DBI-0953839, NIH R01GM109980, and the American Heart Association (Midwest Affiliate) [13GRNT16990076].

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

McMahon, S.

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

Structural alignment of protein descriptors - a combinatorial model.

PubMed

Antczak, Maciej; Kasprzak, Marta; Lukasiak, Piotr; Blazewicz, Jacek

2016-09-17

Structural alignment of proteins is one of the most challenging problems in molecular biology. The tertiary structure of a protein strictly correlates with its function and computationally predicted structures are nowadays a main premise for understanding the latter. However, computationally derived 3D models often exhibit deviations from the native structure. A way to confirm a model is a comparison with other structures. The structural alignment of a pair of proteins can be defined with the use of a concept of protein descriptors. The protein descriptors are local substructures of protein molecules, which allow us to divide the original problem into a set of subproblems and, consequently, to propose a more efficient algorithmic solution. In the literature, one can find many applications of the descriptors concept that prove its usefulness for insight into protein 3D structures, but the proposed approaches are presented rather from the biological perspective than from the computational or algorithmic point of view. Efficient algorithms for identification and structural comparison of descriptors can become crucial components of methods for structural quality assessment as well as tertiary structure prediction. In this paper, we propose a new combinatorial model and new polynomial-time algorithms for the structural alignment of descriptors. The model is based on the maximum-size assignment problem, which we define here and prove that it can be solved in polynomial time. We demonstrate suitability of this approach by comparison with an exact backtracking algorithm. Besides a simplification coming from the combinatorial modeling, both on the conceptual and complexity level, we gain with this approach high quality of obtained results, in terms of 3D alignment accuracy and processing efficiency. All the proposed algorithms were developed and integrated in a computationally efficient tool descs-standalone, which allows the user to identify and structurally compare descriptors of biological molecules, such as proteins and RNAs. Both PDB (Protein Data Bank) and mmCIF (macromolecular Crystallographic Information File) formats are supported. The proposed tool is available as an open source project stored on GitHub ( https://github.com/mantczak/descs-standalone ).

Extended Full Computation-Tree Logic with Sequence Modal Operator: Representing Hierarchical Tree Structures

NASA Astrophysics Data System (ADS)

Kamide, Norihiro; Kaneiwa, Ken

An extended full computation-tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical tree structures where sequence modal operators in CTLS* are applied to tree structures. An embedding theorem of CTLS* into CTL* is proved. The validity, satisfiability and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas.

Cross-Linking and Mass Spectrometry Methodologies to Facilitate Structural Biology: Finding a Path through the Maze

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

Merkley, Eric D.; Cort, John R.; Adkins, Joshua N.

2013-09-01

Multiprotein complexes, rather than individual proteins, make up a large part of the biological macromolecular machinery of a cell. Understanding the structure and organization of these complexes is critical to understanding cellular function. Chemical cross-linking coupled with mass spectrometry is emerging as a complementary technique to traditional structural biology methods and can provide low-resolution structural information for a multitude of purposes, such as distance constraints in computational modeling of protein complexes. In this review, we discuss the experimental considerations for successful application of chemical cross-linking-mass spectrometry in biological studies and highlight three examples of such studies from the recent literature.more » These examples (as well as many others) illustrate the utility of a chemical cross-linking-mass spectrometry approach in facilitating structural analysis of large and challenging complexes.« less

Culture and biology in the origins of linguistic structure.

PubMed

Kirby, Simon

2017-02-01

Language is systematically structured at all levels of description, arguably setting it apart from all other instances of communication in nature. In this article, I survey work over the last 20 years that emphasises the contributions of individual learning, cultural transmission, and biological evolution to explaining the structural design features of language. These 3 complex adaptive systems exist in a network of interactions: individual learning biases shape the dynamics of cultural evolution; universal features of linguistic structure arise from this cultural process and form the ultimate linguistic phenotype; the nature of this phenotype affects the fitness landscape for the biological evolution of the language faculty; and in turn this determines individuals' learning bias. Using a combination of computational simulation, laboratory experiments, and comparison with real-world cases of language emergence, I show that linguistic structure emerges as a natural outcome of cultural evolution once certain minimal biological requirements are in place.

Leveraging unsupervised training sets for multi-scale compartmentalization in renal pathology

NASA Astrophysics Data System (ADS)

Lutnick, Brendon; Tomaszewski, John E.; Sarder, Pinaki

2017-03-01

Clinical pathology relies on manual compartmentalization and quantification of biological structures, which is time consuming and often error-prone. Application of computer vision segmentation algorithms to histopathological image analysis, in contrast, can offer fast, reproducible, and accurate quantitative analysis to aid pathologists. Algorithms tunable to different biologically relevant structures can allow accurate, precise, and reproducible estimates of disease states. In this direction, we have developed a fast, unsupervised computational method for simultaneously separating all biologically relevant structures from histopathological images in multi-scale. Segmentation is achieved by solving an energy optimization problem. Representing the image as a graph, nodes (pixels) are grouped by minimizing a Potts model Hamiltonian, adopted from theoretical physics, modeling interacting electron spins. Pixel relationships (modeled as edges) are used to update the energy of the partitioned graph. By iteratively improving the clustering, the optimal number of segments is revealed. To reduce computational time, the graph is simplified using a Cantor pairing function to intelligently reduce the number of included nodes. The classified nodes are then used to train a multiclass support vector machine to apply the segmentation over the full image. Accurate segmentations of images with as many as 106 pixels can be completed only in 5 sec, allowing for attainable multi-scale visualization. To establish clinical potential, we employed our method in renal biopsies to quantitatively visualize for the first time scale variant compartments of heterogeneous intra- and extraglomerular structures simultaneously. Implications of the utility of our method extend to fields such as oncology, genomics, and non-biological problems.

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

PubMed

Masoudi-Nejad, Ali; Asgari, Yazdan

2015-02-01

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

Perceptual organization in computer vision - A review and a proposal for a classificatory structure

NASA Technical Reports Server (NTRS)

Sarkar, Sudeep; Boyer, Kim L.

1993-01-01

The evolution of perceptual organization in biological vision, and its necessity in advanced computer vision systems, arises from the characteristic that perception, the extraction of meaning from sensory input, is an intelligent process. This is particularly so for high order organisms and, analogically, for more sophisticated computational models. The role of perceptual organization in computer vision systems is explored. This is done from four vantage points. First, a brief history of perceptual organization research in both humans and computer vision is offered. Next, a classificatory structure in which to cast perceptual organization research to clarify both the nomenclature and the relationships among the many contributions is proposed. Thirdly, the perceptual organization work in computer vision in the context of this classificatory structure is reviewed. Finally, the array of computational techniques applied to perceptual organization problems in computer vision is surveyed.

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

PubMed Central

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

2015-01-01

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

Computational study of stability of an H-H-type pseudoknot motif.

PubMed

Wang, Jun; Zhao, Yunjie; Wang, Jian; Xiao, Yi

2015-12-01

Motifs in RNA tertiary structures are important to their structural organizations and biological functions. Here we consider an H-H-type pseudoknot (HHpk) motif that consists of two hairpins connected by a junction loop and with kissing interactions between the two hairpin loops. Such a tertiary structural motif is recurrently found in RNA tertiary structures, but is difficult to predict computationally. So it is important to understand the mechanism of its formation and stability. Here we investigate the stability of the HHpk tertiary structure by using an all-atom molecular dynamics simulation. The results indicate that the HHpk tertiary structure is stable. However, it is found that this stability is not due to the helix-helix packing, as is usually expected, but is maintained by the combined action of the kissing hairpin loops and junctions, although the former plays the main role. Stable HHpk motifs may form structural platforms for the molecules to realize their biological functions. These results are useful for understanding the construction principle of RNA tertiary structures and structure prediction.

Incorporating modeling and simulations in undergraduate biophysical chemistry course to promote understanding of structure-dynamics-function relationships in proteins.

PubMed

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and simulations. In particular, modern computational tools are employed to elucidate the relationship between structure, dynamics, and function in proteins. Computer-based laboratory protocols that we introduced in three modules allow students to visualize the secondary, super-secondary, and tertiary structures of proteins, analyze non-covalent interactions in protein-ligand complexes, develop three-dimensional structural models (homology model) for new protein sequences and evaluate their structural qualities, and study proteins' intrinsic dynamics to understand their functions. In the fourth module, students are assigned to an authentic research problem, where they apply their laboratory skills (acquired in modules 1-3) to answer conceptual biophysical questions. Through this process, students gain in-depth understanding of protein dynamics-the missing link between structure and function. Additionally, the requirement of term papers sharpens students' writing and communication skills. Finally, these projects result in new findings that are communicated in peer-reviewed journals. © 2016 The International Union of Biochemistry and Molecular Biology.

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

Bimolecular dynamics by computer analysis

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

Eilbeck, J.C.; Lomdahl, P.S.; Scott, A.C.

1984-01-01

As numerical tools (computers and display equipment) become more powerful and the atomic structures of important biological molecules become known, the importance of detailed computation of nonequilibrium biomolecular dynamics increases. In this manuscript we report results from a well developed study of the hydrogen bonded polypeptide crystal acetanilide, a model protein. Directions for future research are suggested. 9 references, 6 figures.

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.

A 3D puzzle approach to building protein-DNA structures.

PubMed

Hinton, Deborah M

2017-03-15

Despite recent advances in structural analysis, it is still challenging to obtain a high-resolution structure for a complex of RNA polymerase, transcriptional factors, and DNA. However, using biochemical constraints, 3D printed models of available structures, and computer modeling, one can build biologically relevant models of such supramolecular complexes.

Aggregating Data for Computational Toxicology Applications ...

EPA Pesticide Factsheets

Computational toxicology combines data from high-throughput test methods, chemical structure analyses and other biological domains (e.g., genes, proteins, cells, tissues) with the goals of predicting and understanding the underlying mechanistic causes of chemical toxicity and for predicting toxicity of new chemicals and products. A key feature of such approaches is their reliance on knowledge extracted from large collections of data and data sets in computable formats. The U.S. Environmental Protection Agency (EPA) has developed a large data resource called ACToR (Aggregated Computational Toxicology Resource) to support these data-intensive efforts. ACToR comprises four main repositories: core ACToR (chemical identifiers and structures, and summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data from guideline studies), ExpoCastDB (detailed human exposure data from observational studies of selected chemicals), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information related to genes and pathways). The EPA DSSTox (Distributed Structure-Searchable Toxicity) program provides expert-reviewed chemical structures and associated information for these and other high-interest public inventories. Overall, the ACToR system contains information on about 400,000 chemicals from 1100 different sources. The entire system is built usi

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

PubMed

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

2003-01-01

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

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.

Computational Lipidomics and Lipid Bioinformatics: Filling In the Blanks.

PubMed

Pauling, Josch; Klipp, Edda

2016-12-22

Lipids are highly diverse metabolites of pronounced importance in health and disease. While metabolomics is a broad field under the omics umbrella that may also relate to lipids, lipidomics is an emerging field which specializes in the identification, quantification and functional interpretation of complex lipidomes. Today, it is possible to identify and distinguish lipids in a high-resolution, high-throughput manner and simultaneously with a lot of structural detail. However, doing so may produce thousands of mass spectra in a single experiment which has created a high demand for specialized computational support to analyze these spectral libraries. The computational biology and bioinformatics community has so far established methodology in genomics, transcriptomics and proteomics but there are many (combinatorial) challenges when it comes to structural diversity of lipids and their identification, quantification and interpretation. This review gives an overview and outlook on lipidomics research and illustrates ongoing computational and bioinformatics efforts. These efforts are important and necessary steps to advance the lipidomics field alongside analytic, biochemistry, biomedical and biology communities and to close the gap in available computational methodology between lipidomics and other omics sub-branches.

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…

Evolutionary Models for Simple Biosystems

NASA Astrophysics Data System (ADS)

Bagnoli, Franco

The concept of evolutionary development of structures constituted a real revolution in biology: it was possible to understand how the very complex structures of life can arise in an out-of-equilibrium system. The investigation of such systems has shown that indeed, systems under a flux of energy or matter can self-organize into complex patterns, think for instance to Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular systems under shear. Following this line, one could characterize life as a state of matter, characterized by the slow, continuous process that we call evolution. In this paper we try to identify the organizational level of life, that spans several orders of magnitude from the elementary constituents to whole ecosystems. Although similar structures can be found in other contexts like ideas (memes) in neural systems and self-replicating elements (computer viruses, worms, etc.) in computer systems, we shall concentrate on biological evolutionary structure, and try to put into evidence the role and the emergence of network structure in such systems.

What is bioinformatics? A proposed definition and overview of the field.

PubMed

Luscombe, N M; Greenbaum, D; Gerstein, M

2001-01-01

The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Exploring autonomy through computational biomodelling.

PubMed

Palfreyman, Niall

2009-07-01

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

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.

Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks

PubMed Central

2018-01-01

Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way. PMID:29370181

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.

Computational power and generative capacity of genetic systems.

PubMed

Igamberdiev, Abir U; Shklovskiy-Kordi, Nikita E

2016-01-01

Semiotic characteristics of genetic sequences are based on the general principles of linguistics formulated by Ferdinand de Saussure, such as the arbitrariness of sign and the linear nature of the signifier. Besides these semiotic features that are attributable to the basic structure of the genetic code, the principle of generativity of genetic language is important for understanding biological transformations. The problem of generativity in genetic systems arises to a possibility of different interpretations of genetic texts, and corresponds to what Alexander von Humboldt called "the infinite use of finite means". These interpretations appear in the individual development as the spatiotemporal sequences of realizations of different textual meanings, as well as the emergence of hyper-textual statements about the text itself, which underlies the process of biological evolution. These interpretations are accomplished at the level of the readout of genetic texts by the structures defined by Efim Liberman as "the molecular computer of cell", which includes DNA, RNA and the corresponding enzymes operating with molecular addresses. The molecular computer performs physically manifested mathematical operations and possesses both reading and writing capacities. Generativity paradoxically resides in the biological computational system as a possibility to incorporate meta-statements about the system, and thus establishes the internal capacity for its evolution. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Chemical Structure-Biological Activity Models for Pharmacophores’ 3D-Interactions

PubMed Central

Putz, Mihai V.; Duda-Seiman, Corina; Duda-Seiman, Daniel; Putz, Ana-Maria; Alexandrescu, Iulia; Mernea, Maria; Avram, Speranta

2016-01-01

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners’ (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions. PMID:27399692

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

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

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

RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

ERIC Educational Resources Information Center

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses…

Conformational dynamics of proanthocyanidins: physical and computational approaches

Treesearch

Fred L. Tobiason; Richard W. Hemingway; T. Hatano

1998-01-01

The interaction of plant polyphenols with proteins accounts for a good part of their commercial (e.g., leather manufacture) and biological (e.g., antimicrobial activity) significance. The interplay between observations of physical data such as crystal structure, NMR analyses, and time-resolved fluorescence with results of computational chemistry approaches has been...

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

PubMed Central

Raikov, Ivan; De Schutter, Erik

2012-01-01

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

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

PubMed

Raikov, Ivan; De Schutter, Erik

2012-01-01

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

Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface

PubMed Central

2012-01-01

Background Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface. Methods This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima. Results and conclusions The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework. PMID:22759582

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

ERIC Educational Resources Information Center

Street, Garrett M.; Laubach, Timothy A.

2013-01-01

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

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

ERIC Educational Resources Information Center

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

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

A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research

ERIC Educational Resources Information Center

Magana, Alejandra J.; Taleyarkhan, Manaz; Alvarado, Daniela Rivera; Kane, Michael; Springer, John; Clase, Kari

2014-01-01

Bioinformatics education can be broadly defined as the teaching and learning of the use of computer and information technology, along with mathematical and statistical analysis for gathering, storing, analyzing, interpreting, and integrating data to solve biological problems. The recent surge of genomics, proteomics, and structural biology in the…

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions

PubMed Central

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

2017-01-01

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

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

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

2015-09-04

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

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

PubMed

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

2015-06-01

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

cellPACK: A Virtual Mesoscope to Model and Visualize Structural Systems Biology

PubMed Central

Johnson, Graham T.; Autin, Ludovic; Al-Alusi, Mostafa; Goodsell, David S.; Sanner, Michel F.; Olson, Arthur J.

2014-01-01

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10−7–10−8m) between molecular and cellular biology. cellPACK’s modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive 3D models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is currently available as open source code, with tools for validation of models and with recipes and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators, and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org. PMID:25437435

Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System

PubMed Central

Judson, Richard S.; Martin, Matthew T.; Egeghy, Peter; Gangwal, Sumit; Reif, David M.; Kothiya, Parth; Wolf, Maritja; Cathey, Tommy; Transue, Thomas; Smith, Doris; Vail, James; Frame, Alicia; Mosher, Shad; Cohen Hubal, Elaine A.; Richard, Ann M.

2012-01-01

Computational toxicology combines data from high-throughput test methods, chemical structure analyses and other biological domains (e.g., genes, proteins, cells, tissues) with the goals of predicting and understanding the underlying mechanistic causes of chemical toxicity and for predicting toxicity of new chemicals and products. A key feature of such approaches is their reliance on knowledge extracted from large collections of data and data sets in computable formats. The U.S. Environmental Protection Agency (EPA) has developed a large data resource called ACToR (Aggregated Computational Toxicology Resource) to support these data-intensive efforts. ACToR comprises four main repositories: core ACToR (chemical identifiers and structures, and summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data from guideline studies), ExpoCastDB (detailed human exposure data from observational studies of selected chemicals), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information related to genes and pathways). The EPA DSSTox (Distributed Structure-Searchable Toxicity) program provides expert-reviewed chemical structures and associated information for these and other high-interest public inventories. Overall, the ACToR system contains information on about 400,000 chemicals from 1100 different sources. The entire system is built using open source tools and is freely available to download. This review describes the organization of the data repository and provides selected examples of use cases. PMID:22408426

Aggregating data for computational toxicology applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System.

PubMed

Judson, Richard S; Martin, Matthew T; Egeghy, Peter; Gangwal, Sumit; Reif, David M; Kothiya, Parth; Wolf, Maritja; Cathey, Tommy; Transue, Thomas; Smith, Doris; Vail, James; Frame, Alicia; Mosher, Shad; Cohen Hubal, Elaine A; Richard, Ann M

2012-01-01

Computational toxicology combines data from high-throughput test methods, chemical structure analyses and other biological domains (e.g., genes, proteins, cells, tissues) with the goals of predicting and understanding the underlying mechanistic causes of chemical toxicity and for predicting toxicity of new chemicals and products. A key feature of such approaches is their reliance on knowledge extracted from large collections of data and data sets in computable formats. The U.S. Environmental Protection Agency (EPA) has developed a large data resource called ACToR (Aggregated Computational Toxicology Resource) to support these data-intensive efforts. ACToR comprises four main repositories: core ACToR (chemical identifiers and structures, and summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data from guideline studies), ExpoCastDB (detailed human exposure data from observational studies of selected chemicals), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information related to genes and pathways). The EPA DSSTox (Distributed Structure-Searchable Toxicity) program provides expert-reviewed chemical structures and associated information for these and other high-interest public inventories. Overall, the ACToR system contains information on about 400,000 chemicals from 1100 different sources. The entire system is built using open source tools and is freely available to download. This review describes the organization of the data repository and provides selected examples of use cases.

High-throughput Crystallography for Structural Genomics

PubMed Central

Joachimiak, Andrzej

2009-01-01

Protein X-ray crystallography recently celebrated its 50th anniversary. The structures of myoglobin and hemoglobin determined by Kendrew and Perutz provided the first glimpses into the complex protein architecture and chemistry. Since then, the field of structural molecular biology has experienced extraordinary progress and now over 53,000 proteins structures have been deposited into the Protein Data Bank. In the past decade many advances in macromolecular crystallography have been driven by world-wide structural genomics efforts. This was made possible because of third-generation synchrotron sources, structure phasing approaches using anomalous signal and cryo-crystallography. Complementary progress in molecular biology, proteomics, hardware and software for crystallographic data collection, structure determination and refinement, computer science, databases, robotics and automation improved and accelerated many processes. These advancements provide the robust foundation for structural molecular biology and assure strong contribution to science in the future. In this report we focus mainly on reviewing structural genomics high-throughput X-ray crystallography technologies and their impact. PMID:19765976

Computer-Aided Drug Design Methods.

PubMed

Yu, Wenbo; MacKerell, Alexander D

2017-01-01

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

StrBioLib: a Java library for development of custom computational structural biology applications.

PubMed

Chandonia, John-Marc

2007-08-01

StrBioLib is a library of Java classes useful for developing software for computational structural biology research. StrBioLib contains classes to represent and manipulate protein structures, biopolymer sequences, sets of biopolymer sequences, and alignments between biopolymers based on either sequence or structure. Interfaces are provided to interact with commonly used bioinformatics applications, including (psi)-blast, modeller, muscle and Primer3, and tools are provided to read and write many file formats used to represent bioinformatic data. The library includes a general-purpose neural network object with multiple training algorithms, the Hooke and Jeeves non-linear optimization algorithm, and tools for efficient C-style string parsing and formatting. StrBioLib is the basis for the Pred2ary secondary structure prediction program, is used to build the astral compendium for sequence and structure analysis, and has been extensively tested through use in many smaller projects. Examples and documentation are available at the site below. StrBioLib may be obtained under the terms of the GNU LGPL license from http://strbio.sourceforge.net/

Chemical and protein structural basis for biological crosstalk between PPAR α and COX enzymes

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2015-02-01

We have previously validated a probabilistic framework that combined computational approaches for predicting the biological activities of small molecule drugs. Molecule comparison methods included molecular structural similarity metrics and similarity computed from lexical analysis of text in drug package inserts. Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. Considering those cases where the predicted target was an enzyme with known 3D structure allowed incorporation of information from molecular docking and protein binding pocket similarity in addition to ligand-based comparisons. Taken together, the combination of orthogonal information sources led to investigation of a surprising predicted relationship between a transcription factor and an enzyme, specifically, PPAR α and the cyclooxygenase enzymes. These predictions were confirmed by direct biochemical experiments which validate the approach and show for the first time that PPAR α agonists are cyclooxygenase inhibitors.

Recent advances in jointed quantum mechanics and molecular mechanics calculations of biological macromolecules: schemes and applications coupled to ab initio calculations.

PubMed

Hagiwara, Yohsuke; Tateno, Masaru

2010-10-20

We review the recent research on the functional mechanisms of biological macromolecules using theoretical methodologies coupled to ab initio quantum mechanical (QM) treatments of reaction centers in proteins and nucleic acids. Since in most cases such biological molecules are large, the computational costs of performing ab initio calculations for the entire structures are prohibitive. Instead, simulations that are jointed with molecular mechanics (MM) calculations are crucial to evaluate the long-range electrostatic interactions, which significantly affect the electronic structures of biological macromolecules. Thus, we focus our attention on the methodologies/schemes and applications of jointed QM/MM calculations, and discuss the critical issues to be elucidated in biological macromolecular systems. © 2010 IOP Publishing Ltd

Software and resources for computational medicinal chemistry

PubMed Central

Liao, Chenzhong; Sitzmann, Markus; Pugliese, Angelo; Nicklaus, Marc C

2011-01-01

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools. PMID:21707404

ePlant and the 3D data display initiative: integrative systems biology on the world wide web.

PubMed

Fucile, Geoffrey; Di Biase, David; Nahal, Hardeep; La, Garon; Khodabandeh, Shokoufeh; Chen, Yani; Easley, Kante; Christendat, Dinesh; Kelley, Lawrence; Provart, Nicholas J

2011-01-10

Visualization tools for biological data are often limited in their ability to interactively integrate data at multiple scales. These computational tools are also typically limited by two-dimensional displays and programmatic implementations that require separate configurations for each of the user's computing devices and recompilation for functional expansion. Towards overcoming these limitations we have developed "ePlant" (http://bar.utoronto.ca/eplant) - a suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. These tools display data spanning multiple biological scales on interactive three-dimensional models. Currently, ePlant consists of the following modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. The ePlant's protein structure explorer module represents experimentally determined and theoretical structures covering >70% of the Arabidopsis proteome. The ePlant framework is accessed entirely through a web browser, and is therefore platform-independent. It can be applied to any model organism. To facilitate the development of three-dimensional displays of biological data on the world wide web we have established the "3D Data Display Initiative" (http://3ddi.org).

Computer Center: 2 HyperCard Stacks for Biology.

ERIC Educational Resources Information Center

Duhrkopf, Richard, Ed.

1989-01-01

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

Integrating structure-based and ligand-based approaches for computational drug design.

PubMed

Wilson, Gregory L; Lill, Markus A

2011-04-01

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

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.

Coarse-grained modeling of RNA 3D structure.

PubMed

Dawson, Wayne K; Maciejczyk, Maciej; Jankowska, Elzbieta J; Bujnicki, Janusz M

2016-07-01

Functional RNA molecules depend on three-dimensional (3D) structures to carry out their tasks within the cell. Understanding how these molecules interact to carry out their biological roles requires a detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods also have their limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible. This review presents an introduction to the topic of coarse-grained modeling of RNA 3D structures and dynamics, covering both high- and low-resolution strategies. We discuss how physics-based approaches compare with knowledge based methods that rely on databases of information. In the course of this review, we discuss important aspects in the reasoning process behind building different models and the goals and pitfalls that can result. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The evolvability of programmable hardware.

PubMed

Raman, Karthik; Wagner, Andreas

2011-02-06

In biological systems, individual phenotypes are typically adopted by multiple genotypes. Examples include protein structure phenotypes, where each structure can be adopted by a myriad individual amino acid sequence genotypes. These genotypes form vast connected 'neutral networks' in genotype space. The size of such neutral networks endows biological systems not only with robustness to genetic change, but also with the ability to evolve a vast number of novel phenotypes that occur near any one neutral network. Whether technological systems can be designed to have similar properties is poorly understood. Here we ask this question for a class of programmable electronic circuits that compute digital logic functions. The functional flexibility of such circuits is important in many applications, including applications of evolutionary principles to circuit design. The functions they compute are at the heart of all digital computation. We explore a vast space of 10(45) logic circuits ('genotypes') and 10(19) logic functions ('phenotypes'). We demonstrate that circuits that compute the same logic function are connected in large neutral networks that span circuit space. Their robustness or fault-tolerance varies very widely. The vicinity of each neutral network contains circuits with a broad range of novel functions. Two circuits computing different functions can usually be converted into one another via few changes in their architecture. These observations show that properties important for the evolvability of biological systems exist in a commercially important class of electronic circuitry. They also point to generic ways to generate fault-tolerant, adaptable and evolvable electronic circuitry.

The evolvability of programmable hardware

PubMed Central

Raman, Karthik; Wagner, Andreas

2011-01-01

In biological systems, individual phenotypes are typically adopted by multiple genotypes. Examples include protein structure phenotypes, where each structure can be adopted by a myriad individual amino acid sequence genotypes. These genotypes form vast connected ‘neutral networks’ in genotype space. The size of such neutral networks endows biological systems not only with robustness to genetic change, but also with the ability to evolve a vast number of novel phenotypes that occur near any one neutral network. Whether technological systems can be designed to have similar properties is poorly understood. Here we ask this question for a class of programmable electronic circuits that compute digital logic functions. The functional flexibility of such circuits is important in many applications, including applications of evolutionary principles to circuit design. The functions they compute are at the heart of all digital computation. We explore a vast space of 1045 logic circuits (‘genotypes’) and 1019 logic functions (‘phenotypes’). We demonstrate that circuits that compute the same logic function are connected in large neutral networks that span circuit space. Their robustness or fault-tolerance varies very widely. The vicinity of each neutral network contains circuits with a broad range of novel functions. Two circuits computing different functions can usually be converted into one another via few changes in their architecture. These observations show that properties important for the evolvability of biological systems exist in a commercially important class of electronic circuitry. They also point to generic ways to generate fault-tolerant, adaptable and evolvable electronic circuitry. PMID:20534598

The World as Viewed by and with Unpaired Electrons

PubMed Central

Eaton, Sandra S.; Eaton, Gareth R.

2012-01-01

Recent advances in electron paramagnetic resonance (EPR) include capabilities for applications to areas as diverse as archeology, beer shelf life, biological structure, dosimetry, in vivo imaging, molecular magnets, and quantum computing. Enabling technologies include multifrequency continuous wave, pulsed, and rapid scan EPR. Interpretation is enhanced by increasingly powerful computational models. PMID:22975244

ISMB Conference Funding to Support Attendance of Early Researchers and Students

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

Gaasterland, Terry

ISMB Conference Funding for Students and Young Scientists Historical Description The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 22 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on “intelligent systems” and actual biological data makes ISMB a unique and highly important meeting.more » 21 years of experience in holding the conference has resulted in a consistently well-organized, well attended, and highly respected annual conference. "Intelligent systems" include any software which goes beyond straightforward, closed-form algorithms or standard database technologies, and encompasses those that view data in a symbolic fashion, learn from examples, consolidate multiple levels of abstraction, or synthesize results to be cognitively tractable to a human, including the development and application of advanced computational methods for biological problems. Relevant computational techniques include, but are not limited to: machine learning, pattern recognition, knowledge representation, databases, combinatorics, stochastic modeling, string and graph algorithms, linguistic methods, robotics, constraint satisfaction, and parallel computation. Biological areas of interest include molecular structure, genomics, molecular sequence analysis, evolution and phylogenetics, molecular interactions, metabolic pathways, regulatory networks, developmental control, and molecular biology generally. Emphasis is placed on the validation of methods using real data sets, on practical applications in the biological sciences, and on development of novel computational techniques. The ISMB conferences are distinguished from many other conferences in computational biology or artificial intelligence by an insistence that the researchers work with real molecular biology data, not theoretical or toy examples; and from many other biological conferences by providing a forum for technical advances as they occur, which otherwise may be shunned until a firm experimental result is published. The resulting intellectual richness and cross-disciplinary diversity provides an important opportunity for both students and senior researchers. ISMB has become the premier conference series in this field with refereed, published proceedings, establishing an infrastructure to promote the growing body of research.« less

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.

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

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems.

PubMed

Williams, Richard A; Timmis, Jon; Qwarnstrom, Eva E

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

PubMed Central

Timmis, Jon; Qwarnstrom, Eva E.

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model. PMID:27571414

Integrative, Dynamic Structural Biology at Atomic Resolution—It’s About Time

PubMed Central

van den Bedem, Henry; Fraser, James S.

2015-01-01

Biomolecules adopt a dynamic ensemble of conformations, each with the potential to interact with binding partners or perform the chemical reactions required for a multitude of cellular functions. Recent advances in X-ray crystallography, Nuclear Magnetic Resonance (NMR) spectroscopy, and other techniques are helping us realize the dream of seeing—in atomic detail—how different parts of biomolecules exchange between functional sub-states using concerted motions. Integrative structural biology has advanced our understanding of the formation of large macromolecular complexes and how their components interact in assemblies by leveraging data from many low-resolution methods. Here, we review the growing opportunities for integrative, dynamic structural biology at the atomic scale, contending there is increasing synergistic potential between X-ray crystallography, NMR, and computer simulations to reveal a structural basis for protein conformational dynamics at high resolution. PMID:25825836

Integrated Teaching of Structure-Based Drug Design and Biopharmaceutics: A Computer-Based Approach

ERIC Educational Resources Information Center

Sutch, Brian T.; Romero, Rebecca M.; Neamati, Nouri; Haworth, Ian S.

2012-01-01

Rational drug design requires expertise in structural biology, medicinal chemistry, physiology, and related fields. In teaching structure-based drug design, it is important to develop an understanding of the need for early recognition of molecules with "drug-like" properties as a key component. That is, it is not merely sufficient to teach…

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

PubMed

Bordner, Andrew J; Gorin, Andrey A

2008-05-12

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

Biomolecular electrostatics and solvation: a computational perspective

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

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G.

2012-11-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. Thismore » review discusses the solvation of biomolecules with a computational biophysics view towards describing the phenomenon. While our main focus lies on the computational aspect of the models, we summarize the common characteristics of biomolecular solvation (e.g., solvent structure, polarization, ion binding, and nonpolar behavior) in order to provide reasonable backgrounds to understand the solvation models.« less

Protocols for Molecular Dynamics Simulations of RNA Nanostructures.

PubMed

Kim, Taejin; Kasprzak, Wojciech K; Shapiro, Bruce A

2017-01-01

Molecular dynamics (MD) simulations have been used as one of the main research tools to study a wide range of biological systems and bridge the gap between X-ray crystallography or NMR structures and biological mechanism. In the field of RNA nanostructures, MD simulations have been used to fix steric clashes in computationally designed RNA nanostructures, characterize the dynamics, and investigate the interaction between RNA and other biomolecules such as delivery agents and membranes.In this chapter we present examples of computational protocols for molecular dynamics simulations in explicit and implicit solvent using the Amber Molecular Dynamics Package. We also show examples of post-simulation analysis steps and briefly mention selected tools beyond the Amber package. Limitations of the methods, tools, and protocols are also discussed. Most of the examples are illustrated for a small RNA duplex (helix), but the protocols are applicable to any nucleic acid structure, subject only to the computational speed and memory limitations of the hardware available to the user.

Biomolecular electrostatics and solvation: a computational perspective

PubMed Central

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G.; Schnieders, Michael J.; Marucho, Marcelo; Zhang, Jiajing; Baker, Nathan A.

2012-01-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view towards describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g., solvent structure, polarization, ion binding, and nonpolar behavior) in order to provide a background to understand the different types of solvation models. PMID:23217364

Biomolecular electrostatics and solvation: a computational perspective.

PubMed

Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G; Schnieders, Michael J; Marucho, Marcelo; Zhang, Jiajing; Baker, Nathan A

2012-11-01

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

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.

Distance geometry protocol to generate conformations of natural products to structurally interpret ion mobility-mass spectrometry collision cross sections.

PubMed

Stow, Sarah M; Goodwin, Cody R; Kliman, Michal; Bachmann, Brian O; McLean, John A; Lybrand, Terry P

2014-12-04

Ion mobility-mass spectrometry (IM-MS) allows the separation of ionized molecules based on their charge-to-surface area (IM) and mass-to-charge ratio (MS), respectively. The IM drift time data that is obtained is used to calculate the ion-neutral collision cross section (CCS) of the ionized molecule with the neutral drift gas, which is directly related to the ion conformation and hence molecular size and shape. Studying the conformational landscape of these ionized molecules computationally provides interpretation to delineate the potential structures that these CCS values could represent, or conversely, structural motifs not consistent with the IM data. A challenge in the IM-MS community is the ability to rapidly compute conformations to interpret natural product data, a class of molecules exhibiting a broad range of biological activity. The diversity of biological activity is, in part, related to the unique structural characteristics often observed for natural products. Contemporary approaches to structurally interpret IM-MS data for peptides and proteins typically utilize molecular dynamics (MD) simulations to sample conformational space. However, MD calculations are computationally expensive, they require a force field that accurately describes the molecule of interest, and there is no simple metric that indicates when sufficient conformational sampling has been achieved. Distance geometry is a computationally inexpensive approach that creates conformations based on sampling different pairwise distances between the atoms within the molecule and therefore does not require a force field. Progressively larger distance bounds can be used in distance geometry calculations, providing in principle a strategy to assess when all plausible conformations have been sampled. Our results suggest that distance geometry is a computationally efficient and potentially superior strategy for conformational analysis of natural products to interpret gas-phase CCS data.

Distance Geometry Protocol to Generate Conformations of Natural Products to Structurally Interpret Ion Mobility-Mass Spectrometry Collision Cross Sections

PubMed Central

2015-01-01

Ion mobility-mass spectrometry (IM-MS) allows the separation of ionized molecules based on their charge-to-surface area (IM) and mass-to-charge ratio (MS), respectively. The IM drift time data that is obtained is used to calculate the ion-neutral collision cross section (CCS) of the ionized molecule with the neutral drift gas, which is directly related to the ion conformation and hence molecular size and shape. Studying the conformational landscape of these ionized molecules computationally provides interpretation to delineate the potential structures that these CCS values could represent, or conversely, structural motifs not consistent with the IM data. A challenge in the IM-MS community is the ability to rapidly compute conformations to interpret natural product data, a class of molecules exhibiting a broad range of biological activity. The diversity of biological activity is, in part, related to the unique structural characteristics often observed for natural products. Contemporary approaches to structurally interpret IM-MS data for peptides and proteins typically utilize molecular dynamics (MD) simulations to sample conformational space. However, MD calculations are computationally expensive, they require a force field that accurately describes the molecule of interest, and there is no simple metric that indicates when sufficient conformational sampling has been achieved. Distance geometry is a computationally inexpensive approach that creates conformations based on sampling different pairwise distances between the atoms within the molecule and therefore does not require a force field. Progressively larger distance bounds can be used in distance geometry calculations, providing in principle a strategy to assess when all plausible conformations have been sampled. Our results suggest that distance geometry is a computationally efficient and potentially superior strategy for conformational analysis of natural products to interpret gas-phase CCS data. PMID:25360896

Using argument notation to engineer biological simulations with increased confidence

PubMed Central

Alden, Kieran; Andrews, Paul S.; Polack, Fiona A. C.; Veiga-Fernandes, Henrique; Coles, Mark C.; Timmis, Jon

2015-01-01

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions. PMID:25589574

Using argument notation to engineer biological simulations with increased confidence.

PubMed

Alden, Kieran; Andrews, Paul S; Polack, Fiona A C; Veiga-Fernandes, Henrique; Coles, Mark C; Timmis, Jon

2015-03-06

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions.

Model structure identification for wastewater treatment simulation based on computational fluid dynamics.

PubMed

Alex, J; Kolisch, G; Krause, K

2002-01-01

The objective of this presented project is to use the results of an CFD simulation to automatically, systematically and reliably generate an appropriate model structure for simulation of the biological processes using CSTR activated sludge compartments. Models and dynamic simulation have become important tools for research but also increasingly for the design and optimisation of wastewater treatment plants. Besides the biological models several cases are reported about the application of computational fluid dynamics ICFD) to wastewater treatment plants. One aim of the presented method to derive model structures from CFD results is to exclude the influence of empirical structure selection to the result of dynamic simulations studies of WWTPs. The second application of the approach developed is the analysis of badly performing treatment plants where the suspicion arises that bad flow behaviour such as short cut flows is part of the problem. The method suggested requires as the first step the calculation of fluid dynamics of the biological treatment step at different loading situations by use of 3-dimensional CFD simulation. The result of this information is used to generate a suitable model structure for conventional dynamic simulation of the treatment plant by use of a number of CSTR modules with a pattern of exchange flows between the tanks automatically. The method is explained in detail and the application to the WWTP Wuppertal Buchenhofen is presented.

Review of the fundamental theories behind small angle X-ray scattering, molecular dynamics simulations, and relevant integrated application.

PubMed

Boldon, Lauren; Laliberte, Fallon; Liu, Li

2015-01-01

In this paper, the fundamental concepts and equations necessary for performing small angle X-ray scattering (SAXS) experiments, molecular dynamics (MD) simulations, and MD-SAXS analyses were reviewed. Furthermore, several key biological and non-biological applications for SAXS, MD, and MD-SAXS are presented in this review; however, this article does not cover all possible applications. SAXS is an experimental technique used for the analysis of a wide variety of biological and non-biological structures. SAXS utilizes spherical averaging to produce one- or two-dimensional intensity profiles, from which structural data may be extracted. MD simulation is a computer simulation technique that is used to model complex biological and non-biological systems at the atomic level. MD simulations apply classical Newtonian mechanics' equations of motion to perform force calculations and to predict the theoretical physical properties of the system. This review presents several applications that highlight the ability of both SAXS and MD to study protein folding and function in addition to non-biological applications, such as the study of mechanical, electrical, and structural properties of non-biological nanoparticles. Lastly, the potential benefits of combining SAXS and MD simulations for the study of both biological and non-biological systems are demonstrated through the presentation of several examples that combine the two techniques.

Cyclobutane-Containing Alkaloids: Origin, Synthesis, and Biological Activities

PubMed Central

Sergeiko, Anastasia; Poroikov, Vladimir V; Hanuš, Lumir O; Dembitsky, Valery M

2008-01-01

Present review describes research on novel natural cyclobutane-containing alkaloids isolated from terrestrial and marine species. More than 60 biological active compounds have been confirmed to have antimicrobial, antibacterial, antitumor, and other activities. The structures, synthesis, origins, and biological activities of a selection of cyclobutane-containing alkaloids are reviewed. With the computer program PASS some additional biological activities are also predicted, which point toward new possible applications of these compounds. This review emphasizes the role of cyclobutane-containing alkaloids as an important source of leads for drug discovery. PMID:19696873

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

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

The world as viewed by and with unpaired electrons.

PubMed

Eaton, Sandra S; Eaton, Gareth R

2012-10-01

Recent advances in electron paramagnetic resonance (EPR) include capabilities for applications to areas as diverse as archeology, beer shelf life, biological structure, dosimetry, in vivo imaging, molecular magnets, and quantum computing. Enabling technologies include multifrequency continuous wave, pulsed, and rapid scan EPR. Interpretation is enhanced by increasingly powerful computational models. Copyright © 2012 Elsevier Inc. All rights reserved.

A Self-Assisting Protein Folding Model for Teaching Structural Molecular Biology.

PubMed

Davenport, Jodi; Pique, Michael; Getzoff, Elizabeth; Huntoon, Jon; Gardner, Adam; Olson, Arthur

2017-04-04

Structural molecular biology is now becoming part of high school science curriculum thus posing a challenge for teachers who need to convey three-dimensional (3D) structures with conventional text and pictures. In many cases even interactive computer graphics does not go far enough to address these challenges. We have developed a flexible model of the polypeptide backbone using 3D printing technology. With this model we have produced a polypeptide assembly kit to create an idealized model of the Triosephosphate isomerase mutase enzyme (TIM), which forms a structure known as TIM barrel. This kit has been used in a laboratory practical where students perform a step-by-step investigation into the nature of protein folding, starting with the handedness of amino acids to the formation of secondary and tertiary structure. Based on the classroom evidence we collected, we conclude that these models are valuable and inexpensive resource for teaching structural molecular biology. Copyright © 2017 Elsevier Ltd. All rights reserved.

The discovery of structural form

PubMed Central

Kemp, Charles; Tenenbaum, Joshua B.

2008-01-01

Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., “bigger than” or “better than”) respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development. PMID:18669663

IBIS integrated biological imaging system: electron micrograph image-processing software running on Unix workstations.

PubMed

Flifla, M J; Garreau, M; Rolland, J P; Coatrieux, J L; Thomas, D

1992-12-01

'IBIS' is a set of computer programs concerned with the processing of electron micrographs, with particular emphasis on the requirements for structural analyses of biological macromolecules. The software is written in FORTRAN 77 and runs on Unix workstations. A description of the various functions and the implementation mode is given. Some examples illustrate the user interface.

Smiles2Monomers: a link between chemical and biological structures for polymers.

PubMed

Dufresne, Yoann; Noé, Laurent; Leclère, Valérie; Pupin, Maude

2015-01-01

The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure. Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer. After, a greedy algorithm combines the mapped monomers into a consistent monomeric structure. Finally, a local branch and cut algorithm refines the structure. We tested this method on two manually annotated databases of polymers and reconstructed the structures de novo with a sensitivity over 90 %. The average computation time per polymer is 2 s. s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo.lifl.fr/norine/smiles2monomers.jsp.Graphical abstract:.

Biocatalysis and biomimetics

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

Burrington, J.D.; Clark, D.S.

1989-01-01

This book presents recent advances in catalytic science and biotechnology. The chapters illustrate how many of the key challenges in biotechnology can be addressed by bringing together traditionally separate disciplines within chemistry and biology. The authors focus on emerging enabling technologies at the interfaces of catalysis and biology that will provide new opportunities for the chemicals industries. Key aspects to be presented within this major theme of catalysis and biotechnology are biomimetics and hybrid catalysts, biocatalytic applications of computers and expert systems, enzyme solid-state structure and immobilization, enzyme structure-activity relationships, and the use of enzymes under novel conditions.

An agent-based computational model for tuberculosis spreading on age-structured populations

NASA Astrophysics Data System (ADS)

Graciani Rodrigues, C. C.; Espíndola, Aquino L.; Penna, T. J. P.

2015-06-01

In this work we present an agent-based computational model to study the spreading of the tuberculosis (TB) disease on age-structured populations. The model proposed is a merge of two previous models: an agent-based computational model for the spreading of tuberculosis and a bit-string model for biological aging. The combination of TB with the population aging, reproduces the coexistence of health states, as seen in real populations. In addition, the universal exponential behavior of mortalities curves is still preserved. Finally, the population distribution as function of age shows the prevalence of TB mostly in elders, for high efficacy treatments.

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.

PDB_REDO: automated re-refinement of X-ray structure models in the PDB.

PubMed

Joosten, Robbie P; Salzemann, Jean; Bloch, Vincent; Stockinger, Heinz; Berglund, Ann-Charlott; Blanchet, Christophe; Bongcam-Rudloff, Erik; Combet, Christophe; Da Costa, Ana L; Deleage, Gilbert; Diarena, Matteo; Fabbretti, Roberto; Fettahi, Géraldine; Flegel, Volker; Gisel, Andreas; Kasam, Vinod; Kervinen, Timo; Korpelainen, Eija; Mattila, Kimmo; Pagni, Marco; Reichstadt, Matthieu; Breton, Vincent; Tickle, Ian J; Vriend, Gert

2009-06-01

Structural biology, homology modelling and rational drug design require accurate three-dimensional macromolecular coordinates. However, the coordinates in the Protein Data Bank (PDB) have not all been obtained using the latest experimental and computational methods. In this study a method is presented for automated re-refinement of existing structure models in the PDB. A large-scale benchmark with 16 807 PDB entries showed that they can be improved in terms of fit to the deposited experimental X-ray data as well as in terms of geometric quality. The re-refinement protocol uses TLS models to describe concerted atom movement. The resulting structure models are made available through the PDB_REDO databank (http://www.cmbi.ru.nl/pdb_redo/). Grid computing techniques were used to overcome the computational requirements of this endeavour.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

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

A Molecular–Structure Hypothesis

PubMed Central

Boeyens, Jan C. A.

2010-01-01

The self-similar symmetry that occurs between atomic nuclei, biological growth structures, the solar system, globular clusters and spiral galaxies suggests that a similar pattern should characterize atomic and molecular structures. This possibility is explored in terms of the current molecular structure-hypothesis and its extension into four-dimensional space-time. It is concluded that a quantum molecule only has structure in four dimensions and that classical (Newtonian) structure, which occurs in three dimensions, cannot be simulated by quantum-chemical computation. PMID:21151437

An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures

PubMed Central

Zandi, Kasra; Butler, Gregory; Kharma, Nawwaf

2016-01-01

Computational design of RNA sequences that fold into targeted secondary structures has many applications in biomedicine, nanotechnology and synthetic biology. An RNA molecule is made of different types of secondary structure elements and an important RNA element named pseudoknot plays a key role in stabilizing the functional form of the molecule. However, due to the computational complexities associated with characterizing pseudoknotted RNA structures, most of the existing RNA sequence designer algorithms generally ignore this important structural element and therefore limit their applications. In this paper we present a new algorithm to design RNA sequences for pseudoknotted secondary structures. We use NUPACK as the folding algorithm to compute the equilibrium characteristics of the pseudoknotted RNAs, and describe a new adaptive defect weighted sampling algorithm named Enzymer to design low ensemble defect RNA sequences for targeted secondary structures including pseudoknots. We used a biological data set of 201 pseudoknotted structures from the Pseudobase library to benchmark the performance of our algorithm. We compared the quality characteristics of the RNA sequences we designed by Enzymer with the results obtained from the state of the art MODENA and antaRNA. Our results show our method succeeds more frequently than MODENA and antaRNA do, and generates sequences that have lower ensemble defect, lower probability defect and higher thermostability. Finally by using Enzymer and by constraining the design to a naturally occurring and highly conserved Hammerhead motif, we designed 8 sequences for a pseudoknotted cis-acting Hammerhead ribozyme. Enzymer is available for download at https://bitbucket.org/casraz/enzymer. PMID:27499762

Investigation of innovative synthesis of biologically active compounds on the basis of newly developed reactions.

PubMed

Honda, Toshio

2012-01-01

Synthesis of biologically active compounds, including natural products and pharmaceutical agents, is an important and interesting research area since the large structural diversity and complexity of bioactive compounds make them an important source of leads and scaffolds in drug discovery and development. Many structurally and also biologically interesting compounds, including marine natural products, have been isolated from nature and have also been prepared on the basis of a computational design for the purpose of developing medicinal chemistry. In order to obtain a wide variety of derivatives of biologically active compounds from the viewpoint of medicinal chemistry, it is essential to establish efficient synthetic procedures for desired targets. Newly developed reactions should also be used for efficient synthesis of desired compounds. Thus, recent progress in the synthesis of biologically active compounds by focusing on the development of new reactions is summarized in this review article.

Predicting protein structures with a multiplayer online game.

PubMed

Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit

2010-08-05

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.

Structural Biology of Tumor Necrosis Factor Demonstrated for Undergraduates Instruction by Computer Simulation

ERIC Educational Resources Information Center

Roy, Urmi

2016-01-01

This work presents a three-dimensional (3D) modeling exercise for undergraduate students in chemistry and health sciences disciplines, focusing on a protein-group linked to immune system regulation. Specifically, the exercise involves molecular modeling and structural analysis of tumor necrosis factor (TNF) proteins, both wild type and mutant. The…

Evaluation of Software for Introducing Protein Structure: Visualization and Simulation

ERIC Educational Resources Information Center

White, Brian; Kahriman, Azmin; Luberice, Lois; Idleh, Farhia

2010-01-01

Communicating an understanding of the forces and factors that determine a protein's structure is an important goal of many biology and biochemistry courses at a variety of levels. Many educators use computer software that allows visualization of these complex molecules for this purpose. Although visualization is in wide use and has been associated…

Molecular modeling in structural nano-toxicology: interactions of nano-particles with nano-machinery of cells.

PubMed

Yanamala, Naveena; Kagan, Valerian E; Shvedova, Anna A

2013-12-01

Over the past two decades, nanotechnology has emerged as a key player in various disciplines of science and technology. Some of the most exciting applications are in the field of biomedicine - for theranostics (for combined diagnostic and therapeutic purposes) as well as for exploration of biological systems. A detailed understanding of the molecular interactions between nanoparticles and biological nano-machinery - macromolecules, membranes, and intracellular organelles - is crucial for obtaining adequate information on mechanisms of action of nanomaterials as well as a perspective on the long term effects of these materials and their possible toxicological outcomes. This review focuses on the use of structure-based computational molecular modeling as a tool to understand and to predict the interactions between nanomaterials and nano-biosystems. We review major approaches and provide examples of computational analysis of the structural principles behind such interactions. A rationale on how nanoparticles of different sizes, shape, structure and chemical properties can affect the organization and functions of nano-machinery of cells is also presented. Published by Elsevier B.V.

Sequence-structure mapping errors in the PDB: OB-fold domains

PubMed Central

Venclovas, Česlovas; Ginalski, Krzysztof; Kang, Chulhee

2004-01-01

The Protein Data Bank (PDB) is the single most important repository of structural data for proteins and other biologically relevant molecules. Therefore, it is critically important to keep the PDB data, as much as possible, error-free. In this study, we have analyzed PDB crystal structures possessing oligonucleotide/oligosaccharide binding (OB)-fold, one of the highly populated folds, for the presence of sequence-structure mapping errors. Using energy-based structure quality assessment coupled with sequence analyses, we have found that there are at least five OB-structures in the PDB that have regions where sequences have been incorrectly mapped onto the structure. We have demonstrated that the combination of these computation techniques is effective not only in detecting sequence-structure mapping errors, but also in providing guidance to correct them. Namely, we have used results of computational analysis to direct a revision of X-ray data for one of the PDB entries containing a fairly inconspicuous sequence-structure mapping error. The revised structure has been deposited with the PDB. We suggest use of computational energy assessment and sequence analysis techniques to facilitate structure determination when homologs having known structure are available to use as a reference. Such computational analysis may be useful in either guiding the sequence-structure assignment process or verifying the sequence mapping within poorly defined regions. PMID:15133161

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.

Molecular mechanics and dynamics characterization of an in silico mutated protein: a stand-alone lab module or support activity for in vivo and in vitro analyses of targeted proteins.

PubMed

Chiang, Harry; Robinson, Lucy C; Brame, Cynthia J; Messina, Troy C

2013-01-01

Over the past 20 years, the biological sciences have increasingly incorporated chemistry, physics, computer science, and mathematics to aid in the development and use of mathematical models. Such combined approaches have been used to address problems from protein structure-function relationships to the workings of complex biological systems. Computer simulations of molecular events can now be accomplished quickly and with standard computer technology. Also, simulation software is freely available for most computing platforms, and online support for the novice user is ample. We have therefore created a molecular dynamics laboratory module to enhance undergraduate student understanding of molecular events underlying organismal phenotype. This module builds on a previously described project in which students use site-directed mutagenesis to investigate functions of conserved sequence features in members of a eukaryotic protein kinase family. In this report, we detail the laboratory activities of a MD module that provide a complement to phenotypic outcomes by providing a hypothesis-driven and quantifiable measure of predicted structural changes caused by targeted mutations. We also present examples of analyses students may perform. These laboratory activities can be integrated with genetics or biochemistry experiments as described, but could also be used independently in any course that would benefit from a quantitative approach to protein structure-function relationships. Copyright © 2013 Wiley Periodicals, Inc.

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

PubMed

Ligon, Thomas S; Fröhlich, Fabian; Chis, Oana T; Banga, Julio R; Balsa-Canto, Eva; Hasenauer, Jan

2018-04-15

Mathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed. We introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models. GenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI. thomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.de. Supplementary data are available at Bioinformatics online.

First-principles modeling of biological systems and structure-based drug-design.

PubMed

Sgrignani, Jacopo; Magistrato, Alessandra

2013-03-01

Molecular modeling techniques play a relevant role in drug design providing detailed information at atomistic level on the structural, dynamical, mechanistic and electronic properties of biological systems involved in diseases' onset, integrating and supporting commonly used experimental approaches. These information are often not accessible to the experimental techniques taken singularly, but are of crucial importance for drug design. Due to the enormous increase of the computer power in the last decades, quantum mechanical (QM) or first-principles-based methods have become often used to address biological issues of pharmaceutical relevance, providing relevant information for drug design. Due to their complexity and their size, biological systems are often investigated by means of a mixed quantum-classical (QM/MM) approach, which treats at an accurate QM level a limited chemically relevant portion of the system and at the molecular mechanics (MM) level the remaining of the biomolecule and its environment. This method provides a good compromise between computational cost and accuracy, allowing to characterize the properties of the biological system and the (free) energy landscape of the process in study with the accuracy of a QM description. In this review, after a brief introduction of QM and QM/MM methods, we will discuss few representative examples, taken from our work, of the application of these methods in the study of metallo-enzymes of pharmaceutical interest, of metal-containing anticancer drugs targeting the DNA as well as of neurodegenerative diseases. The information obtained from these studies may provide the basis for a rationale structure-based drug design of new and more efficient inhibitors or drugs.

Review of the fundamental theories behind small angle X-ray scattering, molecular dynamics simulations, and relevant integrated application

PubMed Central

Boldon, Lauren; Laliberte, Fallon; Liu, Li

2015-01-01

In this paper, the fundamental concepts and equations necessary for performing small angle X-ray scattering (SAXS) experiments, molecular dynamics (MD) simulations, and MD-SAXS analyses were reviewed. Furthermore, several key biological and non-biological applications for SAXS, MD, and MD-SAXS are presented in this review; however, this article does not cover all possible applications. SAXS is an experimental technique used for the analysis of a wide variety of biological and non-biological structures. SAXS utilizes spherical averaging to produce one- or two-dimensional intensity profiles, from which structural data may be extracted. MD simulation is a computer simulation technique that is used to model complex biological and non-biological systems at the atomic level. MD simulations apply classical Newtonian mechanics’ equations of motion to perform force calculations and to predict the theoretical physical properties of the system. This review presents several applications that highlight the ability of both SAXS and MD to study protein folding and function in addition to non-biological applications, such as the study of mechanical, electrical, and structural properties of non-biological nanoparticles. Lastly, the potential benefits of combining SAXS and MD simulations for the study of both biological and non-biological systems are demonstrated through the presentation of several examples that combine the two techniques. PMID:25721341

The Role of Protein Loops and Linkers in Conformational Dynamics and Allostery.

PubMed

Papaleo, Elena; Saladino, Giorgio; Lambrughi, Matteo; Lindorff-Larsen, Kresten; Gervasio, Francesco Luigi; Nussinov, Ruth

2016-06-08

Proteins are dynamic entities that undergo a plethora of conformational changes that may take place on a wide range of time scales. These changes can be as small as the rotation of one or a few side-chain dihedral angles or involve concerted motions in larger portions of the three-dimensional structure; both kinds of motions can be important for biological function and allostery. It is becoming increasingly evident that "connector regions" are important components of the dynamic personality of protein structures. These regions may be either disordered loops, i.e., poorly structured regions connecting secondary structural elements, or linkers that connect entire protein domains. Experimental and computational studies have, however, revealed that these regions are not mere connectors, and their role in allostery and conformational changes has been emerging in the last few decades. Here we provide a detailed overview of the structural properties and classification of loops and linkers, as well as a discussion of the main computational methods employed to investigate their function and dynamical properties. We also describe their importance for protein dynamics and allostery using as examples key proteins in cellular biology and human diseases such as kinases, ubiquitinating enzymes, and transcription factors.

Structural computational modeling of RNA aptamers.

PubMed

Xu, Xiaojun; Dickey, David D; Chen, Shi-Jie; Giangrande, Paloma H

2016-07-01

RNA aptamers represent an emerging class of biologics that can be easily adapted for personalized and precision medicine. Several therapeutic aptamers with desirable binding and functional properties have been developed and evaluated in preclinical studies over the past 25years. However, for the majority of these aptamers, their clinical potential has yet to be realized. A significant hurdle to the clinical adoption of this novel class of biologicals is the limited information on their secondary and tertiary structure. Knowledge of the RNA's structure would greatly facilitate and expedite the post-selection optimization steps required for translation, including truncation (to reduce costs of manufacturing), chemical modification (to enhance stability and improve safety) and chemical conjugation (to improve drug properties for combinatorial therapy). Here we describe a structural computational modeling methodology that when coupled to a standard functional assay, can be used to determine key sequence and structural motifs of an RNA aptamer. We applied this methodology to enable the truncation of an aptamer to prostate specific membrane antigen (PSMA) with great potential for targeted therapy that had failed previous truncation attempts. This methodology can be easily applied to optimize other aptamers with therapeutic potential. Copyright © 2016. Published by Elsevier Inc.

Energy and Technology Review

NASA Astrophysics Data System (ADS)

Poggio, Andrew J.

1988-10-01

This issue of Energy and Technology Review contains: Neutron Penumbral Imaging of Laser-Fusion Targets--using our new penumbral-imaging diagnostic, we have obtained the first images that can be used to measure directly the deuterium-tritium burn region in laser-driven fusion targets; Computed Tomography for Nondestructive Evaluation--various computed tomography systems and computational techniques are used in nondestructive evaluation; Three-Dimensional Image Analysis for Studying Nuclear Chromatin Structure--we have developed an optic-electronic system for acquiring cross-sectional views of cell nuclei, and computer codes to analyze these images and reconstruct the three-dimensional structures they represent; Imaging in the Nuclear Test Program--advanced techniques produce images of unprecedented detail and resolution from Nevada Test Site data; and Computational X-Ray Holography--visible-light experiments and numerically simulated holograms test our ideas about an X-ray microscope for biological research.

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.

Toward a structure determination method for biomineral-associated protein using combined solid- state NMR and computational structure prediction.

PubMed

Masica, David L; Ash, Jason T; Ndao, Moise; Drobny, Gary P; Gray, Jeffrey J

2010-12-08

Protein-biomineral interactions are paramount to materials production in biology, including the mineral phase of hard tissue. Unfortunately, the structure of biomineral-associated proteins cannot be determined by X-ray crystallography or solution nuclear magnetic resonance (NMR). Here we report a method for determining the structure of biomineral-associated proteins. The method combines solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. In addition, the algorithm is able to identify lattice geometries most compatible with ssNMR constraints, representing a quantitative, novel method for investigating crystal-face binding specificity. We use this method to determine most of the structure of human salivary statherin interacting with the mineral phase of tooth enamel. Computation and experiment converge on an ensemble of related structures and identify preferential binding at three crystal surfaces. The work represents a significant advance toward determining structure of biomineral-adsorbed protein using experimentally biased structure prediction. This method is generally applicable to proteins that can be chemically synthesized. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

Computational dynamic approaches for temporal omics data with applications to systems medicine.

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Computer Aided Drug Design: Success and Limitations.

PubMed

Baig, Mohammad Hassan; Ahmad, Khurshid; Roy, Sudeep; Ashraf, Jalaluddin Mohammad; Adil, Mohd; Siddiqui, Mohammad Haris; Khan, Saif; Kamal, Mohammad Amjad; Provazník, Ivo; Choi, Inho

2016-01-01

Over the last few decades, computer-aided drug design has emerged as a powerful technique playing a crucial role in the development of new drug molecules. Structure-based drug design and ligand-based drug design are two methods commonly used in computer-aided drug design. In this article, we discuss the theory behind both methods, as well as their successful applications and limitations. To accomplish this, we reviewed structure based and ligand based virtual screening processes. Molecular dynamics simulation, which has become one of the most influential tool for prediction of the conformation of small molecules and changes in their conformation within the biological target, has also been taken into account. Finally, we discuss the principles and concepts of molecular docking, pharmacophores and other methods used in computer-aided drug design.

BIOCOMPUTATION: some history and prospects.

PubMed

Cull, Paul

2013-06-01

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

An Introduction to Biological NMR Spectroscopy*

PubMed Central

Marion, Dominique

2013-01-01

NMR spectroscopy is a powerful tool for biologists interested in the structure, dynamics, and interactions of biological macromolecules. This review aims at presenting in an accessible manner the requirements and limitations of this technique. As an introduction, the history of NMR will highlight how the method evolved from physics to chemistry and finally to biology over several decades. We then introduce the NMR spectral parameters used in structural biology, namely the chemical shift, the J-coupling, nuclear Overhauser effects, and residual dipolar couplings. Resonance assignment, the required step for any further NMR study, bears a resemblance to jigsaw puzzle strategy. The NMR spectral parameters are then converted into angle and distances and used as input using restrained molecular dynamics to compute a bundle of structures. When interpreting a NMR-derived structure, the biologist has to judge its quality on the basis of the statistics provided. When the 3D structure is a priori known by other means, the molecular interaction with a partner can be mapped by NMR: information on the binding interface as well as on kinetic and thermodynamic constants can be gathered. NMR is suitable to monitor, over a wide range of frequencies, protein fluctuations that play a crucial role in their biological function. In the last section of this review, intrinsically disordered proteins, which have escaped the attention of classical structural biology, are discussed in the perspective of NMR, one of the rare available techniques able to describe structural ensembles. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 16 MCP). PMID:23831612

Enhancement of DFT-calculations at petascale: Nuclear Magnetic Resonance, Hybrid Density Functional Theory and Car-Parrinello calculations

NASA Astrophysics Data System (ADS)

Varini, Nicola; Ceresoli, Davide; Martin-Samos, Layla; Girotto, Ivan; Cavazzoni, Carlo

2013-08-01

One of the most promising techniques used for studying the electronic properties of materials is based on Density Functional Theory (DFT) approach and its extensions. DFT has been widely applied in traditional solid state physics problems where periodicity and symmetry play a crucial role in reducing the computational workload. With growing compute power capability and the development of improved DFT methods, the range of potential applications is now including other scientific areas such as Chemistry and Biology. However, cross disciplinary combinations of traditional Solid-State Physics, Chemistry and Biology drastically improve the system complexity while reducing the degree of periodicity and symmetry. Large simulation cells containing of hundreds or even thousands of atoms are needed to model these kind of physical systems. The treatment of those systems still remains a computational challenge even with modern supercomputers. In this paper we describe our work to improve the scalability of Quantum ESPRESSO (Giannozzi et al., 2009 [3]) for treating very large cells and huge numbers of electrons. To this end we have introduced an extra level of parallelism, over electronic bands, in three kernels for solving computationally expensive problems: the Sternheimer equation solver (Nuclear Magnetic Resonance, package QE-GIPAW), the Fock operator builder (electronic ground-state, package PWscf) and most of the Car-Parrinello routines (Car-Parrinello dynamics, package CP). Final benchmarks show our success in computing the Nuclear Magnetic Response (NMR) chemical shift of a large biological assembly, the electronic structure of defected amorphous silica with hybrid exchange-correlation functionals and the equilibrium atomic structure of height Porphyrins anchored to a Carbon Nanotube, on many thousands of CPU cores.

In Silico Analysis for the Study of Botulinum Toxin Structure

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2010-01-01

Protein-protein interactions play many important roles in biological function. Knowledge of protein-protein complex structure is required for understanding the function. The determination of protein-protein complex structure by experimental studies remains difficult, therefore computational prediction of protein structures by structure modeling and docking studies is valuable method. In addition, MD simulation is also one of the most popular methods for protein structure modeling and characteristics. Here, we attempt to predict protein-protein complex structure and property using some of bioinformatic methods, and we focus botulinum toxin complex as target structure.

Molecular Mechanics and Dynamics Characterization of an "in silico" Mutated Protein: A Stand-Alone Lab Module or Support Activity for "in vivo" and "in vitro" Analyses of Targeted Proteins

ERIC Educational Resources Information Center

Chiang, Harry; Robinson, Lucy C.; Brame, Cynthia J.; Messina, Troy C.

2013-01-01

Over the past 20 years, the biological sciences have increasingly incorporated chemistry, physics, computer science, and mathematics to aid in the development and use of mathematical models. Such combined approaches have been used to address problems from protein structure-function relationships to the workings of complex biological systems.…

Diffraction Techniques in Structural Biology

PubMed Central

Egli, Martin

2016-01-01

A detailed understanding of chemical and biological function and the mechanisms underlying the molecular activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy, and neutron diffraction are well established and they have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last 20 years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches, and high-speed computing and visualization, now provide specialists and nonspecialists alike with a steady flow of molecular images of unprecedented detail. The present unit combines a general overview of diffraction methods with a detailed description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis, and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. PMID:27248784

Diffraction Techniques in Structural Biology

PubMed Central

Egli, Martin

2010-01-01

A detailed understanding of chemical and biological function and the mechanisms underlying the activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy and neutron diffraction are well established and have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last 20 years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches and high-speed computing and visualization, now provide specialists and non-specialists alike with a steady flow of molecular images of unprecedented detail. The present chapter combines a general overview of diffraction methods with a step-by-step description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. PMID:20517991

Diffraction Techniques in Structural Biology.

PubMed

Egli, Martin

2016-06-01

A detailed understanding of chemical and biological function and the mechanisms underlying the molecular activities ultimately requires atomic-resolution structural data. Diffraction-based techniques such as single-crystal X-ray crystallography, electron microscopy, and neutron diffraction are well established and they have paved the road to the stunning successes of modern-day structural biology. The major advances achieved in the last twenty years in all aspects of structural research, including sample preparation, crystallization, the construction of synchrotron and spallation sources, phasing approaches, and high-speed computing and visualization, now provide specialists and nonspecialists alike with a steady flow of molecular images of unprecedented detail. The present unit combines a general overview of diffraction methods with a detailed description of the process of a single-crystal X-ray structure determination experiment, from chemical synthesis or expression to phasing and refinement, analysis, and quality control. For novices it may serve as a stepping-stone to more in-depth treatises of the individual topics. Readers relying on structural information for interpreting functional data may find it a useful consumer guide. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Designing synthetic RNAs to determine the relevance of structural motifs in picornavirus IRES elements

NASA Astrophysics Data System (ADS)

Fernandez-Chamorro, Javier; Lozano, Gloria; Garcia-Martin, Juan Antonio; Ramajo, Jorge; Dotu, Ivan; Clote, Peter; Martinez-Salas, Encarnacion

2016-04-01

The function of Internal Ribosome Entry Site (IRES) elements is intimately linked to their RNA structure. Viral IRES elements are organized in modular domains consisting of one or more stem-loops that harbor conserved RNA motifs critical for internal initiation of translation. A conserved motif is the pyrimidine-tract located upstream of the functional initiation codon in type I and II picornavirus IRES. By computationally designing synthetic RNAs to fold into a structure that sequesters the polypyrimidine tract in a hairpin, we establish a correlation between predicted inaccessibility of the pyrimidine tract and IRES activity, as determined in both in vitro and in vivo systems. Our data supports the hypothesis that structural sequestration of the pyrimidine-tract within a stable hairpin inactivates IRES activity, since the stronger the stability of the hairpin the higher the inhibition of protein synthesis. Destabilization of the stem-loop immediately upstream of the pyrimidine-tract also decreases IRES activity. Our work introduces a hybrid computational/experimental method to determine the importance of structural motifs for biological function. Specifically, we show the feasibility of using the software RNAiFold to design synthetic RNAs with particular sequence and structural motifs that permit subsequent experimental determination of the importance of such motifs for biological function.

Computational protein design: a review

NASA Astrophysics Data System (ADS)

Coluzza, Ivan

2017-04-01

Proteins are one of the most versatile modular assembling systems in nature. Experimentally, more than 110 000 protein structures have been identified and more are deposited every day in the Protein Data Bank. Such an enormous structural variety is to a first approximation controlled by the sequence of amino acids along the peptide chain of each protein. Understanding how the structural and functional properties of the target can be encoded in this sequence is the main objective of protein design. Unfortunately, rational protein design remains one of the major challenges across the disciplines of biology, physics and chemistry. The implications of solving this problem are enormous and branch into materials science, drug design, evolution and even cryptography. For instance, in the field of drug design an effective computational method to design protein-based ligands for biological targets such as viruses, bacteria or tumour cells, could give a significant boost to the development of new therapies with reduced side effects. In materials science, self-assembly is a highly desired property and soon artificial proteins could represent a new class of designable self-assembling materials. The scope of this review is to describe the state of the art in computational protein design methods and give the reader an outline of what developments could be expected in the near future.

To boldly glow ... applications of laser scanning confocal microscopy in developmental biology.

PubMed

Paddock, S W

1994-05-01

The laser scanning confocal microscope (LSCM) is now established as an invaluable tool in developmental biology for improved light microscope imaging of fluorescently labelled eggs, embryos and developing tissues. The universal application of the LSCM in biomedical research has stimulated improvements to the microscopes themselves and the synthesis of novel probes for imaging biological structures and physiological processes. Moreover the ability of the LSCM to produce an optical series in perfect register has made computer 3-D reconstruction and analysis of light microscope images a practical option.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: from big data to big analytical tools.

PubMed

Liu, Zhandong; Zheng, W Jim; Allen, Genevera I; Liu, Yin; Ruan, Jianhua; Zhao, Zhongming

2017-10-03

The 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) was held on December 8-10, 2016 in Houston, Texas, USA. ICIBM included eight scientific sessions, four tutorials, one poster session, four highlighted talks and four keynotes that covered topics on 3D genomics structural analysis, next generation sequencing (NGS) analysis, computational drug discovery, medical informatics, cancer genomics, and systems biology. Here, we present a summary of the nine research articles selected from ICIBM 2016 program for publishing in BMC Bioinformatics.

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

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.

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

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

PubMed Central

Dührkop, Kai; Shen, Huibin; Meusel, Marvin; Rousu, Juho; Böcker, Sebastian

2015-01-01

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin. PMID:26392543

Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

PubMed Central

Fischer, Axel W.; Bordignon, Enrica; Bleicken, Stephanie; García-Sáez, Ana J.; Jeschke, Gunnar; Meiler, Jens

2016-01-01

Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively. PMID:27129417

Biological knowledge bases using Wikis: combining the flexibility of Wikis with the structure of databases.

PubMed

Brohée, Sylvain; Barriot, Roland; Moreau, Yves

2010-09-01

In recent years, the number of knowledge bases developed using Wiki technology has exploded. Unfortunately, next to their numerous advantages, classical Wikis present a critical limitation: the invaluable knowledge they gather is represented as free text, which hinders their computational exploitation. This is in sharp contrast with the current practice for biological databases where the data is made available in a structured way. Here, we present WikiOpener an extension for the classical MediaWiki engine that augments Wiki pages by allowing on-the-fly querying and formatting resources external to the Wiki. Those resources may provide data extracted from databases or DAS tracks, or even results returned by local or remote bioinformatics analysis tools. This also implies that structured data can be edited via dedicated forms. Hence, this generic resource combines the structure of biological databases with the flexibility of collaborative Wikis. The source code and its documentation are freely available on the MediaWiki website: http://www.mediawiki.org/wiki/Extension:WikiOpener.

Parallel workflow manager for non-parallel bioinformatic applications to solve large-scale biological problems on a supercomputer.

PubMed

Suplatov, Dmitry; Popova, Nina; Zhumatiy, Sergey; Voevodin, Vladimir; Švedas, Vytas

2016-04-01

Rapid expansion of online resources providing access to genomic, structural, and functional information associated with biological macromolecules opens an opportunity to gain a deeper understanding of the mechanisms of biological processes due to systematic analysis of large datasets. This, however, requires novel strategies to optimally utilize computer processing power. Some methods in bioinformatics and molecular modeling require extensive computational resources. Other algorithms have fast implementations which take at most several hours to analyze a common input on a modern desktop station, however, due to multiple invocations for a large number of subtasks the full task requires a significant computing power. Therefore, an efficient computational solution to large-scale biological problems requires both a wise parallel implementation of resource-hungry methods as well as a smart workflow to manage multiple invocations of relatively fast algorithms. In this work, a new computer software mpiWrapper has been developed to accommodate non-parallel implementations of scientific algorithms within the parallel supercomputing environment. The Message Passing Interface has been implemented to exchange information between nodes. Two specialized threads - one for task management and communication, and another for subtask execution - are invoked on each processing unit to avoid deadlock while using blocking calls to MPI. The mpiWrapper can be used to launch all conventional Linux applications without the need to modify their original source codes and supports resubmission of subtasks on node failure. We show that this approach can be used to process huge amounts of biological data efficiently by running non-parallel programs in parallel mode on a supercomputer. The C++ source code and documentation are available from http://biokinet.belozersky.msu.ru/mpiWrapper .

Design of synthetic biological logic circuits based on evolutionary algorithm.

PubMed

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

2013-08-01

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

Computer synthesis of high resolution electron micrographs

NASA Technical Reports Server (NTRS)

Nathan, R.

1976-01-01

Specimen damage, spherical aberration, low contrast and noisy sensors combine to prevent direct atomic viewing in a conventional electron microscope. The paper describes two methods for obtaining ultra-high resolution in biological specimens under the electron microscope. The first method assumes the physical limits of the electron objective lens and uses a series of dark field images of biological crystals to obtain direct information on the phases of the Fourier diffraction maxima; this information is used in an appropriate computer to synthesize a large aperture lens for a 1-A resolution. The second method assumes there is sufficient amplitude scatter from images recorded in focus which can be utilized with a sensitive densitometer and computer contrast stretching to yield fine structure image details. Cancer virus characterization is discussed as an illustrative example. Numerous photographs supplement the text.

Computational Methods to Predict Protein Interaction Partners

NASA Astrophysics Data System (ADS)

Valencia, Alfonso; Pazos, Florencio

In the new paradigm for studying biological phenomena represented by Systems Biology, cellular components are not considered in isolation but as forming complex networks of relationships. Protein interaction networks are among the first objects studied from this new point of view. Deciphering the interactome (the whole network of interactions for a given proteome) has been shown to be a very complex task. Computational techniques for detecting protein interactions have become standard tools for dealing with this problem, helping and complementing their experimental counterparts. Most of these techniques use genomic or sequence features intuitively related with protein interactions and are based on "first principles" in the sense that they do not involve training with examples. There are also other computational techniques that use other sources of information (i.e. structural information or even experimental data) or are based on training with examples.

Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

PubMed

Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.

Incorporating Modeling and Simulations in Undergraduate Biophysical Chemistry Course to Promote Understanding of Structure-Dynamics-Function Relationships in Proteins

ERIC Educational Resources Information Center

Hati, Sanchita; Bhattacharyya, Sudeep

2016-01-01

A project-based biophysical chemistry laboratory course, which is offered to the biochemistry and molecular biology majors in their senior year, is described. In this course, the classroom study of the structure-function of biomolecules is integrated with the discovery-guided laboratory study of these molecules using computer modeling and…

Biology Notes.

ERIC Educational Resources Information Center

School Science Review, 1981

1981-01-01

Presents a computer program for analyzing diets, a game designed to supplement a topic on insects, a technique for demonstrating the role of ethene in fruit ripening, an apparatus for removing arthropods from soil samples, activities using cichlids, and an activity on bonds stabilizing protein structure. (JN)

Computational strategies to address chromatin structure problems

NASA Astrophysics Data System (ADS)

Perišić, Ognjen; Schlick, Tamar

2016-06-01

While the genetic information is contained in double helical DNA, gene expression is a complex multilevel process that involves various functional units, from nucleosomes to fully formed chromatin fibers accompanied by a host of various chromatin binding enzymes. The chromatin fiber is a polymer composed of histone protein complexes upon which DNA wraps, like yarn upon many spools. The nature of chromatin structure has been an open question since the beginning of modern molecular biology. Many experiments have shown that the chromatin fiber is a highly dynamic entity with pronounced structural diversity that includes properties of idealized zig-zag and solenoid models, as well as other motifs. This diversity can produce a high packing ratio and thus inhibit access to a majority of the wound DNA. Despite much research, chromatin’s dynamic structure has not yet been fully described. Long stretches of chromatin fibers exhibit puzzling dynamic behavior that requires interpretation in the light of gene expression patterns in various tissue and organisms. The properties of chromatin fiber can be investigated with experimental techniques, like in vitro biochemistry, in vivo imagining, and high-throughput chromosome capture technology. Those techniques provide useful insights into the fiber’s structure and dynamics, but they are limited in resolution and scope, especially regarding compact fibers and chromosomes in the cellular milieu. Complementary but specialized modeling techniques are needed to handle large floppy polymers such as the chromatin fiber. In this review, we discuss current approaches in the chromatin structure field with an emphasis on modeling, such as molecular dynamics and coarse-grained computational approaches. Combinations of these computational techniques complement experiments and address many relevant biological problems, as we will illustrate with special focus on epigenetic modulation of chromatin structure.

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

PubMed Central

Brender, Jeffrey R.; Czajka, Jeff; Marsh, David; Gray, Felicia; Cierpicki, Tomasz; Zhang, Yang

2013-01-01

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality. PMID:24204234

PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

PubMed

Kaplan, Noam; Vaaknin, Avishay; Linial, Michal

2003-10-01

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase.

PubMed

Venko, Katja; Roy Choudhury, A; Novič, Marjana

2017-01-01

The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix-helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.

Protein Modelling: What Happened to the “Protein Structure Gap”?

PubMed Central

Schwede, Torsten

2013-01-01

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing vision in structural biology as it holds the promise to bypass part of the laborious process of experimental structure solution. Over the last two decades, a paradigm shift has occurred: starting from a situation where the “structure knowledge gap” between the huge number of protein sequences and small number of known structures has hampered the widespread use of structure-based approaches in life science research, today some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. With the scientific focus of interest moving towards larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows studying large and complex molecular machines. Computational modeling and prediction techniques are still facing a number of challenges which hamper the more widespread use by the non-expert scientist. For example, it is often difficult to convey the underlying assumptions of a computational technique, as well as the expected accuracy and structural variability of a specific model. However, these aspects are crucial to understand the limitations of a model, and to decide which interpretations and conclusions can be supported. PMID:24010712

Computational design of RNAs with complex energy landscapes.

PubMed

Höner zu Siederdissen, Christian; Hammer, Stefan; Abfalter, Ingrid; Hofacker, Ivo L; Flamm, Christoph; Stadler, Peter F

2013-12-01

RNA has become an integral building material in synthetic biology. Dominated by their secondary structures, which can be computed efficiently, RNA molecules are amenable not only to in vitro and in vivo selection, but also to rational, computation-based design. While the inverse folding problem of constructing an RNA sequence with a prescribed ground-state structure has received considerable attention for nearly two decades, there have been few efforts to design RNAs that can switch between distinct prescribed conformations. We introduce a user-friendly tool for designing RNA sequences that fold into multiple target structures. The underlying algorithm makes use of a combination of graph coloring and heuristic local optimization to find sequences whose energy landscapes are dominated by the prescribed conformations. A flexible interface allows the specification of a wide range of design goals. We demonstrate that bi- and tri-stable "switches" can be designed easily with moderate computational effort for the vast majority of compatible combinations of desired target structures. RNAdesign is freely available under the GPL-v3 license. Copyright © 2013 Wiley Periodicals, Inc.

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum

PubMed Central

Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

2015-01-01

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently ‘intelligent’ behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton—a ubiquitous cellular protein scaffold whose functions are manifold and essential to life—and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness. PMID:26478782

On the role of the plasmodial cytoskeleton in facilitating intelligent behavior in slime mold Physarum polycephalum.

PubMed

Mayne, Richard; Adamatzky, Andrew; Jones, Jeff

2015-01-01

The plasmodium of slime mold Physarum polycephalum behaves as an amorphous reaction-diffusion computing substrate and is capable of apparently 'intelligent' behavior. But how does intelligence emerge in an acellular organism? Through a range of laboratory experiments, we visualize the plasmodial cytoskeleton-a ubiquitous cellular protein scaffold whose functions are manifold and essential to life-and discuss its putative role as a network for transducing, transmitting and structuring data streams within the plasmodium. Through a range of computer modeling techniques, we demonstrate how emergent behavior, and hence computational intelligence, may occur in cytoskeletal communications networks. Specifically, we model the topology of both the actin and tubulin cytoskeletal networks and discuss how computation may occur therein. Furthermore, we present bespoke cellular automata and particle swarm models for the computational process within the cytoskeleton and observe the incidence of emergent patterns in both. Our work grants unique insight into the origins of natural intelligence; the results presented here are therefore readily transferable to the fields of natural computation, cell biology and biomedical science. We conclude by discussing how our results may alter our biological, computational and philosophical understanding of intelligence and consciousness.

Combining computational models, semantic annotations and simulation experiments in a graph database

PubMed Central

Henkel, Ron; Wolkenhauer, Olaf; Waltemath, Dagmar

2015-01-01

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/ PMID:25754863

Interactive and coordinated visualization approaches for biological data analysis.

PubMed

Cruz, António; Arrais, Joel P; Machado, Penousal

2018-03-26

The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

Integrative Approaches for Predicting in vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-term Biological Assays

PubMed Central

Low, Yen S.; Sedykh, Alexander; Rusyn, Ivan; Tropsha, Alexander

2017-01-01

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity. PMID:24805064

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).

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'.

Computational modeling of diffusion in the cerebellum.

PubMed

Marinov, Toma M; Santamaria, Fidel

2014-01-01

Diffusion is a major transport mechanism in living organisms. In the cerebellum, diffusion is responsible for the propagation of molecular signaling involved in synaptic plasticity and metabolism, both intracellularly and extracellularly. In this chapter, we present an overview of the cerebellar structure and function. We then discuss the types of diffusion processes present in the cerebellum and their biological importance. We particularly emphasize the differences between extracellular and intracellular diffusion and the presence of tortuosity and anomalous diffusion in different parts of the cerebellar cortex. We provide a mathematical introduction to diffusion and a conceptual overview of various computational modeling techniques. We discuss their scope and their limit of application. Although our focus is the cerebellum, we have aimed at presenting the biological and mathematical foundations as general as possible to be applicable to any other area in biology in which diffusion is of importance. © 2014 Elsevier Inc. All rights reserved.

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.

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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.

Structural Bioinformatics of the Interactome

PubMed Central

Petrey, Donald; Honig, Barry

2014-01-01

The last decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information, and to analyze this information so as to infer both the function of individual molecules and how they interact to modulate the behavior of biological systems. Here we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance the basic understanding of biological systems and their disregulation, and how they are being applied in drug development. PMID:24895853

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

Evolutionary and biological metaphors for engineering design

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

Jakiela, M.

1994-12-31

Since computing became generally available, there has been strong interest in using computers to assist and automate engineering design processes. Specifically, for design optimization and automation, nonlinear programming and artificial intelligence techniques have been extensively studied. New computational techniques, based upon the natural processes of evolution, adaptation, and learing, are showing promise because of their generality and robustness. This presentation will describe the use of two such techniques, genetic algorithms and classifier systems, for a variety of engineering design problems. Structural topology optimization, meshing, and general engineering optimization are shown as example applications.

Phase-contrast x-ray computed tomography for observing biological specimens and organic materials

NASA Astrophysics Data System (ADS)

Momose, Atsushi; Takeda, Tohoru; Itai, Yuji

1995-02-01

A novel three-dimensional x-ray imaging method has been developed by combining a phase-contrast x-ray imaging technique with x-ray computed tomography. This phase-contrast x-ray computed tomography (PCX-CT) provides sectional images of organic specimens that would produce absorption-contrast x-ray CT images with little contrast. Comparing PCX-CT images of rat cerebellum and cancerous rabbit liver specimens with corresponding absorption-contrast CT images shows that PCX-CT is much more sensitive to the internal structure of organic specimens.

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

PubMed

Egri-Nagy, Attila; Nehaniv, Chrystopher L

2008-01-01

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

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

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

PubMed

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

2013-01-01

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

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

PubMed Central

2013-01-01

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

Biologically Inspired, Anisoptropic Flexible Wing for Optimal Flapping Flight

DTIC Science & Technology

2013-01-31

Anisotropic Flexible Wing for Optimal Flapping Flight FA9550-07-1-0547 Sb. GRANT NUMBER Sc. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Sd. PROJECT NUMBER...anisotropic structural flexibility ; c) Conducted coordinated experimental and computational modeling to determine the roles of aerodynamic loading, wing inertia...and structural flexibility and elasticity; and d) Developed surrogate tools for flapping wing MA V design and optimization. Detailed research

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

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.

Computational structure analysis of biomacromolecule complexes by interface geometry.

PubMed

Mahdavi, Sedigheh; Salehzadeh-Yazdi, Ali; Mohades, Ali; Masoudi-Nejad, Ali

2013-12-01

The ability to analyze and compare protein-nucleic acid and protein-protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein-nucleic acid and protein-protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA-protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model based on solvent accessibility and the high validation parameters in comparison to the classical model. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Kufareva, Irina; Gustavsson, Martin; Zheng, Yi

Chemokines and their cell surface G protein–coupled receptors are critical for cell migration, not only in many fundamental biological processes but also in inflammatory diseases and cancer. Recent X-ray structures of two chemokines complexed with full-length receptors provided unprecedented insight into the atomic details of chemokine recognition and receptor activation, and computational modeling informed by new experiments leverages these insights to gain understanding of many more receptor:chemokine pairs. In parallel, chemokine receptor structures with small molecules reveal the complicated and diverse structural foundations of small molecule antagonism and allostery, highlight the inherent physicochemical challenges of receptor:chemokine interfaces, and suggest novelmore » epitopes that can be exploited to overcome these challenges. The structures and models promote unique understanding of chemokine receptor biology, including the interpretation of two decades of experimental studies, and will undoubtedly assist future drug discovery endeavors.« less

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 discovery and repurposing.

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

PubMed

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

2014-01-01

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

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

PubMed Central

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

2014-01-01

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

CFD simulation of flow through heart: a perspective review.

PubMed

Khalafvand, S S; Ng, E Y K; Zhong, L

2011-01-01

The heart is an organ which pumps blood around the body by contraction of muscular wall. There is a coupled system in the heart containing the motion of wall and the motion of blood fluid; both motions must be computed simultaneously, which make biological computational fluid dynamics (CFD) difficult. The wall of the heart is not rigid and hence proper boundary conditions are essential for CFD modelling. Fluid-wall interaction is very important for real CFD modelling. There are many assumptions for CFD simulation of the heart that make it far from a real model. A realistic fluid-structure interaction modelling the structure by the finite element method and the fluid flow by CFD use more realistic coupling algorithms. This type of method is very powerful to solve the complex properties of the cardiac structure and the sensitive interaction of fluid and structure. The final goal of heart modelling is to simulate the total heart function by integrating cardiac anatomy, electrical activation, mechanics, metabolism and fluid mechanics together, as in the computational framework.

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

On Crowd-verification of Biological Networks

PubMed Central

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

2013-01-01

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

Scientific conferences: A big hello to halogen bonding

NASA Astrophysics Data System (ADS)

Erdelyi, Mate

2014-09-01

Halogen bonding connects a wide range of subjects -- from materials science to structural biology, from computation to crystal engineering, and from synthesis to spectroscopy. The 1st International Symposium on Halogen Bonding explored the state of the art in this fast-growing field of research.

RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

PubMed Central

Ellington, Roni; Wachira, James

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968

RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

PubMed

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.

The Use of Multiscale Molecular Simulations in Understanding a Relationship between the Structure and Function of Biological Systems of the Brain: The Application to Monoamine Oxidase Enzymes

PubMed Central

Vianello, Robert; Domene, Carmen; Mavri, Janez

2016-01-01

HIGHLIGHTS Computational techniques provide accurate descriptions of the structure and dynamics of biological systems, contributing to their understanding at an atomic level.Classical MD simulations are a precious computational tool for the processes where no chemical reactions take place.QM calculations provide valuable information about the enzyme activity, being able to distinguish among several mechanistic pathways, provided a carefully selected cluster model of the enzyme is considered.Multiscale QM/MM simulation is the method of choice for the computational treatment of enzyme reactions offering quantitative agreement with experimentally determined reaction parameters.Molecular simulation provide insight into the mechanism of both the catalytic activity and inhibition of monoamine oxidases, thus aiding in the rational design of their inhibitors that are all employed and antidepressants and antiparkinsonian drugs. Aging society and therewith associated neurodegenerative and neuropsychiatric diseases, including depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease, urgently require novel drug candidates. Targets include monoamine oxidases A and B (MAOs), acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and various receptors and transporters. For rational drug design it is particularly important to combine experimental synthetic, kinetic, toxicological, and pharmacological information with structural and computational work. This paper describes the application of various modern computational biochemistry methods in order to improve the understanding of a relationship between the structure and function of large biological systems including ion channels, transporters, receptors, and metabolic enzymes. The methods covered stem from classical molecular dynamics simulations to understand the physical basis and the time evolution of the structures, to combined QM, and QM/MM approaches to probe the chemical mechanisms of enzymatic activities and their inhibition. As an illustrative example, the later will focus on the monoamine oxidase family of enzymes, which catalyze the degradation of amine neurotransmitters in various parts of the brain, the imbalance of which is associated with the development and progression of a range of neurodegenerative disorders. Inhibitors that act mainly on MAO A are used in the treatment of depression, due to their ability to raise serotonin concentrations, while MAO B inhibitors decrease dopamine degradation and improve motor control in patients with Parkinson disease. Our results give strong support that both MAO isoforms, A and B, operate through the hydride transfer mechanism. Relevance of MAO catalyzed reactions and MAO inhibition in the context of neurodegeneration will be discussed. PMID:27471444

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

PubMed

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

2018-01-01

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

The effects of a visualization-centered curriculum on conceptual understanding and representational competence in high school biology

NASA Astrophysics Data System (ADS)

Wilder, Anna

The purpose of this study was to investigate the effects of a visualization-centered curriculum, Hemoglobin: A Case of Double Identity, on conceptual understanding and representational competence in high school biology. Sixty-nine students enrolled in three sections of freshman biology taught by the same teacher participated in this study. Online Chemscape Chime computer-based molecular visualizations were incorporated into the 10-week curriculum to introduce students to fundamental structure and function relationships. Measures used in this study included a Hemoglobin Structure and Function Test, Mental Imagery Questionnaire, Exam Difficulty Survey, the Student Assessment of Learning Gains, the Group Assessment of Logical Thinking, the Attitude Toward Science in School Assessment, audiotapes of student interviews, students' artifacts, weekly unit activity surveys, informal researcher observations and a teacher's weekly questionnaire. The Hemoglobin Structure and Function Test, consisting of Parts A and B, was administered as a pre and posttest. Part A used exclusively verbal test items to measure conceptual understanding, while Part B used visual-verbal test items to measure conceptual understanding and representational competence. Results of the Hemoglobin Structure and Function pre and posttest revealed statistically significant gains in conceptual understanding and representational competence, suggesting the visualization-centered curriculum implemented in this study was effective in supporting positive learning outcomes. The large positive correlation between posttest results on Part A, comprised of all-verbal test items, and Part B, using visual-verbal test items, suggests this curriculum supported students' mutual development of conceptual understanding and representational competence. Evidence based on student interviews, Student Assessment of Learning Gains ratings and weekly activity surveys indicated positive attitudes toward the use of Chemscape Chime software and the computer-based molecular visualization activities as learning tools. Evidence from these same sources also indicated that students felt computer-based molecular visualization activities in conjunction with other classroom activities supported their learning. Implications for instructional design are discussed.

Meta!Blast computer game: a pipeline from science to 3D art to education

NASA Astrophysics Data System (ADS)

Schneller, William; Campbell, P. J.; Bassham, Diane; Wurtele, Eve Syrkin

2012-03-01

Meta!Blast (http://www.metablast.org) is designed to address the challenges students often encounter in understanding cell and metabolic biology. Developed by faculty and students in biology, biochemistry, computer science, game design, pedagogy, art and story, Meta!Blast is being created using Maya (http://usa.autodesk.com/maya/) and the Unity 3D (http://unity3d.com/) game engine, for Macs and PCs in classrooms; it has also been exhibited in an immersive environment. Here, we describe the pipeline from protein structural data and holographic information to art to the threedimensional (3D) environment to the game engine, by which we provide a publicly-available interactive 3D cellular world that mimics a photosynthetic plant cell.

Modern morphometry: new perspectives in physical anthropology.

PubMed

Mantini, Simone; Ripani, Maurizio

2009-06-01

In the past one hundred years physical anthropology has recourse to more and more efficient methods, which provide several new information regarding, human evolution and biology. Apart from the molecular approach, the introduction of new computed assisted techniques gave rise to a new concept of morphometry. Computed tomography and 3D-imaging, allowed providing anatomical description of the external and inner structures exceeding the problems encountered with the traditional morphometric methods. Furthermore, the support of geometric morphometrics, allowed creating geometric models to investigate morphological variation in terms of evolution, ontogeny and variability. The integration of these new tools gave rise to the virtual anthropology and to a new image of the anthropologist in which anatomical, biological, mathematical statistical and data processing information are fused in a multidisciplinary approach.

Systems Biology and Birth Defects Prevention: Blockade of the Glucocorticoid Receptor Prevents Arsenic-Induced Birth Defects

PubMed Central

Ahir, Bhavesh K.; Sanders, Alison P.; Rager, Julia E.

2013-01-01

Background: The biological mechanisms by which environmental metals are associated with birth defects are largely unknown. Systems biology–based approaches may help to identify key pathways that mediate metal-induced birth defects as well as potential targets for prevention. Objectives: First, we applied a novel computational approach to identify a prioritized biological pathway that associates metals with birth defects. Second, in a laboratory setting, we sought to determine whether inhibition of the identified pathway prevents developmental defects. Methods: Seven environmental metals were selected for inclusion in the computational analysis: arsenic, cadmium, chromium, lead, mercury, nickel, and selenium. We used an in silico strategy to predict genes and pathways associated with both metal exposure and developmental defects. The most significant pathway was identified and tested using an in ovo whole chick embryo culture assay. We further evaluated the role of the pathway as a mediator of metal-induced toxicity using the in vitro midbrain micromass culture assay. Results: The glucocorticoid receptor pathway was computationally predicted to be a key mediator of multiple metal-induced birth defects. In the chick embryo model, structural malformations induced by inorganic arsenic (iAs) were prevented when signaling of the glucocorticoid receptor pathway was inhibited. Further, glucocorticoid receptor inhibition demonstrated partial to complete protection from both iAs- and cadmium-induced neurodevelopmental toxicity in vitro. Conclusions: Our findings highlight a novel approach to computationally identify a targeted biological pathway for examining birth defects prevention. PMID:23458687

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

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.

The Electron Microscopy Outreach Program: A Web-based resource for research and education.

PubMed

Sosinsky, G E; Baker, T S; Hand, G; Ellisman, M H

1999-01-01

We have developed a centralized World Wide Web (WWW)-based environment that serves as a resource of software tools and expertise for biological electron microscopy. A major focus is molecular electron microscopy, but the site also includes information and links on structural biology at all levels of resolution. This site serves to help integrate or link structural biology techniques in accordance with user needs. The WWW site, called the Electron Microscopy (EM) Outreach Program (URL: http://emoutreach.sdsc.edu), provides scientists with computational and educational tools for their research and edification. In particular, we have set up a centralized resource containing course notes, references, and links to image analysis and three-dimensional reconstruction software for investigators wanting to learn about EM techniques either within or outside of their fields of expertise. Copyright 1999 Academic Press.

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

PubMed Central

Sali, Andrej; Berman, Helen M.; Schwede, Torsten; Trewhella, Jill; Kleywegt, Gerard; Burley, Stephen K.; Markley, John; Nakamura, Haruki; Adams, Paul; Bonvin, Alexandre M.J.J.; Chiu, Wah; Dal Peraro, Matteo; Di Maio, Frank; Ferrin, Thomas E.; Grünewald, Kay; Gutmanas, Aleksandras; Henderson, Richard; Hummer, Gerhard; Iwasaki, Kenji; Johnson, Graham; Lawson, Catherine L.; Meiler, Jens; Marti-Renom, Marc A.; Montelione, Gaetano T.; Nilges, Michael; Nussinov, Ruth; Patwardhan, Ardan; Rappsilber, Juri; Read, Randy J.; Saibil, Helen; Schröder, Gunnar F.; Schwieters, Charles D.; Seidel, Claus A. M.; Svergun, Dmitri; Topf, Maya; Ulrich, Eldon L.; Velankar, Sameer; Westbrook, John D.

2016-01-01

Summary Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models? PMID:26095030

Tools and procedures for visualization of proteins and other biomolecules.

PubMed

Pan, Lurong; Aller, Stephen G

2015-04-01

Protein, peptides, and nucleic acids are biomolecules that drive biological processes in living organisms. An enormous amount of structural data for a large number of these biomolecules has been described with atomic precision in the form of structural "snapshots" that are freely available in public repositories. These snapshots can help explain how the biomolecules function, the nature of interactions between multi-molecular complexes, and even how small-molecule drugs can modulate the biomolecules for clinical benefits. Furthermore, these structural snapshots serve as inputs for sophisticated computer simulations to turn the biomolecules into moving, "breathing" molecular machines for understanding their dynamic properties in real-time computer simulations. In order for the researcher to take advantage of such a wealth of structural data, it is necessary to gain competency in the use of computer molecular visualization tools for exploring the structures and visualizing three-dimensional spatial representations. Here, we present protocols for using two common visualization tools--the Web-based Jmol and the stand-alone PyMOL package--as well as a few examples of other popular tools. Copyright © 2015 John Wiley & Sons, Inc.

International Journal of Quantum Chemistry, Quantum Biology Symposium No. 24. Proceedings of the International Symposium on the Application of Fundamental Theory to Problems of Biology and Pharmacology, Held at Ponce de Leon Resort, St. Augustine, Florida on March 1-7, 1997. Volume 65, No. 6, 1997.

DTIC Science & Technology

1997-03-01

characteristic musk odor [7]. Molecular shape is important in circular dichroism and chirality. Enantiomers, if viewed in isolation, have identical all...simply a function of the length of the elon - gated structure). We can introduce V/S as an index of molecular shape by using computed molecular

The computational linguistics of biological sequences

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

Searls, D.

1995-12-31

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

Computer Simulations of Intrinsically Disordered Proteins

NASA Astrophysics Data System (ADS)

Chong, Song-Ho; Chatterjee, Prathit; Ham, Sihyun

2017-05-01

The investigation of intrinsically disordered proteins (IDPs) is a new frontier in structural and molecular biology that requires a new paradigm to connect structural disorder to function. Molecular dynamics simulations and statistical thermodynamics potentially offer ideal tools for atomic-level characterizations and thermodynamic descriptions of this fascinating class of proteins that will complement experimental studies. However, IDPs display sensitivity to inaccuracies in the underlying molecular mechanics force fields. Thus, achieving an accurate structural characterization of IDPs via simulations is a challenge. It is also daunting to perform a configuration-space integration over heterogeneous structural ensembles sampled by IDPs to extract, in particular, protein configurational entropy. In this review, we summarize recent efforts devoted to the development of force fields and the critical evaluations of their performance when applied to IDPs. We also survey recent advances in computational methods for protein configurational entropy that aim to provide a thermodynamic link between structural disorder and protein activity.

ISMB 2016 offers outstanding science, networking, and celebration

PubMed Central

Fogg, Christiana

2016-01-01

The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community.  ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas. PMID:27347392

ISMB 2016 offers outstanding science, networking, and celebration.

PubMed

Fogg, Christiana

2016-01-01

The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community.  ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas.

Three Dimensional Object Recognition Using an Unsupervised Neural Network: Understanding the Distinguishing Features

DTIC Science & Technology

1992-12-23

predominance of structural models of recognition, of which a recent example is the Recognition By Components (RBC) theory ( Biederman , 1987 ). Structural...related to recent statistical theory (Huber, 1985; Friedman, 1987 ) and is derived from a biologically motivated computational theory (Bienenstock et...dimensional object recognition (Intrator and Gold, 1991). The method is related to recent statistical theory (Huber, 1985; Friedman, 1987 ) and is derived

Cloud Computing for Protein-Ligand Binding Site Comparison

PubMed Central

2013-01-01

The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery. PMID:23762824

Cloud computing for protein-ligand binding site comparison.

PubMed

Hung, Che-Lun; Hua, Guan-Jie

2013-01-01

The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery.

CSB: a Python framework for structural bioinformatics.

PubMed

Kalev, Ivan; Mechelke, Martin; Kopec, Klaus O; Holder, Thomas; Carstens, Simeon; Habeck, Michael

2012-11-15

Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice. Stable release packages are available for download from the Python Package Index (PyPI) as well as from the project's website http://csb.codeplex.com. ivan.kalev@gmail.com or michael.habeck@tuebingen.mpg.de

sbv IMPROVER: Modern Approach to Systems Biology.

PubMed

Guryanova, Svetlana; Guryanova, Anna

2017-01-01

The increasing amount and variety of data in biosciences call for innovative methods of visualization, scientific verification, and pathway analysis. Novel approaches to biological networks and research quality control are important because of their role in development of new products, improvement, and acceleration of existing health policies and research for novel ways of solving scientific challenges. One such approach is sbv IMPROVER. It is a platform that uses crowdsourcing and verification to create biological networks with easy public access. It contains 120 networks built in Biological Expression Language (BEL) to interpret data from PubMed articles with high-quality verification available for free on the CBN database. Computable, human-readable biological networks with a structured syntax are a powerful way of representing biological information generated from high-density data. This article presents sbv IMPROVER, a crowd-verification approach for the visualization and expansion of biological networks.

Nucleic Acid-Based Nanodevices in Biological Imaging.

PubMed

Chakraborty, Kasturi; Veetil, Aneesh T; Jaffrey, Samie R; Krishnan, Yamuna

2016-06-02

The nanoscale engineering of nucleic acids has led to exciting molecular technologies for high-end biological imaging. The predictable base pairing, high programmability, and superior new chemical and biological methods used to access nucleic acids with diverse lengths and in high purity, coupled with computational tools for their design, have allowed the creation of a stunning diversity of nucleic acid-based nanodevices. Given their biological origin, such synthetic devices have a tremendous capacity to interface with the biological world, and this capacity lies at the heart of several nucleic acid-based technologies that are finding applications in biological systems. We discuss these diverse applications and emphasize the advantage, in terms of physicochemical properties, that the nucleic acid scaffold brings to these contexts. As our ability to engineer this versatile scaffold increases, its applications in structural, cellular, and organismal biology are clearly poised to massively expand.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core

PubMed Central

Hucka, Michael; Bergmann, Frank T.; Hoops, Stefan; Keating, Sarah M.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Wilkinson, Darren J.

2017-01-01

Summary Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/. PMID:26528564

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Hoops, Stefan; Keating, Sarah M; Sahle, Sven; Schaff, James C; Smith, Lucian P; Wilkinson, Darren J

2015-09-04

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

PubMed

Hucka, Michael; Bergmann, Frank T; Hoops, Stefan; Keating, Sarah M; Sahle, Sven; Schaff, James C; Smith, Lucian P; Wilkinson, Darren J

2015-06-01

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.

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.

LMSD: LIPID MAPS structure database

PubMed Central

Sud, Manish; Fahy, Eoin; Cotter, Dawn; Brown, Alex; Dennis, Edward A.; Glass, Christopher K.; Merrill, Alfred H.; Murphy, Robert C.; Raetz, Christian R. H.; Russell, David W.; Subramaniam, Shankar

2007-01-01

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available at PMID:17098933

New generation of elastic network models.

PubMed

López-Blanco, José Ramón; Chacón, Pablo

2016-04-01

The intrinsic flexibility of proteins and nucleic acids can be grasped from remarkably simple mechanical models of particles connected by springs. In recent decades, Elastic Network Models (ENMs) combined with Normal Model Analysis widely confirmed their ability to predict biologically relevant motions of biomolecules and soon became a popular methodology to reveal large-scale dynamics in multiple structural biology scenarios. The simplicity, robustness, low computational cost, and relatively high accuracy are the reasons behind the success of ENMs. This review focuses on recent advances in the development and application of ENMs, paying particular attention to combinations with experimental data. Successful application scenarios include large macromolecular machines, structural refinement, docking, and evolutionary conservation. Copyright © 2015 Elsevier Ltd. All rights reserved.

The importance of employing computational resources for the automation of drug discovery.

PubMed

Rosales-Hernández, Martha Cecilia; Correa-Basurto, José

2015-03-01

The application of computational tools to drug discovery helps researchers to design and evaluate new drugs swiftly with a reduce economic resources. To discover new potential drugs, computational chemistry incorporates automatization for obtaining biological data such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), as well as drug mechanisms of action. This editorial looks at examples of these computational tools, including docking, molecular dynamics simulation, virtual screening, quantum chemistry, quantitative structural activity relationship, principal component analysis and drug screening workflow systems. The authors then provide their perspectives on the importance of these techniques for drug discovery. Computational tools help researchers to design and discover new drugs for the treatment of several human diseases without side effects, thus allowing for the evaluation of millions of compounds with a reduced cost in both time and economic resources. The problem is that operating each program is difficult; one is required to use several programs and understand each of the properties being tested. In the future, it is possible that a single computer and software program will be capable of evaluating the complete properties (mechanisms of action and ADMET properties) of ligands. It is also possible that after submitting one target, this computer-software will be capable of suggesting potential compounds along with ways to synthesize them, and presenting biological models for testing.

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.

Recent advances in automated protein design and its future challenges.

PubMed

Setiawan, Dani; Brender, Jeffrey; Zhang, Yang

2018-04-25

Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

RaptorX server: a resource for template-based protein structure modeling.

PubMed

Källberg, Morten; Margaryan, Gohar; Wang, Sheng; Ma, Jianzhu; Xu, Jinbo

2014-01-01

Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server ( http://raptorx.uchicago.edu ), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling.Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min.

The RCSB protein data bank: integrative view of protein, gene and 3D structural information

PubMed Central

Rose, Peter W.; Prlić, Andreas; Altunkaya, Ali; Bi, Chunxiao; Bradley, Anthony R.; Christie, Cole H.; Costanzo, Luigi Di; Duarte, Jose M.; Dutta, Shuchismita; Feng, Zukang; Green, Rachel Kramer; Goodsell, David S.; Hudson, Brian; Kalro, Tara; Lowe, Robert; Peisach, Ezra; Randle, Christopher; Rose, Alexander S.; Shao, Chenghua; Tao, Yi-Ping; Valasatava, Yana; Voigt, Maria; Westbrook, John D.; Woo, Jesse; Yang, Huangwang; Young, Jasmine Y.; Zardecki, Christine; Berman, Helen M.; Burley, Stephen K.

2017-01-01

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, http://rcsb.org), the US data center for the global PDB archive, makes PDB data freely available to all users, from structural biologists to computational biologists and beyond. New tools and resources have been added to the RCSB PDB web portal in support of a ‘Structural View of Biology.’ Recent developments have improved the User experience, including the high-speed NGL Viewer that provides 3D molecular visualization in any web browser, improved support for data file download and enhanced organization of website pages for query, reporting and individual structure exploration. Structure validation information is now visible for all archival entries. PDB data have been integrated with external biological resources, including chromosomal position within the human genome; protein modifications; and metabolic pathways. PDB-101 educational materials have been reorganized into a searchable website and expanded to include new features such as the Geis Digital Archive. PMID:27794042

Novel Yersinia Pestis Toxin that Resembles Bacillus Anthracis Edema Factor: Study of Activity and Structural Modeling

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

Motin, V; Garcia, E; Barsky, D

2003-02-05

The goal of this project was to begin both experimental and computational studies of the novel plague toxin to establish its biological properties and create its 3D-model. The project was divided into two parts. (1) Experimental--This part was devoted to determine distribution of the genes encoding novel plague toxin among different isolates of Y.pestis. If the EF-like activity is important for Y.pestis pathogenicity, it is anticipated that all highly virulent strains will contain the toxin genes. Also, they proposed to initiate research to investigate the functionality of the novel Y.pestis toxin that they hypothesize is likely to significantly contribute tomore » the virulence of this dangerous microbe. this research design consisted of amplification, cloning and expression in E.coli the toxin genes followed by affinity purification of the recombinant protein that can be further used for testing of enzymatic activity. (2) Computational--The structural modeling of the putative EF of Y.pestis was based on multiple sequence alignments, secondary structure predictions, and comparison with 3D models of the EF of B. anthracis. The x-ray structure of the last has been recently published [Nature. 2002. 415(Jan):396-402]. The final model was selected after detailed analysis to determine if the structure is consistent with the biological function.« less

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

PubMed Central

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-01-01

Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems. PMID:17081289

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

PubMed

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-11-02

We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.

Using directed information for influence discovery in interconnected dynamical systems

NASA Astrophysics Data System (ADS)

Rao, Arvind; Hero, Alfred O.; States, David J.; Engel, James Douglas

2008-08-01

Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

Defining functional distance using manifold embeddings of gene ontology annotations

PubMed Central

Lerman, Gilad; Shakhnovich, Boris E.

2007-01-01

Although rigorous measures of similarity for sequence and structure are now well established, the problem of defining functional relationships has been particularly daunting. Here, we present several manifold embedding techniques to compute distances between Gene Ontology (GO) functional annotations and consequently estimate functional distances between protein domains. To evaluate accuracy, we correlate the functional distance to the well established measures of sequence, structural, and phylogenetic similarities. Finally, we show that manual classification of structures into folds and superfamilies is mirrored by proximity in the newly defined function space. We show how functional distances place structure–function relationships in biological context resulting in insight into divergent and convergent evolution. The methods and results in this paper can be readily generalized and applied to a wide array of biologically relevant investigations, such as accuracy of annotation transference, the relationship between sequence, structure, and function, or coherence of expression modules. PMID:17595300

Frustration in Biomolecules

PubMed Central

Ferreiro, Diego U.; Komives, Elizabeth A.; Wolynes, Peter G.

2014-01-01

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their own structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of frustration in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how biomolecular structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding mechanisms. We review here how a large part of the biological functions of proteins are related to subtle local physical frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology. PMID:25225856

Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy.

PubMed

Ogorzalek, Tadeusz L; Hura, Greg L; Belsom, Adam; Burnett, Kathryn H; Kryshtafovych, Andriy; Tainer, John A; Rappsilber, Juri; Tsutakawa, Susan E; Fidelis, Krzysztof

2018-03-01

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. © 2018 Wiley Periodicals, Inc.

A computational framework to empower probabilistic protein design

PubMed Central

Fromer, Menachem; Yanover, Chen

2008-01-01

Motivation: The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult. Results: In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future. Contact: fromer@cs.huji.ac.il PMID:18586717

Design, synthesis and bioactivity evaluation of tribactam beta lactamase inhibitors.

PubMed

Copar, Anton; Prevec, Tadeja; Anzic, Borut; Mesar, Tomaz; Selic, Lovro; Vilar, Mateja; Solmajer, Tom

2002-03-25

Known carbapenem compounds with inhibitory effect towards beta-lactamase enzymes are formed from bicyclical beta lactam structural scaffolds. On the basis of results from theoretical computational methods and molecular modelling we have designed and developed a synthetic route towards novel, biologically active tricyclic derivatives of carbapenems.

Understanding Phylogenies in Biology: The Influence of a Gestalt Perceptual Principle

ERIC Educational Resources Information Center

Novick, Laura R.; Catley, Kefyn M.

2007-01-01

Cladograms, hierarchical diagrams depicting evolutionary histories among (groups of) species, are commonly drawn in 2 informationally equivalent formats--tree and ladder. The authors hypothesize that these formats are not computationally equivalent because the Gestalt principle of good continuation obscures the hierarchical structure of ladders.…

Application of Virtual Reality Technology in Biology Education.

ERIC Educational Resources Information Center

Shim, Kew-Cheol; Park, Jong-Seok; Kim, Hyun-Sup; Kim, Jae-Hyun; Park, Young-Chul; Ryu, Hai-Il

2003-01-01

Reports on the findings of a study designed to develop three-dimensional virtual reality technology (VRT) learning programs for middle school students and evaluate the program's educational value. Focuses on the topic of structure and function of the eye. Concludes that VRT simulations allow comfortable interaction with computers and increase the…

Receptor Surface Models in the Classroom: Introducing Molecular Modeling to Students in a 3-D World

ERIC Educational Resources Information Center

Geldenhuys, Werner J.; Hayes, Michael; Van der Schyf, Cornelis J.; Allen, David D.; Malan, Sarel F.

2007-01-01

A simple, novel and generally applicable method to demonstrate structure-activity associations of a group of biologically interesting compounds in relation to receptor binding is described. This method is useful for undergraduates and graduate students in medicinal chemistry and computer modeling programs.

Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology

PubMed Central

2013-01-01

Background The Gene Ontology (GO) facilitates the description of the action of gene products in a biological context. Many GO terms refer to chemical entities that participate in biological processes. To facilitate accurate and consistent systems-wide biological representation, it is necessary to integrate the chemical view of these entities with the biological view of GO functions and processes. We describe a collaborative effort between the GO and the Chemical Entities of Biological Interest (ChEBI) ontology developers to ensure that the representation of chemicals in the GO is both internally consistent and in alignment with the chemical expertise captured in ChEBI. Results We have examined and integrated the ChEBI structural hierarchy into the GO resource through computationally-assisted manual curation of both GO and ChEBI. Our work has resulted in the creation of computable definitions of GO terms that contain fully defined semantic relationships to corresponding chemical terms in ChEBI. Conclusions The set of logical definitions using both the GO and ChEBI has already been used to automate aspects of GO development and has the potential to allow the integration of data across the domains of biology and chemistry. These logical definitions are available as an extended version of the ontology from http://purl.obolibrary.org/obo/go/extensions/go-plus.owl. PMID:23895341

Parallel seed-based approach to multiple protein structure similarities detection

DOE PAGES

Chapuis, Guillaume; Le Boudic-Jamin, Mathilde; Andonov, Rumen; ...

2015-01-01

Finding similarities between protein structures is a crucial task in molecular biology. Most of the existing tools require proteins to be aligned in order-preserving way and only find single alignments even when multiple similar regions exist. We propose a new seed-based approach that discovers multiple pairs of similar regions. Its computational complexity is polynomial and it comes with a quality guarantee—the returned alignments have both root mean squared deviations (coordinate-based as well as internal-distances based) lower than a given threshold, if such exist. We do not require the alignments to be order preserving (i.e., we consider nonsequential alignments), which makesmore » our algorithm suitable for detecting similar domains when comparing multidomain proteins as well as to detect structural repetitions within a single protein. Because the search space for nonsequential alignments is much larger than for sequential ones, the computational burden is addressed by extensive use of parallel computing techniques: a coarse-grain level parallelism making use of available CPU cores for computation and a fine-grain level parallelism exploiting bit-level concurrency as well as vector instructions.« less

Computational approaches for de novo design and redesign of metal-binding sites on proteins.

PubMed

Akcapinar, Gunseli Bayram; Sezerman, Osman Ugur

2017-04-28

Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature's own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox. © 2017 The Author(s).

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.

Rosen's (M,R) system as an X-machine.

PubMed

Palmer, Michael L; Williams, Richard A; Gatherer, Derek

2016-11-07

Robert Rosen's (M,R) system is an abstract biological network architecture that is allegedly both irreducible to sub-models of its component states and non-computable on a Turing machine. (M,R) stands as an obstacle to both reductionist and mechanistic presentations of systems biology, principally due to its self-referential structure. If (M,R) has the properties claimed for it, computational systems biology will not be possible, or at best will be a science of approximate simulations rather than accurate models. Several attempts have been made, at both empirical and theoretical levels, to disprove this assertion by instantiating (M,R) in software architectures. So far, these efforts have been inconclusive. In this paper, we attempt to demonstrate why - by showing how both finite state machine and stream X-machine formal architectures fail to capture the self-referential requirements of (M,R). We then show that a solution may be found in communicating X-machines, which remove self-reference using parallel computation, and then synthesise such machine architectures with object-orientation to create a formal basis for future software instantiations of (M,R) systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

RNA folding kinetics using Monte Carlo and Gillespie algorithms.

PubMed

Clote, Peter; Bayegan, Amir H

2018-04-01

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as the hok/sok system in E. coli. Although linear algebra provides an exact computational solution of secondary structure folding kinetics with respect to the Turner energy model for tiny ([Formula: see text]20 nt) RNA sequences, the folding kinetics for larger sequences can only be approximated by binning structures into macrostates in a coarse-grained model, or by repeatedly simulating secondary structure folding with either the Monte Carlo algorithm or the Gillespie algorithm. Here we investigate the relation between the Monte Carlo algorithm and the Gillespie algorithm. We prove that asymptotically, the expected time for a K-step trajectory of the Monte Carlo algorithm is equal to [Formula: see text] times that of the Gillespie algorithm, where [Formula: see text] denotes the Boltzmann expected network degree. If the network is regular (i.e. every node has the same degree), then the mean first passage time (MFPT) computed by the Monte Carlo algorithm is equal to MFPT computed by the Gillespie algorithm multiplied by [Formula: see text]; however, this is not true for non-regular networks. In particular, RNA secondary structure folding kinetics, as computed by the Monte Carlo algorithm, is not equal to the folding kinetics, as computed by the Gillespie algorithm, although the mean first passage times are roughly correlated. Simulation software for RNA secondary structure folding according to the Monte Carlo and Gillespie algorithms is publicly available, as is our software to compute the expected degree of the network of secondary structures of a given RNA sequence-see http://bioinformatics.bc.edu/clote/RNAexpNumNbors .

Single-molecule protein sequencing through fingerprinting: computational assessment

NASA Astrophysics Data System (ADS)

Yao, Yao; Docter, Margreet; van Ginkel, Jetty; de Ridder, Dick; Joo, Chirlmin

2015-10-01

Proteins are vital in all biological systems as they constitute the main structural and functional components of cells. Recent advances in mass spectrometry have brought the promise of complete proteomics by helping draft the human proteome. Yet, this commonly used protein sequencing technique has fundamental limitations in sensitivity. Here we propose a method for single-molecule (SM) protein sequencing. A major challenge lies in the fact that proteins are composed of 20 different amino acids, which demands 20 molecular reporters. We computationally demonstrate that it suffices to measure only two types of amino acids to identify proteins and suggest an experimental scheme using SM fluorescence. When achieved, this highly sensitive approach will result in a paradigm shift in proteomics, with major impact in the biological and medical sciences.

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.

Computational Biochemistry-Enzyme Mechanisms Explored.

PubMed

Culka, Martin; Gisdon, Florian J; Ullmann, G Matthias

2017-01-01

Understanding enzyme mechanisms is a major task to achieve in order to comprehend how living cells work. Recent advances in biomolecular research provide huge amount of data on enzyme kinetics and structure. The analysis of diverse experimental results and their combination into an overall picture is, however, often challenging. Microscopic details of the enzymatic processes are often anticipated based on several hints from macroscopic experimental data. Computational biochemistry aims at creation of a computational model of an enzyme in order to explain microscopic details of the catalytic process and reproduce or predict macroscopic experimental findings. Results of such computations are in part complementary to experimental data and provide an explanation of a biochemical process at the microscopic level. In order to evaluate the mechanism of an enzyme, a structural model is constructed which can be analyzed by several theoretical approaches. Several simulation methods can and should be combined to get a reliable picture of the process of interest. Furthermore, abstract models of biological systems can be constructed combining computational and experimental data. In this review, we discuss structural computational models of enzymatic systems. We first discuss various models to simulate enzyme catalysis. Furthermore, we review various approaches how to characterize the enzyme mechanism both qualitatively and quantitatively using different modeling approaches. © 2017 Elsevier Inc. All rights reserved.

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-based tool for several different input formats, and it makes analysis of complex models accessible to a larger community, as it is platform independent as a web-service and does not require understanding of the underlying mathematics.

Mental structures and hierarchical brain processing. Comment on “Toward a computational framework for cognitive biology: Unifying approaches from cognitive neuroscience and comparative cognition” by W. Tecumseh Fitch

NASA Astrophysics Data System (ADS)

Petkov, C. I.

2014-09-01

Fitch proposes an appealing hypothesis that humans are dendrophiles, who constantly build mental trees supported by analogous hierarchical brain processes [1]. Moreover, it is argued that, by comparison, nonhuman animals build flat or more compact behaviorally-relevant structures. Should we thus expect less impressive hierarchical brain processes in other animals? Not necessarily.

Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment.

PubMed

Baichoo, Shakuntala; Ouzounis, Christos A

A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.

PubMed

Wang, Juexin; Luttrell, Joseph; Zhang, Ning; Khan, Saad; Shi, NianQing; Wang, Michael X; Kang, Jing-Qiong; Wang, Zheng; Xu, Dong

2016-01-01

Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.

Genome Scale Modeling in Systems Biology: Algorithms and Resources

PubMed Central

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

2014-01-01

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

Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein.

PubMed

Steindl, Theodora M; Crump, Carolyn E; Hayden, Frederick G; Langer, Thierry

2005-10-06

The development and application of a sophisticated virtual screening and selection protocol to identify potential, novel inhibitors of the human rhinovirus coat protein employing various computer-assisted strategies are described. A large commercially available database of compounds was screened using a highly selective, structure-based pharmacophore model generated with the program Catalyst. A docking study and a principal component analysis were carried out within the software package Cerius and served to validate and further refine the obtained results. These combined efforts led to the selection of six candidate structures, for which in vitro anti-rhinoviral activity could be shown in a biological assay.

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop.

PubMed

Sali, Andrej; Berman, Helen M; Schwede, Torsten; Trewhella, Jill; Kleywegt, Gerard; Burley, Stephen K; Markley, John; Nakamura, Haruki; Adams, Paul; Bonvin, Alexandre M J J; Chiu, Wah; Peraro, Matteo Dal; Di Maio, Frank; Ferrin, Thomas E; Grünewald, Kay; Gutmanas, Aleksandras; Henderson, Richard; Hummer, Gerhard; Iwasaki, Kenji; Johnson, Graham; Lawson, Catherine L; Meiler, Jens; Marti-Renom, Marc A; Montelione, Gaetano T; Nilges, Michael; Nussinov, Ruth; Patwardhan, Ardan; Rappsilber, Juri; Read, Randy J; Saibil, Helen; Schröder, Gunnar F; Schwieters, Charles D; Seidel, Claus A M; Svergun, Dmitri; Topf, Maya; Ulrich, Eldon L; Velankar, Sameer; Westbrook, John D

2015-07-07

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models? Copyright © 2015 Elsevier Ltd. All rights reserved.

Learning nucleic acids solving by bioinformatics problems.

PubMed

Nunes, Rhewter; Barbosa de Almeida Júnior, Edivaldo; Pessoa Pinto de Menezes, Ivandilson; Malafaia, Guilherme

2015-01-01

The article describes the development of a new approach to teach molecular biology to undergraduate biology students. The 34 students who participated in this research belonged to the first period of the Biological Sciences teaching course of the Instituto Federal Goiano at Urutaí Campus, Brazil. They were registered in Cell Biology in the first semester of 2013. They received four 55 min-long expository/dialogued lectures that covered the content of "structure and functions of nucleic acids". Later the students were invited to attend four meetings (in a computer laboratory) in which some concepts of Bioinformatics were presented and some problems of the Rosalind platform were solved. The observations we report here are very useful as a broad groundwork to development new research. An interesting possibility is research into the effects of bioinformatics interventions that improve molecular biology learning. © 2015 The International Union of Biochemistry and Molecular Biology.

Conception and development of the Second Life® Embryo Physics Course.

PubMed

Gordon, Richard

2013-06-01

The study of embryos with the tools and mindset of physics, started by Wilhelm His in the 1880s, has resumed after a hiatus of a century. The Embryo Physics Course convenes online allowing interested researchers and students, who are scattered around the world, to gather weekly in one place, the virtual world of Second Life®. It attracts people from a wide variety of disciplines and walks of life: applied mathematics, artificial life, bioengineering, biophysics, cancer biology, cellular automata, civil engineering, computer science, embryology, electrical engineering, evolution, finite element methods, history of biology, human genetics, mathematics, molecular developmental biology, molecular biology, nanotechnology, philosophy of biology, phycology, physics, self-reproducing systems, stem cells, tensegrity structures, theoretical biology, and tissue engineering. Now in its fifth year, the Embryo Physics Course provides a focus for research on the central question of how an embryo builds itself.

Conflicts of interest improve collective computation of adaptive social structures

PubMed Central

Brush, Eleanor R.; Krakauer, David C.; Flack, Jessica C.

2018-01-01

In many biological systems, the functional behavior of a group is collectively computed by the system’s individual components. An example is the brain’s ability to make decisions via the activity of billions of neurons. A long-standing puzzle is how the components’ decisions combine to produce beneficial group-level outputs, despite conflicts of interest and imperfect information. We derive a theoretical model of collective computation from mechanistic first principles, using results from previous work on the computation of power structure in a primate model system. Collective computation has two phases: an information accumulation phase, in which (in this study) pairs of individuals gather information about their fighting abilities and make decisions about their dominance relationships, and an information aggregation phase, in which these decisions are combined to produce a collective computation. To model information accumulation, we extend a stochastic decision-making model—the leaky integrator model used to study neural decision-making—to a multiagent game-theoretic framework. We then test alternative algorithms for aggregating information—in this study, decisions about dominance resulting from the stochastic model—and measure the mutual information between the resultant power structure and the “true” fighting abilities. We find that conflicts of interest can improve accuracy to the benefit of all agents. We also find that the computation can be tuned to produce different power structures by changing the cost of waiting for a decision. The successful application of a similar stochastic decision-making model in neural and social contexts suggests general principles of collective computation across substrates and scales. PMID:29376116

Navigating 3D electron microscopy maps with EM-SURFER.

PubMed

Esquivel-Rodríguez, Juan; Xiong, Yi; Han, Xusi; Guang, Shuomeng; Christoffer, Charles; Kihara, Daisuke

2015-05-30

The Electron Microscopy DataBank (EMDB) is growing rapidly, accumulating biological structural data obtained mainly by electron microscopy and tomography, which are emerging techniques for determining large biomolecular complex and subcellular structures. Together with the Protein Data Bank (PDB), EMDB is becoming a fundamental resource of the tertiary structures of biological macromolecules. To take full advantage of this indispensable resource, the ability to search the database by structural similarity is essential. However, unlike high-resolution structures stored in PDB, methods for comparing low-resolution electron microscopy (EM) density maps in EMDB are not well established. We developed a computational method for efficiently searching low-resolution EM maps. The method uses a compact fingerprint representation of EM maps based on the 3D Zernike descriptor, which is derived from a mathematical series expansion for EM maps that are considered as 3D functions. The method is implemented in a web server named EM-SURFER, which allows users to search against the entire EMDB in real-time. EM-SURFER compares the global shapes of EM maps. Examples of search results from different types of query structures are discussed. We developed EM-SURFER, which retrieves structurally relevant matches for query EM maps from EMDB within seconds. The unique capability of EM-SURFER to detect 3D shape similarity of low-resolution EM maps should prove invaluable in structural biology.

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.

Optimal structure and parameter learning of Ising models

DOE PAGES

Lokhov, Andrey; Vuffray, Marc Denis; Misra, Sidhant; ...

2018-03-16

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, whichmore » is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.« less

The application of quantum mechanics in structure-based drug design.

PubMed

Mucs, Daniel; Bryce, Richard A

2013-03-01

Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein-ligand recognition. In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein-ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein-ligand docking, protein-ligand binding affinities and ligand strain on binding. Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.

Optimal structure and parameter learning of Ising models

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

Lokhov, Andrey; Vuffray, Marc Denis; Misra, Sidhant

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, whichmore » is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.« less

Force fields and scoring functions for carbohydrate simulation.

PubMed

Xiong, Xiuming; Chen, Zhaoqiang; Cossins, Benjamin P; Xu, Zhijian; Shao, Qiang; Ding, Kai; Zhu, Weiliang; Shi, Jiye

2015-01-12

Carbohydrate dynamics plays a vital role in many biological processes, but we are not currently able to probe this with experimental approaches. The highly flexible nature of carbohydrate structures differs in many aspects from other biomolecules, posing significant challenges for studies employing computational simulation. Over past decades, computational study of carbohydrates has been focused on the development of structure prediction methods, force field optimization, molecular dynamics simulation, and scoring functions for carbohydrate-protein interactions. Advances in carbohydrate force fields and scoring functions can be largely attributed to enhanced computational algorithms, application of quantum mechanics, and the increasing number of experimental structures determined by X-ray and NMR techniques. The conformational analysis of carbohydrates is challengeable and has gone into intensive study in elucidating the anomeric, the exo-anomeric, and the gauche effects. Here, we review the issues associated with carbohydrate force fields and scoring functions, which will have a broad application in the field of carbohydrate-based drug design. Copyright © 2014 Elsevier Ltd. All rights reserved.

The minimalist grammar of action

PubMed Central

Pastra, Katerina; Aloimonos, Yiannis

2012-01-01

Language and action have been found to share a common neural basis and in particular a common ‘syntax’, an analogous hierarchical and compositional organization. While language structure analysis has led to the formulation of different grammatical formalisms and associated discriminative or generative computational models, the structure of action is still elusive and so are the related computational models. However, structuring action has important implications on action learning and generalization, in both human cognition research and computation. In this study, we present a biologically inspired generative grammar of action, which employs the structure-building operations and principles of Chomsky's Minimalist Programme as a reference model. In this grammar, action terminals combine hierarchically into temporal sequences of actions of increasing complexity; the actions are bound with the involved tools and affected objects and are governed by certain goals. We show, how the tool role and the affected-object role of an entity within an action drives the derivation of the action syntax in this grammar and controls recursion, merge and move, the latter being mechanisms that manifest themselves not only in human language, but in human action too. PMID:22106430

FOLD-EM: automated fold recognition in medium- and low-resolution (4-15 Å) electron density maps.

PubMed

Saha, Mitul; Morais, Marc C

2012-12-15

Owing to the size and complexity of large multi-component biological assemblies, the most tractable approach to determining their atomic structure is often to fit high-resolution radiographic or nuclear magnetic resonance structures of isolated components into lower resolution electron density maps of the larger assembly obtained using cryo-electron microscopy (cryo-EM). This hybrid approach to structure determination requires that an atomic resolution structure of each component, or a suitable homolog, is available. If neither is available, then the amount of structural information regarding that component is limited by the resolution of the cryo-EM map. However, even if a suitable homolog cannot be identified using sequence analysis, a search for structural homologs should still be performed because structural homology often persists throughout evolution even when sequence homology is undetectable, As macromolecules can often be described as a collection of independently folded domains, one way of searching for structural homologs would be to systematically fit representative domain structures from a protein domain database into the medium/low resolution cryo-EM map and return the best fits. Taken together, the best fitting non-overlapping structures would constitute a 'mosaic' backbone model of the assembly that could aid map interpretation and illuminate biological function. Using the computational principles of the Scale-Invariant Feature Transform (SIFT), we have developed FOLD-EM-a computational tool that can identify folded macromolecular domains in medium to low resolution (4-15 Å) electron density maps and return a model of the constituent polypeptides in a fully automated fashion. As a by-product, FOLD-EM can also do flexible multi-domain fitting that may provide insight into conformational changes that occur in macromolecular assemblies.

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 neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology.

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 neurally-grounded framework for theoretical cognitive science is within reach that can move beyond polarized debates and provide a more adequate theoretical future for cognitive biology. Copyright © 2014. Published by Elsevier B.V.

microRNAs Databases: Developmental Methodologies, Structural and Functional Annotations.

PubMed

Singh, Nagendra Kumar

2017-09-01

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

Understanding phylogenies in biology: the influence of a Gestalt Perceptual Principle.

PubMed

Novick, Laura R; Catley, Kefyn M

2007-12-01

Cladograms, hierarchical diagrams depicting evolutionary histories among (groups of) species, are commonly drawn in 2 informationally equivalent formats--tree and ladder. The authors hypothesize that these formats are not computationally equivalent because the Gestalt principle of good continuation obscures the hierarchical structure of ladders. Experimental results confirmed that university students (N = 44) prefer to subdivide ladders in accordance with good continuation rather than with the underlying hierarchical structure. Two subsequent experiments (N = 164) investigated cladogram understanding by examining students' ability to translate between formats (e.g., from tree to ladder). As predicted, students had greater difficulty understanding ladders than trees. This effect was larger for students with weaker backgrounds in biology. These results have important implications for evolution education reform.

Anomalous Diffraction in Crystallographic Phase Evaluation

PubMed Central

Hendrickson, Wayne A.

2014-01-01

X-ray diffraction patterns from crystals of biological macromolecules contain sufficient information to define atomic structures, but atomic positions are inextricable without having electron-density images. Diffraction measurements provide amplitudes, but the computation of electron density also requires phases for the diffracted waves. The resonance phenomenon known as anomalous scattering offers a powerful solution to this phase problem. Exploiting scattering resonances from diverse elements, the methods of multiwavelength anomalous diffraction (MAD) and single-wavelength anomalous diffraction (SAD) now predominate for de novo determinations of atomic-level biological structures. This review describes the physical underpinnings of anomalous diffraction methods, the evolution of these methods to their current maturity, the elements, procedures and instrumentation used for effective implementation, and the realm of applications. PMID:24726017

78 FR 59927 - Next Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-30

... Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational, and Systems Biology..., Computational, and Systems Biology [External Review Draft]'' (EPA/600/R-13/214A). EPA is also announcing that... Advances in Molecular, Computational, and Systems Biology [External Review Draft]'' is available primarily...

78 FR 68058 - Next Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-13

... Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational, and Systems Biology... Generation Risk Assessment: Incorporation of Recent Advances in Molecular, Computational, and Systems Biology..., computational, and systems biology data can better inform risk assessment. This draft document is available for...

DNA as information.

PubMed

Wills, Peter R

2016-03-13

This article reviews contributions to this theme issue covering the topic 'DNA as information' in relation to the structure of DNA, the measure of its information content, the role and meaning of information in biology and the origin of genetic coding as a transition from uninformed to meaningful computational processes in physical systems. © 2016 The Author(s).

Legacies in material flux: Structural changes before long-term studies

Treesearch

D.J. Bain; M.B. Green; J. Campbell; J. Chamblee; S. Chaoka; J. Fraterrigo; S. Kaushal; S. Martin; T. Jordan; T. Parolari; B. Sobczak; D. Weller; W. M. Wollheim; E. Boose; J. Duncan; G. Gettel; B. Hall; P. Kumar; J. Thompson; J. Vose; E. Elliott; D. Leigh

2012-01-01

Legacy effects of past land use and disturbance are increasingly recognized, yet consistent definitions of and criteria for defining them do not exist. To address this gap in biological- and ecosystem-assessment frameworks, we propose a general metric for evaluating potential legacy effects, which are computed by normalizing altered system function persistence with...

AN INTERACTIVE DEMONSTRATION OF 3D COMPUTER-AIDED TOMOGRAPHY IMAGERY FOR INTERPRETING AND QUANTIFYING ANTHROPOGENIC IMPACTS TO MARINE BENTHIC COMMUNITIES

EPA Science Inventory

Two dimensional CT analysis has allowed the quantification of biologically derived structures (tubes and tunnels) within benthic sediments. The percent tube and tunnel area (PTTA) and the sediment bulk density (SXA) resulting from these activities have been shown be to highly cor...

Simulation of the M13 life cycle I: Assembly of a genetically-structured deterministic chemical kinetic simulation.

PubMed

Smeal, Steven W; Schmitt, Margaret A; Pereira, Ronnie Rodrigues; Prasad, Ashok; Fisk, John D

2017-01-01

To expand the quantitative, systems level understanding and foster the expansion of the biotechnological applications of the filamentous bacteriophage M13, we have unified the accumulated quantitative information on M13 biology into a genetically-structured, experimentally-based computational simulation of the entire phage life cycle. The deterministic chemical kinetic simulation explicitly includes the molecular details of DNA replication, mRNA transcription, protein translation and particle assembly, as well as the competing protein-protein and protein-nucleic acid interactions that control the timing and extent of phage production. The simulation reproduces the holistic behavior of M13, closely matching experimentally reported values of the intracellular levels of phage species and the timing of events in the M13 life cycle. The computational model provides a quantitative description of phage biology, highlights gaps in the present understanding of M13, and offers a framework for exploring alternative mechanisms of regulation in the context of the complete M13 life cycle. Copyright © 2016 Elsevier Inc. All rights reserved.

Enhanced conformational sampling of carbohydrates by Hamiltonian replica-exchange simulation.

PubMed

Mishra, Sushil Kumar; Kara, Mahmut; Zacharias, Martin; Koca, Jaroslav

2014-01-01

Knowledge of the structure and conformational flexibility of carbohydrates in an aqueous solvent is important to improving our understanding of how carbohydrates function in biological systems. In this study, we extend a variant of the Hamiltonian replica-exchange molecular dynamics (MD) simulation to improve the conformational sampling of saccharides in an explicit solvent. During the simulations, a biasing potential along the glycosidic-dihedral linkage between the saccharide monomer units in an oligomer is applied at various levels along the replica runs to enable effective transitions between various conformations. One reference replica runs under the control of the original force field. The method was tested on disaccharide structures and further validated on biologically relevant blood group B, Lewis X and Lewis A trisaccharides. The biasing potential-based replica-exchange molecular dynamics (BP-REMD) method provided a significantly improved sampling of relevant conformational states compared with standard continuous MD simulations, with modest computational costs. Thus, the proposed BP-REMD approach adds a new dimension to existing carbohydrate conformational sampling approaches by enhancing conformational sampling in the presence of solvent molecules explicitly at relatively low computational cost.

Life's attractors : understanding developmental systems through reverse engineering and in silico evolution.

PubMed

Jaeger, Johannes; Crombach, Anton

2012-01-01

We propose an approach to evolutionary systems biology which is based on reverse engineering of gene regulatory networks and in silico evolutionary simulations. We infer regulatory parameters for gene networks by fitting computational models to quantitative expression data. This allows us to characterize the regulatory structure and dynamical repertoire of evolving gene regulatory networks with a reasonable amount of experimental and computational effort. We use the resulting network models to identify those regulatory interactions that are conserved, and those that have diverged between different species. Moreover, we use the models obtained by data fitting as starting points for simulations of evolutionary transitions between species. These simulations enable us to investigate whether such transitions are random, or whether they show stereotypical series of regulatory changes which depend on the structure and dynamical repertoire of an evolving network. Finally, we present a case study-the gap gene network in dipterans (flies, midges, and mosquitoes)-to illustrate the practical application of the proposed methodology, and to highlight the kind of biological insights that can be gained by this approach.

Palingol: a declarative programming language to describe nucleic acids' secondary structures and to scan sequence database.

PubMed Central

Billoud, B; Kontic, M; Viari, A

1996-01-01

At the DNA/RNA level, biological signals are defined by a combination of spatial structures and sequence motifs. Until now, few attempts had been made in writing general purpose search programs that take into account both sequence and structure criteria. Indeed, the most successful structure scanning programs are usually dedicated to particular structures and are written using general purpose programming languages through a complex and time consuming process where the biological problem of defining the structure and the computer engineering problem of looking for it are intimately intertwined. In this paper, we describe a general representation of structures, suitable for database scanning, together with a programming language, Palingol, designed to manipulate it. Palingol has specific data types, corresponding to structural elements-basically helices-that can be arranged in any way to form a complex structure. As a consequence of the declarative approach used in Palingol, the user should only focus on 'what to search for' while the language engine takes care of 'how to look for it'. Therefore, it becomes simpler to write a scanning program and the structural constraints that define the required structure are more clearly identified. PMID:8628670

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Data Intensive Analysis of Biomolecular Simulations

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

Straatsma, TP; Soares, Thereza A.

2007-12-01

The advances in biomolecular modeling and simulation made possible by the availability of increasingly powerful high performance computing resources is extending molecular simulations to biological more relevant system size and time scales. At the same time, advances in simulation methodologies are allowing more complex processes to be described more accurately. These developments make a systems approach to computational structural biology feasible, but this will require a focused emphasis on the comparative analysis of the increasing number of molecular simulations that are being carried out for biomolecular systems with more realistic models, multi-component environments, and for longer simulation times. Just asmore » in the case of the analysis of the large data sources created by the new high-throughput experimental technologies, biomolecular computer simulations contribute to the progress in biology through comparative analysis. The continuing increase in available protein structures allows the comparative analysis of the role of structure and conformational flexibility in protein function, and is the foundation of the discipline of structural bioinformatics. This creates the opportunity to derive general findings from the comparative analysis of molecular dynamics simulations of a wide range of proteins, protein-protein complexes and other complex biological systems. Because of the importance of protein conformational dynamics for protein function, it is essential that the analysis of molecular trajectories is carried out using a novel, more integrative and systematic approach. We are developing a much needed rigorous computer science based framework for the efficient analysis of the increasingly large data sets resulting from molecular simulations. Such a suite of capabilities will also provide the required tools for access and analysis of a distributed library of generated trajectories. Our research is focusing on the following areas: (1) the development of an efficient analysis framework for very large scale trajectories on massively parallel architectures, (2) the development of novel methodologies that allow automated detection of events in these very large data sets, and (3) the efficient comparative analysis of multiple trajectories. The goal of the presented work is the development of new algorithms that will allow biomolecular simulation studies to become an integral tool to address the challenges of post-genomic biological research. The strategy to deliver the required data intensive computing applications that can effectively deal with the volume of simulation data that will become available is based on taking advantage of the capabilities offered by the use of large globally addressable memory architectures. The first requirement is the design of a flexible underlying data structure for single large trajectories that will form an adaptable framework for a wide range of analysis capabilities. The typical approach to trajectory analysis is to sequentially process trajectories time frame by time frame. This is the implementation found in molecular simulation codes such as NWChem, and has been designed in this way to be able to run on workstation computers and other architectures with an aggregate amount of memory that would not allow entire trajectories to be held in core. The consequence of this approach is an I/O dominated solution that scales very poorly on parallel machines. We are currently using an approach of developing tools specifically intended for use on large scale machines with sufficient main memory that entire trajectories can be held in core. This greatly reduces the cost of I/O as trajectories are read only once during the analysis. In our current Data Intensive Analysis (DIANA) implementation, each processor determines and skips to the entry within the trajectory that typically will be available in multiple files and independently from all other processors read the appropriate frames.« less

Data Intensive Analysis of Biomolecular Simulations

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

Straatsma, TP

2008-03-01

The advances in biomolecular modeling and simulation made possible by the availability of increasingly powerful high performance computing resources is extending molecular simulations to biological more relevant system size and time scales. At the same time, advances in simulation methodologies are allowing more complex processes to be described more accurately. These developments make a systems approach to computational structural biology feasible, but this will require a focused emphasis on the comparative analysis of the increasing number of molecular simulations that are being carried out for biomolecular systems with more realistic models, multi-component environments, and for longer simulation times. Just asmore » in the case of the analysis of the large data sources created by the new high-throughput experimental technologies, biomolecular computer simulations contribute to the progress in biology through comparative analysis. The continuing increase in available protein structures allows the comparative analysis of the role of structure and conformational flexibility in protein function, and is the foundation of the discipline of structural bioinformatics. This creates the opportunity to derive general findings from the comparative analysis of molecular dynamics simulations of a wide range of proteins, protein-protein complexes and other complex biological systems. Because of the importance of protein conformational dynamics for protein function, it is essential that the analysis of molecular trajectories is carried out using a novel, more integrative and systematic approach. We are developing a much needed rigorous computer science based framework for the efficient analysis of the increasingly large data sets resulting from molecular simulations. Such a suite of capabilities will also provide the required tools for access and analysis of a distributed library of generated trajectories. Our research is focusing on the following areas: (1) the development of an efficient analysis framework for very large scale trajectories on massively parallel architectures, (2) the development of novel methodologies that allow automated detection of events in these very large data sets, and (3) the efficient comparative analysis of multiple trajectories. The goal of the presented work is the development of new algorithms that will allow biomolecular simulation studies to become an integral tool to address the challenges of post-genomic biological research. The strategy to deliver the required data intensive computing applications that can effectively deal with the volume of simulation data that will become available is based on taking advantage of the capabilities offered by the use of large globally addressable memory architectures. The first requirement is the design of a flexible underlying data structure for single large trajectories that will form an adaptable framework for a wide range of analysis capabilities. The typical approach to trajectory analysis is to sequentially process trajectories time frame by time frame. This is the implementation found in molecular simulation codes such as NWChem, and has been designed in this way to be able to run on workstation computers and other architectures with an aggregate amount of memory that would not allow entire trajectories to be held in core. The consequence of this approach is an I/O dominated solution that scales very poorly on parallel machines. We are currently using an approach of developing tools specifically intended for use on large scale machines with sufficient main memory that entire trajectories can be held in core. This greatly reduces the cost of I/O as trajectories are read only once during the analysis. In our current Data Intensive Analysis (DIANA) implementation, each processor determines and skips to the entry within the trajectory that typically will be available in multiple files and independently from all other processors read the appropriate frames.« less

Bioinformatics by Example: From Sequence to Target

NASA Astrophysics Data System (ADS)

Kossida, Sophia; Tahri, Nadia; Daizadeh, Iraj

2002-12-01

With the completion of the human genome, and the imminent completion of other large-scale sequencing and structure-determination projects, computer-assisted bioscience is aimed to become the new paradigm for conducting basic and applied research. The presence of these additional bioinformatics tools stirs great anxiety for experimental researchers (as well as for pedagogues), since they are now faced with a wider and deeper knowledge of differing disciplines (biology, chemistry, physics, mathematics, and computer science). This review targets those individuals who are interested in using computational methods in their teaching or research. By analyzing a real-life, pharmaceutical, multicomponent, target-based example the reader will experience this fascinating new discipline.

Developing protein documentaries and other multimedia presentations for molecular biology.

PubMed

Quinn, G; Wang, H P; Martinez, D; Bourne, P E

1999-01-01

Computer-based multimedia technology for distance learning and research has come of age--the price point is acceptable, domain experts using off-the-shelf software can prepare compelling materials, and the material can be efficiently delivered via the Internet to a large audience. While not presenting any new scientific results, this paper outlines experiences with a variety of commercial and free software tools and the associated protocols we have used to prepare protein documentaries and other multimedia presentations relevant to molecular biology. A protein documentary is defined here as a description of the relationship between structure and function in a single protein or in a related family of proteins. A description using text and images which is further enhanced by the use of sound and interactive graphics. Examples of documentaries prepared to describe cAMP dependent protein kinase, the founding structural member of the protein kinase family for which there is now over 40 structures can be found at http://franklin.burnham-inst.org/rcsb. A variety of other prototype multimedia presentations for molecular biology described in this paper can be found at http://fraklin.burnham-inst.org.

Life sciences and environmental sciences

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

Not Available

1992-02-01

The DOE laboratories play a unique role in bringing multidisciplinary talents -- in biology, physics, chemistry, computer sciences, and engineering -- to bear on major problems in the life and environmental sciences. Specifically, the laboratories utilize these talents to fulfill OHER's mission of exploring and mitigating the health and environmental effects of energy use, and of developing health and medical applications of nuclear energy-related phenomena. At Lawrence Berkeley Laboratory (LBL) support of this mission is evident across the spectrum of OHER-sponsored research, especially in the broad areas of genomics, structural biology, basic cell and molecular biology, carcinogenesis, energy and environment,more » applications to biotechnology, and molecular, nuclear and radiation medicine. These research areas are briefly described.« less

Infrared Microtransmission And Microreflectance Of Biological Systems

NASA Astrophysics Data System (ADS)

Hill, Steve L.; Krishnan, K.; Powell, Jay R.

1989-12-01

The infrared microsampling technique has been successfully applied to a variety of biological systems. A microtomed tissue section may be prepared to permit both visual and infrared discrimination. Infrared structural information may be obtained for a single cell, and computer-enhanced images of tissue specimens may be calculated from spectral map data sets. An analysis of a tissue section anomaly may gg suest eitherprotein compositional differences or a localized concentration of foreign matterp. Opaque biological materials such as teeth, gallstones, and kidney stones may be analyzed by microreflectance spectroscop. Absorption anomalies due to specular dispersion are corrected with the Kraymers-Kronig transformation. Corrected microreflectance spectra may contribute to compositional analysis and correlate diseased-related spectral differences to visual specimen anomalies.

A generative, probabilistic model of local protein structure.

PubMed

Boomsma, Wouter; Mardia, Kanti V; Taylor, Charles C; Ferkinghoff-Borg, Jesper; Krogh, Anders; Hamelryck, Thomas

2008-07-01

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data.

PubMed

O'Neill, M A; Hilgetag, C C

2001-08-29

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement.

The portable UNIX programming system (PUPS) and CANTOR: a computational environment for dynamical representation and analysis of complex neurobiological data.

PubMed Central

O'Neill, M A; Hilgetag, C C

2001-01-01

Many problems in analytical biology, such as the classification of organisms, the modelling of macromolecules, or the structural analysis of metabolic or neural networks, involve complex relational data. Here, we describe a software environment, the portable UNIX programming system (PUPS), which has been developed to allow efficient computational representation and analysis of such data. The system can also be used as a general development tool for database and classification applications. As the complexity of analytical biology problems may lead to computation times of several days or weeks even on powerful computer hardware, the PUPS environment gives support for persistent computations by providing mechanisms for dynamic interaction and homeostatic protection of processes. Biological objects and their interrelations are also represented in a homeostatic way in PUPS. Object relationships are maintained and updated by the objects themselves, thus providing a flexible, scalable and current data representation. Based on the PUPS environment, we have developed an optimization package, CANTOR, which can be applied to a wide range of relational data and which has been employed in different analyses of neuroanatomical connectivity. The CANTOR package makes use of the PUPS system features by modifying candidate arrangements of objects within the system's database. This restructuring is carried out via optimization algorithms that are based on user-defined cost functions, thus providing flexible and powerful tools for the structural analysis of the database content. The use of stochastic optimization also enables the CANTOR system to deal effectively with incomplete and inconsistent data. Prototypical forms of PUPS and CANTOR have been coded and used successfully in the analysis of anatomical and functional mammalian brain connectivity, involving complex and inconsistent experimental data. In addition, PUPS has been used for solving multivariate engineering optimization problems and to implement the digital identification system (DAISY), a system for the automated classification of biological objects. PUPS is implemented in ANSI-C under the POSIX.1 standard and is to a great extent architecture- and operating-system independent. The software is supported by systems libraries that allow multi-threading (the concurrent processing of several database operations), as well as the distribution of the dynamic data objects and library operations over clusters of computers. These attributes make the system easily scalable, and in principle allow the representation and analysis of arbitrarily large sets of relational data. PUPS and CANTOR are freely distributed (http://www.pups.org.uk) as open-source software under the GNU license agreement. PMID:11545702

The 2008 Lindau Nobel Laureate Meeting: Robert Huber, Chemistry 1988. Interview by Klaus J. Korak.

PubMed

Huber, Robert

2008-11-25

Robert Huber and his colleagues, Johann Deisenhofer and Hartmut Michel, elucidated the three-dimensional structure of the Rhodopseudomonas viridis photosynthetic reaction center. This membrane protein complex is a basic component of photosynthesis - a process fundamental to life on Earth - and for their work, Huber and his colleagues received the 1988 Nobel Prize in Chemistry. Because structural information is central to understanding virtually any biological process, Huber likens their discovery to "switching on the light" for scientists trying to understand photosynthesis. Huber marvels at the growth of structural biology since the time he entered the field, when crystallographers worked with hand-made instruments and primitive computers, and only "a handful" of crystallographers would meet annually in the Bavarian Alps. In the "explosion" of structural biology since his early days of research, Huber looks to the rising generation of scientists to solve the remaining mysteries in the field - such as the mechanisms that underlie protein folding. A strong proponent of science mentorship, Huber delights in meeting young researchers at the annual Nobel Laureate Meetings in Lindau, Germany. He hopes that among these young scientists is an "Einstein of biology" who, he says with a twinkle in his eye, "doesn't know it yet." The interview was conducted by JoVE co-founder Klaus J. Korak at the Lindau Nobel Laureate Meeting 2008 in Lindau, Germany.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

PubMed Central

Pathak, Rajesh Kumar; Gupta, Sanjay Mohan; Gaur, Vikram Singh; Pandey, Dinesh

2015-01-01

Abstract In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes. PMID:26484978

Use of micro computed-tomography and 3D printing for reverse engineering of mouse embryo nasal capsule

NASA Astrophysics Data System (ADS)

Tesařová, M.; Zikmund, T.; Kaucká, M.; Adameyko, I.; Jaroš, J.; Paloušek, D.; Škaroupka, D.; Kaiser, J.

2016-03-01

Imaging of increasingly complex cartilage in vertebrate embryos is one of the key tasks of developmental biology. This is especially important to study shape-organizing processes during initial skeletal formation and growth. Advanced imaging techniques that are reflecting biological needs give a powerful impulse to push the boundaries of biological visualization. Recently, techniques for contrasting tissues and organs have improved considerably, extending traditional 2D imaging approaches to 3D . X-ray micro computed tomography (μCT), which allows 3D imaging of biological objects including their internal structures with a resolution in the micrometer range, in combination with contrasting techniques seems to be the most suitable approach for non-destructive imaging of embryonic developing cartilage. Despite there are many software-based ways for visualization of 3D data sets, having a real solid model of the studied object might give novel opportunities to fully understand the shape-organizing processes in the developing body. In this feasibility study we demonstrated the full procedure of creating a real 3D object of mouse embryo nasal capsule, i.e. the staining, the μCT scanning combined by the advanced data processing and the 3D printing.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core.

PubMed

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

2018-03-09

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.

Semantic Web meets Integrative Biology: a survey.

PubMed

Chen, Huajun; Yu, Tong; Chen, Jake Y

2013-01-01

Integrative Biology (IB) uses experimental or computational quantitative technologies to characterize biological systems at the molecular, cellular, tissue and population levels. IB typically involves the integration of the data, knowledge and capabilities across disciplinary boundaries in order to solve complex problems. We identify a series of bioinformatics problems posed by interdisciplinary integration: (i) data integration that interconnects structured data across related biomedical domains; (ii) ontology integration that brings jargons, terminologies and taxonomies from various disciplines into a unified network of ontologies; (iii) knowledge integration that integrates disparate knowledge elements from multiple sources; (iv) service integration that build applications out of services provided by different vendors. We argue that IB can benefit significantly from the integration solutions enabled by Semantic Web (SW) technologies. The SW enables scientists to share content beyond the boundaries of applications and websites, resulting into a web of data that is meaningful and understandable to any computers. In this review, we provide insight into how SW technologies can be used to build open, standardized and interoperable solutions for interdisciplinary integration on a global basis. We present a rich set of case studies in system biology, integrative neuroscience, bio-pharmaceutics and translational medicine, to highlight the technical features and benefits of SW applications in IB.

Protein structure determination by exhaustive search of Protein Data Bank derived databases.

PubMed

Stokes-Rees, Ian; Sliz, Piotr

2010-12-14

Parallel sequence and structure alignment tools have become ubiquitous and invaluable at all levels in the study of biological systems. We demonstrate the application and utility of this same parallel search paradigm to the process of protein structure determination, benefitting from the large and growing corpus of known structures. Such searches were previously computationally intractable. Through the method of Wide Search Molecular Replacement, developed here, they can be completed in a few hours with the aide of national-scale federated cyberinfrastructure. By dramatically expanding the range of models considered for structure determination, we show that small (less than 12% structural coverage) and low sequence identity (less than 20% identity) template structures can be identified through multidimensional template scoring metrics and used for structure determination. Many new macromolecular complexes can benefit significantly from such a technique due to the lack of known homologous protein folds or sequences. We demonstrate the effectiveness of the method by determining the structure of a full-length p97 homologue from Trichoplusia ni. Example cases with the MHC/T-cell receptor complex and the EmoB protein provide systematic estimates of minimum sequence identity, structure coverage, and structural similarity required for this method to succeed. We describe how this structure-search approach and other novel computationally intensive workflows are made tractable through integration with the US national computational cyberinfrastructure, allowing, for example, rapid processing of the entire Structural Classification of Proteins protein fragment database.

Computational Study of the Genomic and Epigenomic Phenomena

NASA Astrophysics Data System (ADS)

Yang, Wenjing

Biological systems are perhaps the ultimate complex systems, uniquely capable of processing and communicating information, reproducing in their lifetimes, and adapting in evolutionary time scales. My dissertation research focuses on using computational approaches to understand the biocomplexity manifested in the multitude of length scales and time scales. At the molecular and cellular level, central to the complex behavior of a biological system is the regulatory network. My research study focused on epigenetics, which is essential for multicellular organisms to establish cellular identity during development or in response to intracellular and environmental stimuli. My computational study of epigenomics is greatly facilitated by recent advances in high-throughput sequencing technology, which enables high-resolution snapshots of epigenomes and transcriptomes. Using human CD4+ T cell as a model system, the dynamical changes in epigenome and transcriptome pertinent to T cell activation were investigated at the genome scale. Going beyond traditional focus on transcriptional regulation, I provided evidences that post-transcriptional regulation may serve as a major component of the regulatory network. In addition, I explored alternative polyadenylation, another novel aspect of gene regulation, and how it cross-talks with the local chromatin structure. As the renowned theoretical biologist Theodosius Dobzhansky said eloquently, "Nothing in biology makes sense except in the light of evolution''. To better understand this ubiquitous driving force in the biological world, I went beyond molecular events in a single organism, and investigated the dynamical changes of population structure along the evolutionary time scale. To this end, we used HIV virus population dynamics in the host immune system as a model system. The evolution of HIV viral population plays a key role in AIDS immunopathogenesis with its exceptionally high mutation rate. However, the theoretical studies of the effect of recombination have been rather limited. Given the phylogenetic and experimental evidences for the high recombination rate and its important role in HIV evolution and epidemics, I established a mathematical model to study the effect of recombination, and explored the complex behavior of this dynamics system.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

Optimization of the molecular dynamics method for simulations of DNA and ion transport through biological nanopores.

PubMed

Wells, David B; Bhattacharya, Swati; Carr, Rogan; Maffeo, Christopher; Ho, Anthony; Comer, Jeffrey; Aksimentiev, Aleksei

2012-01-01

Molecular dynamics (MD) simulations have become a standard method for the rational design and interpretation of experimental studies of DNA translocation through nanopores. The MD method, however, offers a multitude of algorithms, parameters, and other protocol choices that can affect the accuracy of the resulting data as well as computational efficiency. In this chapter, we examine the most popular choices offered by the MD method, seeking an optimal set of parameters that enable the most computationally efficient and accurate simulations of DNA and ion transport through biological nanopores. In particular, we examine the influence of short-range cutoff, integration timestep and force field parameters on the temperature and concentration dependence of bulk ion conductivity, ion pairing, ion solvation energy, DNA structure, DNA-ion interactions, and the ionic current through a nanopore.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

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

Molecular Dynamics, Monte Carlo Simulations, and Langevin Dynamics: A Computational Review

PubMed Central

Paquet, Eric; Viktor, Herna L.

2015-01-01

Macromolecular structures, such as neuraminidases, hemagglutinins, and monoclonal antibodies, are not rigid entities. Rather, they are characterised by their flexibility, which is the result of the interaction and collective motion of their constituent atoms. This conformational diversity has a significant impact on their physicochemical and biological properties. Among these are their structural stability, the transport of ions through the M2 channel, drug resistance, macromolecular docking, binding energy, and rational epitope design. To assess these properties and to calculate the associated thermodynamical observables, the conformational space must be efficiently sampled and the dynamic of the constituent atoms must be simulated. This paper presents algorithms and techniques that address the abovementioned issues. To this end, a computational review of molecular dynamics, Monte Carlo simulations, Langevin dynamics, and free energy calculation is presented. The exposition is made from first principles to promote a better understanding of the potentialities, limitations, applications, and interrelations of these computational methods. PMID:25785262

A consistent S-Adenosylmethionine force field improved by dynamic Hirshfeld-I atomic charges for biomolecular simulation

NASA Astrophysics Data System (ADS)

Saez, David Adrian; Vöhringer-Martinez, Esteban

2015-10-01

S-Adenosylmethionine (AdoMet) is involved in many biological processes as cofactor in enzymes transferring its sulfonium methyl group to various substrates. Additionally, it is used as drug and nutritional supplement to reduce the pain in osteoarthritis and against depression. Due to the biological relevance of AdoMet it has been part of various computational simulation studies and will also be in the future. However, to our knowledge no rigorous force field parameter development for its simulation in biological systems has been reported. Here, we use electronic structure calculations combined with molecular dynamics simulations in explicit solvent to develop force field parameters compatible with the AMBER99 force field. Additionally, we propose new dynamic Hirshfeld-I atomic charges which are derived from the polarized electron density of AdoMet in aqueous solution to describe its electrostatic interactions in biological systems. The validation of the force field parameters and the atomic charges is performed against experimental interproton NOE distances of AdoMet in aqueous solution and crystal structures of AdoMet in the cavity of three representative proteins.

CASTp 3.0: computed atlas of surface topography of proteins.

PubMed

Tian, Wei; Chen, Chang; Lei, Xue; Zhao, Jieling; Liang, Jie

2018-06-01

Geometric and topological properties of protein structures, including surface pockets, interior cavities and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a web server that provides online services for locating, delineating and measuring these geometric and topological properties of protein structures. It has been widely used since its inception in 2003. In this article, we present the latest version of the web server, CASTp 3.0. CASTp 3.0 continues to provide reliable and comprehensive identifications and quantifications of protein topography. In addition, it now provides: (i) imprints of the negative volumes of pockets, cavities and channels, (ii) topographic features of biological assemblies in the Protein Data Bank, (iii) improved visualization of protein structures and pockets, and (iv) more intuitive structural and annotated information, including information of secondary structure, functional sites, variant sites and other annotations of protein residues. The CASTp 3.0 web server is freely accessible at http://sts.bioe.uic.edu/castp/.

Year 2 Report: Protein Function Prediction Platform

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

Zhou, C E

2012-04-27

Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fullymore » automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.« less

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

antaRNA: ant colony-based RNA sequence design.

PubMed

Kleinkauf, Robert; Mann, Martin; Backofen, Rolf

2015-10-01

RNA sequence design is studied at least as long as the classical folding problem. Although for the latter the functional fold of an RNA molecule is to be found ,: inverse folding tries to identify RNA sequences that fold into a function-specific target structure. In combination with RNA-based biotechnology and synthetic biology ,: reliable RNA sequence design becomes a crucial step to generate novel biochemical components. In this article ,: the computational tool antaRNA is presented. It is capable of compiling RNA sequences for a given structure that comply in addition with an adjustable full range objective GC-content distribution ,: specific sequence constraints and additional fuzzy structure constraints. antaRNA applies ant colony optimization meta-heuristics and its superior performance is shown on a biological datasets. http://www.bioinf.uni-freiburg.de/Software/antaRNA CONTACT: backofen@informatik.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

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

PubMed Central

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

2016-01-01

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

Prediction of enzymatic pathways by integrative pathway mapping

PubMed Central

Wichelecki, Daniel J; San Francisco, Brian; Zhao, Suwen; Rodionov, Dmitry A; Vetting, Matthew W; Al-Obaidi, Nawar F; Lin, Henry; O'Meara, Matthew J; Scott, David A; Morris, John H; Russel, Daniel; Almo, Steven C; Osterman, Andrei L

2018-01-01

The functions of most proteins are yet to be determined. The function of an enzyme is often defined by its interacting partners, including its substrate and product, and its role in larger metabolic networks. Here, we describe a computational method that predicts the functions of orphan enzymes by organizing them into a linear metabolic pathway. Given candidate enzyme and metabolite pathway members, this aim is achieved by finding those pathways that satisfy structural and network restraints implied by varied input information, including that from virtual screening, chemoinformatics, genomic context analysis, and ligand -binding experiments. We demonstrate this integrative pathway mapping method by predicting the L-gulonate catabolic pathway in Haemophilus influenzae Rd KW20. The prediction was subsequently validated experimentally by enzymology, crystallography, and metabolomics. Integrative pathway mapping by satisfaction of structural and network restraints is extensible to molecular networks in general and thus formally bridges the gap between structural biology and systems biology. PMID:29377793

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

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).

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

PubMed Central

Biesiada, Marcin; Boniecki, Michał J.; Chou, Fang-Chieh; Ferré-D'Amaré, Adrian R.; Das, Rhiju; Dunin-Horkawicz, Stanisław; Geniesse, Caleb; Kappel, Kalli; Kladwang, Wipapat; Krokhotin, Andrey; Łach, Grzegorz E.; Major, François; Mann, Thomas H.; Pachulska-Wieczorek, Katarzyna; Patel, Dinshaw J.; Piccirilli, Joseph A.; Popenda, Mariusz; Purzycka, Katarzyna J.; Ren, Aiming; Rice, Greggory M.; Santalucia, John; Tandon, Arpit; Trausch, Jeremiah J.; Wang, Jian; Weeks, Kevin M.; Williams, Benfeard; Xiao, Yi; Zhang, Dong; Zok, Tomasz

2017-01-01

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5′-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson–Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/. PMID:28138060

Beyond terrestrial biology: charting the chemical universe of α-amino acid structures.

PubMed

Meringer, Markus; Cleaves, H James; Freeland, Stephen J

2013-11-25

α-Amino acids are fundamental to biochemistry as the monomeric building blocks with which cells construct proteins according to genetic instructions. However, the 20 amino acids of the standard genetic code represent a tiny fraction of the number of α-amino acid chemical structures that could plausibly play such a role, both from the perspective of natural processes by which life emerged and evolved, and from the perspective of human-engineered genetically coded proteins. Until now, efforts to describe the structures comprising this broader set, or even estimate their number, have been hampered by the complex combinatorial properties of organic molecules. Here, we use computer software based on graph theory and constructive combinatorics in order to conduct an efficient and exhaustive search of the chemical structures implied by two careful and precise definitions of the α-amino acids relevant to coded biological proteins. Our results include two virtual libraries of α-amino acid structures corresponding to these different approaches, comprising 121 044 and 3 846 structures, respectively, and suggest a simple approach to exploring much larger, as yet uncomputed, libraries of interest.

A computational chemistry perspective on the current status and future direction of hepatitis B antiviral drug discovery.

PubMed

Morgnanesi, Dante; Heinrichs, Eric J; Mele, Anthony R; Wilkinson, Sean; Zhou, Suzanne; Kulp, John L

2015-11-01

Computational chemical biology, applied to research on hepatitis B virus (HBV), has two major branches: bioinformatics (statistical models) and first-principle methods (molecular physics). While bioinformatics focuses on statistical tools and biological databases, molecular physics uses mathematics and chemical theory to study the interactions of biomolecules. Three computational techniques most commonly used in HBV research are homology modeling, molecular docking, and molecular dynamics. Homology modeling is a computational simulation to predict protein structure and has been used to construct conformers of the viral polymerase (reverse transcriptase domain and RNase H domain) and the HBV X protein. Molecular docking is used to predict the most likely orientation of a ligand when it is bound to a protein, as well as determining an energy score of the docked conformation. Molecular dynamics is a simulation that analyzes biomolecule motions and determines conformation and stability patterns. All of these modeling techniques have aided in the understanding of resistance mutations on HBV non-nucleos(t)ide reverse-transcriptase inhibitor binding. Finally, bioinformatics can be used to study the DNA and RNA protein sequences of viruses to both analyze drug resistance and to genotype the viral genomes. Overall, with these techniques, and others, computational chemical biology is becoming more and more necessary in hepatitis B research. This article forms part of a symposium in Antiviral Research on "An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B." Copyright © 2015 Elsevier B.V. All rights reserved.

Ensemble-based evaluation for protein structure models.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2016-06-15

Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts' intuitive assessment of computational models and provides information of practical usefulness of models. https://bitbucket.org/mjamroz/flexscore dkihara@purdue.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Ensemble-based evaluation for protein structure models

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2016-01-01

Motivation: Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. Results: We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts’ intuitive assessment of computational models and provides information of practical usefulness of models. Availability and implementation: https://bitbucket.org/mjamroz/flexscore Contact: dkihara@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307633

Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

PubMed Central

MacDonald, James T.; Kabasakal, Burak V.; Godding, David; Kraatz, Sebastian; Henderson, Louie; Barber, James; Freemont, Paul S.; Murray, James W.

2016-01-01

The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops. PMID:27573845

Structural studies of RNA-protein complexes: A hybrid approach involving hydrodynamics, scattering, and computational methods.

PubMed

Patel, Trushar R; Chojnowski, Grzegorz; Astha; Koul, Amit; McKenna, Sean A; Bujnicki, Janusz M

2017-04-15

The diverse functional cellular roles played by ribonucleic acids (RNA) have emphasized the need to develop rapid and accurate methodologies to elucidate the relationship between the structure and function of RNA. Structural biology tools such as X-ray crystallography and Nuclear Magnetic Resonance are highly useful methods to obtain atomic-level resolution models of macromolecules. However, both methods have sample, time, and technical limitations that prevent their application to a number of macromolecules of interest. An emerging alternative to high-resolution structural techniques is to employ a hybrid approach that combines low-resolution shape information about macromolecules and their complexes from experimental hydrodynamic (e.g. analytical ultracentrifugation) and solution scattering measurements (e.g., solution X-ray or neutron scattering), with computational modeling to obtain atomic-level models. While promising, scattering methods rely on aggregation-free, monodispersed preparations and therefore the careful development of a quality control pipeline is fundamental to an unbiased and reliable structural determination. This review article describes hydrodynamic techniques that are highly valuable for homogeneity studies, scattering techniques useful to study the low-resolution shape, and strategies for computational modeling to obtain high-resolution 3D structural models of RNAs, proteins, and RNA-protein complexes. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Computer-Aided Design of RNA Origami Structures.

PubMed

Sparvath, Steffen L; Geary, Cody W; Andersen, Ebbe S

2017-01-01

RNA nanostructures can be used as scaffolds to organize, combine, and control molecular functionalities, with great potential for applications in nanomedicine and synthetic biology. The single-stranded RNA origami method allows RNA nanostructures to be folded as they are transcribed by the RNA polymerase. RNA origami structures provide a stable framework that can be decorated with functional RNA elements such as riboswitches, ribozymes, interaction sites, and aptamers for binding small molecules or protein targets. The rich library of RNA structural and functional elements combined with the possibility to attach proteins through aptamer-based binding creates virtually limitless possibilities for constructing advanced RNA-based nanodevices.In this chapter we provide a detailed protocol for the single-stranded RNA origami design method using a simple 2-helix tall structure as an example. The first step involves 3D modeling of a double-crossover between two RNA double helices, followed by decoration with tertiary motifs. The second step deals with the construction of a 2D blueprint describing the secondary structure and sequence constraints that serves as the input for computer programs. In the third step, computer programs are used to design RNA sequences that are compatible with the structure, and the resulting outputs are evaluated and converted into DNA sequences to order.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Phylogenetic tree and community structure from a Tangled Nature model.

PubMed

Canko, Osman; Taşkın, Ferhat; Argın, Kamil

2015-10-07

In evolutionary biology, the taxonomy and origination of species are widely studied subjects. An estimation of the evolutionary tree can be done via available DNA sequence data. The calculation of the tree is made by well-known and frequently used methods such as maximum likelihood and neighbor-joining. In order to examine the results of these methods, an evolutionary tree is pursued computationally by a mathematical model, called Tangled Nature. A relatively small genome space is investigated due to computational burden and it is found that the actual and predicted trees are in reasonably good agreement in terms of shape. Moreover, the speciation and the resulting community structure of the food-web are investigated by modularity. Copyright © 2015 Elsevier Ltd. All rights reserved.

COMPUTER PREDICTION OF BIOLOGICAL ACTIVITY OF DIMETHYL-N-(BENZOYL)AMIDOPHOSPHATE AND DIMETHYL-N-(PHENYLSULFONYL)AMIDOPHOSPHATE, EVALUATION OF THEIR CYTOTOXIC ACTIVITY AGAINST LEUKEMIC CELLS IN VITRO.

PubMed

Grynyuk, I I; Prylutska, S V; Kariaka, N S; Sliva, T Yu; Moroz, O V; Franskevych, D V; Amirkhanov, V M; Matyshevska, O P; Slobodyanik, M S

2015-01-01

Structural analogues of β-diketones--dimethyl-N-(benzoyl)amidophosphate (HCP) and dimethyl-N-(phenylsulfonyl)amidophosphate (HSP) were synthesized and identified by the methods of IR, 1H and 31P NMR spectroscopy. Screening of biological activity and calculation of physicochemical parameters of HCP and HSP compounds were done with the use of PASS and ACD/Labs computer programs. A wide range of biological activity of synthesized compounds, antitumor activity in particular, has been found. Calculations of the bioavailability criteria indicate that the investigated compounds have no deviations from Lipinski's rules. HCP compound is characterized by a high lipophilicity at physiological pH as compared to HSP. It was found that cytotoxic effect of the studied compounds on the leukemic L1210 cells was of time- and dose-dependent character. HCP is characterized by more pronounced and early cytotoxic effects as compared to HSP. It was shown that 2.5 mM HCP increased ROS production 3 times in the early period of incubation, and decreased cell viability by 40% after 48 h, and by 66%--after 72 h. Based on the computer calculation and undertaken research, HCP was selected for target chemical modifications and enhancement of its antitumor effect.

Systems biology of the structural proteome.

PubMed

Brunk, Elizabeth; Mih, Nathan; Monk, Jonathan; Zhang, Zhen; O'Brien, Edward J; Bliven, Spencer E; Chen, Ke; Chang, Roger L; Bourne, Philip E; Palsson, Bernhard O

2016-03-11

The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

UML as a cell and biochemistry modeling language.

PubMed

Webb, Ken; White, Tony

2005-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Evolutionary optimization with data collocation for reverse engineering of biological networks.

PubMed

Tsai, Kuan-Yao; Wang, Feng-Sheng

2005-04-01

Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem that requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. We compare three techniques of parameter estimation for nonlinear dynamic biological systems. In the proposed scheme, the modified collocation method is applied to convert the differential equations to the system of algebraic equations. The observed time-course data are then substituted into the algebraic system equations to decouple system interactions in order to obtain the approximate model profiles. Hybrid differential evolution (HDE) with population size of five is able to find a global solution. The method is not only suited for parameter estimation but also can be applied for structure identification. The solution obtained by HDE is then used as the starting point for a local search method to yield the refined estimates.

HPC AND GRID COMPUTING FOR INTEGRATIVE BIOMEDICAL RESEARCH

PubMed Central

Kurc, Tahsin; Hastings, Shannon; Kumar, Vijay; Langella, Stephen; Sharma, Ashish; Pan, Tony; Oster, Scott; Ervin, David; Permar, Justin; Narayanan, Sivaramakrishnan; Gil, Yolanda; Deelman, Ewa; Hall, Mary; Saltz, Joel

2010-01-01

Integrative biomedical research projects query, analyze, and integrate many different data types and make use of datasets obtained from measurements or simulations of structure and function at multiple biological scales. With the increasing availability of high-throughput and high-resolution instruments, the integrative biomedical research imposes many challenging requirements on software middleware systems. In this paper, we look at some of these requirements using example research pattern templates. We then discuss how middleware systems, which incorporate Grid and high-performance computing, could be employed to address the requirements. PMID:20107625

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

PubMed

Ivanciuc, Ovidiu

2013-06-01

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

The Prediction of Botulinum Toxin Structure Based on in Silico and in Vitro Analysis

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

Many of biological system mediated through protein-protein interactions. Knowledge of protein-protein complex structure is required for understanding the function. The determination of huge size and flexible protein-protein complex structure by experimental studies remains difficult, costly and five-consuming, therefore computational prediction of protein structures by homolog modeling and docking studies is valuable method. In addition, MD simulation is also one of the most powerful methods allowing to see the real dynamics of proteins. Here, we predict protein-protein complex structure of botulinum toxin to analyze its property. These bioinformatics methods are useful to report the relation between the flexibility of backbone structure and the activity.

BicPAMS: software for biological data analysis with pattern-based biclustering.

PubMed

Henriques, Rui; Ferreira, Francisco L; Madeira, Sara C

2017-02-02

Biclustering has been largely applied for the unsupervised analysis of biological data, being recognised today as a key technique to discover putative modules in both expression data (subsets of genes correlated in subsets of conditions) and network data (groups of coherently interconnected biological entities). However, given its computational complexity, only recent breakthroughs on pattern-based biclustering enabled efficient searches without the restrictions that state-of-the-art biclustering algorithms place on the structure and homogeneity of biclusters. As a result, pattern-based biclustering provides the unprecedented opportunity to discover non-trivial yet meaningful biological modules with putative functions, whose coherency and tolerance to noise can be tuned and made problem-specific. To enable the effective use of pattern-based biclustering by the scientific community, we developed BicPAMS (Biclustering based on PAttern Mining Software), a software that: 1) makes available state-of-the-art pattern-based biclustering algorithms (BicPAM (Henriques and Madeira, Alg Mol Biol 9:27, 2014), BicNET (Henriques and Madeira, Alg Mol Biol 11:23, 2016), BicSPAM (Henriques and Madeira, BMC Bioinforma 15:130, 2014), BiC2PAM (Henriques and Madeira, Alg Mol Biol 11:1-30, 2016), BiP (Henriques and Madeira, IEEE/ACM Trans Comput Biol Bioinforma, 2015), DeBi (Serin and Vingron, AMB 6:1-12, 2011) and BiModule (Okada et al., IPSJ Trans Bioinf 48(SIG5):39-48, 2007)); 2) consistently integrates their dispersed contributions; 3) further explores additional accuracy and efficiency gains; and 4) makes available graphical and application programming interfaces. Results on both synthetic and real data confirm the relevance of BicPAMS for biological data analysis, highlighting its essential role for the discovery of putative modules with non-trivial yet biologically significant functions from expression and network data. BicPAMS is the first biclustering tool offering the possibility to: 1) parametrically customize the structure, coherency and quality of biclusters; 2) analyze large-scale biological networks; and 3) tackle the restrictive assumptions placed by state-of-the-art biclustering algorithms. These contributions are shown to be key for an adequate, complete and user-assisted unsupervised analysis of biological data. BicPAMS and its tutorial available in http://www.bicpams.com .

Quantitative Characterization of Tissue Microstructure with Temporal Diffusion Spectroscopy

PubMed Central

Xu, Junzhong; Does, Mark D.; Gore, John C.

2009-01-01

The signals recorded by diffusion-weighted magnetic resonance imaging (DWI) are dependent on the micro-structural properties of biological tissues, so it is possible to obtain quantitative structural information non-invasively from such measurements. Oscillating gradient spin echo (OGSE) methods have the ability to probe the behavior of water diffusion over different time scales and the potential to detect variations in intracellular structure. To assist in the interpretation of OGSE data, analytical expressions have been derived for diffusion-weighted signals with OGSE methods for restricted diffusion in some typical structures, including parallel planes, cylinders and spheres, using the theory of temporal diffusion spectroscopy. These analytical predictions have been confirmed with computer simulations. These expressions suggest how OGSE signals from biological tissues should be analyzed to characterize tissue microstructure, including how to estimate cell nuclear sizes. This approach provides a model to interpret diffusion data obtained from OGSE measurements that can be used for applications such as monitoring tumor response to treatment in vivo. PMID:19616979

SOD activity of carboxyfullerenes predicts their neuroprotective efficacy: A structure-activity study

PubMed Central

Ali, Sameh Saad; Hardt, Joshua I.; Dugan, Laura L.

2008-01-01

Superoxide radical anion is a biologically important oxidant that has been linked to tissue injury and inflammation in several diseases. Here we carried out a structure-activity study on 6 different carboxyfullerene superoxide dismutase (SOD) mimetics with distinct electronic and biophysical characteristics. Neurotoxicity via NMDA receptors, which involves intracellular superoxide, was used as a model to evaluate structure-activity relationships between reactivity towards superoxide and neuronal rescue by these drugs. A significant correlation between neuroprotection by carboxyfullerenes and their ki towards superoxide radical was observed. Computer-assistant molecular modeling demonstrated that the reactivity towards superoxide is sensitive to changes in dipole moment which are dictated not only by the number of carboxyl groups, but also by their distribution on the fullerene ball. These results indicate that the SOD activity of these cell-permeable compounds predicts neuroprotection, and establishes a structure-activity relationship to aid in future studies on the biology of superoxide across disciplines. PMID:18656425

A Protein in the palm of your hand through augmented reality.

PubMed

Berry, Colin; Board, Jason

2014-01-01

Understanding of proteins and other biological macromolecules must be based on an appreciation of their 3-dimensional shape and the fine details of their structure. Conveying these details in a clear and stimulating fashion can present challenges using conventional approaches and 2-dimensional monitors and projectors. Here we describe a method for the production of 3-D interactive images of protein structures that can be manipulated in real time through the use of augmented reality software. Users first see a real-time image of themselves using the computer's camera, then, when they hold up a trigger image, a model of a molecule appears automatically in the video. This model rotates and translates in space in response to movements of the trigger card. The system described has been optimized to allow customization for the display of user-selected structures to create engaging, educational visualizations to explore 3-D structures. Copyright © 2014 The International Union of Biochemistry and Molecular Biology.

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

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…

Learning a Generative Probabilistic Grammar of Experience: A Process-Level Model of Language Acquisition

ERIC Educational Resources Information Center

Kolodny, Oren; Lotem, Arnon; Edelman, Shimon

2015-01-01

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given…

A Computer Lab Exploring Evolutionary Aspects of Chromatin Structure and Dynamics for an Undergraduate Chromatin Course

ERIC Educational Resources Information Center

Eirin-Lopez, Jose M.

2013-01-01

The study of chromatin constitutes one of the most active research fields in life sciences, being subject to constant revisions that continuously redefine the state of the art in its knowledge. As every other rapidly changing field, chromatin biology requires clear and straightforward educational strategies able to efficiently translate such a…

Legacy effects in material flux: structural catchment changes predate long-term studies

Treesearch

Daniel Bain; Mark B. Green; John L. Campbell; John F. Chamblee; Sayo Chaoka; Jennifer M. Fraterrigo; Sujay S. Kaushal; Sujay S. Kaushal; Sherry L. Martin; Thomas E. Jordan; Anthony J. Parolari; William V. Sobczak; Donald E. Weller; Wilfred M. Wolheim; Emery R. Boose; Jonathan M. Duncan; Gretchen M. Gettel; Brian R. Hall; Praveen Kumar; Jonathan R. Thompson; James M. Vose; Emily M. Elliott; David S. Leigh

2012-01-01

Legacy effects of past land use and disturbance are increasingly recognized, yet consistent definitions of and criteria for defining them do not exist. To address this gap in biological- and ecosystem-assessment frameworks, we propose a general metric for evaluating potential legacy effects, which are computed by normalizing altered system function persistence with...

Eppur Si Muove! The 2013 Nobel Prize in Chemistry

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

Smith, Jeremy C.; Roux, Benoit

2013-12-03

The 2013 Nobel Prize in Chemistry has been awarded to Martin Karplus, Michael Levitt, and Arieh Warshel for their work on developing computational methods to study complex chemical systems. Hence, their work has led to mechanistic critical insights into chemical systems both large and small and has enabled progress in a number of different fields, including structural biology.

Investigating Hydrogen Bonding in Phenol Using Infrared Spectroscopy and Computational Chemistry

ERIC Educational Resources Information Center

Fedor, Anna M.; Toda, Megan J.

2014-01-01

The hydrogen bonding of phenol can be used as an introductory model for biological systems because of its structural similarities to tyrosine, a para-substituted phenol that is an amino acid essential to the synthesis of proteins. Phenol is able to form hydrogen bonds readily in solution, which makes it a suitable model for biological…

Inferring the origin of populations introduced from a genetically structured native range by approximate Bayesian computation: case study of the invasive ladybird Harmonia axyridis

USDA-ARS?s Scientific Manuscript database

The correct identification of the source population of an invasive species is a prerequisite for defining and testing different hypotheses concerning the environmental and evolutionary factors responsible for biological invasions. The native area of invasive species may be large, barely known and/or...

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

PubMed

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

PubMed Central

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

Computing the structural influence matrix for biological systems.

PubMed

Giordano, Giulia; Cuba Samaniego, Christian; Franco, Elisa; Blanchini, Franco

2016-06-01

We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.

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

Beauchamp, R.O. Jr.

A preliminary examination of chemical-substructure analysis (CSA) demonstrates the effective use of the Chemical Abstracts compound connectivity file in conjunction with the bibliographic file for relating chemical structures to biological activity. The importance of considering the role of metabolic intermediates under a variety of conditions is illustrated, suggesting structures that should be examined that may exhibit potential activity. This CSA technique, which utilizes existing large files accessible with online personal computers, is recommended for use as another tool in examining chemicals in drugs. 2 refs., 4 figs.

Synthetic Analog and Digital Circuits for Cellular Computation and Memory

PubMed Central

Purcell, Oliver; Lu, Timothy K.

2014-01-01

Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss recent progress in designing gene circuits that exhibit memory, and how memory and computation have been integrated to yield more complex systems that can both process and record information. Finally, we suggest new directions for engineering biological circuits capable of computation. PMID:24794536

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

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction.

PubMed

Boniecki, Michal J; Lach, Grzegorz; Dawson, Wayne K; Tomala, Konrad; Lukasz, Pawel; Soltysinski, Tomasz; Rother, Kristian M; Bujnicki, Janusz M

2016-04-20

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Thermodynamic characterization of networks using graph polynomials

NASA Astrophysics Data System (ADS)

Ye, Cheng; Comin, César H.; Peron, Thomas K. DM.; Silva, Filipi N.; Rodrigues, Francisco A.; Costa, Luciano da F.; Torsello, Andrea; Hancock, Edwin R.

2015-09-01

In this paper, we present a method for characterizing the evolution of time-varying complex networks by adopting a thermodynamic representation of network structure computed from a polynomial (or algebraic) characterization of graph structure. Commencing from a representation of graph structure based on a characteristic polynomial computed from the normalized Laplacian matrix, we show how the polynomial is linked to the Boltzmann partition function of a network. This allows us to compute a number of thermodynamic quantities for the network, including the average energy and entropy. Assuming that the system does not change volume, we can also compute the temperature, defined as the rate of change of entropy with energy. All three thermodynamic variables can be approximated using low-order Taylor series that can be computed using the traces of powers of the Laplacian matrix, avoiding explicit computation of the normalized Laplacian spectrum. These polynomial approximations allow a smoothed representation of the evolution of networks to be constructed in the thermodynamic space spanned by entropy, energy, and temperature. We show how these thermodynamic variables can be computed in terms of simple network characteristics, e.g., the total number of nodes and node degree statistics for nodes connected by edges. We apply the resulting thermodynamic characterization to real-world time-varying networks representing complex systems in the financial and biological domains. The study demonstrates that the method provides an efficient tool for detecting abrupt changes and characterizing different stages in network evolution.

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.

A dictionary based informational genome analysis

PubMed Central

2012-01-01

Background In the post-genomic era several methods of computational genomics are emerging to understand how the whole information is structured within genomes. Literature of last five years accounts for several alignment-free methods, arisen as alternative metrics for dissimilarity of biological sequences. Among the others, recent approaches are based on empirical frequencies of DNA k-mers in whole genomes. Results Any set of words (factors) occurring in a genome provides a genomic dictionary. About sixty genomes were analyzed by means of informational indexes based on genomic dictionaries, where a systemic view replaces a local sequence analysis. A software prototype applying a methodology here outlined carried out some computations on genomic data. We computed informational indexes, built the genomic dictionaries with different sizes, along with frequency distributions. The software performed three main tasks: computation of informational indexes, storage of these in a database, index analysis and visualization. The validation was done by investigating genomes of various organisms. A systematic analysis of genomic repeats of several lengths, which is of vivid interest in biology (for example to compute excessively represented functional sequences, such as promoters), was discussed, and suggested a method to define synthetic genetic networks. Conclusions We introduced a methodology based on dictionaries, and an efficient motif-finding software application for comparative genomics. This approach could be extended along many investigation lines, namely exported in other contexts of computational genomics, as a basis for discrimination of genomic pathologies. PMID:22985068

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.

Scanning transmission ion micro-tomography (STIM-T) of biological specimens.

PubMed

Schwertner, Micheal; Sakellariou, Arthur; Reinert, Tilo; Butz, Tilman

2006-05-01

Computed tomography (CT) was applied to sets of Scanning Transmission Ion Microscopy (STIM) projections recorded at the LIPSION ion beam laboratory (Leipzig) in order to visualize the 3D-mass distribution in several specimens. Examples for a test structure (copper grid) and for biological specimens (cartilage cells, cygospore) are shown. Scanning Transmission Micro-Tomography (STIM-T) at a resolution of 260 nm was demonstrated for the first time. Sub-micron features of the Cu-grid specimen were verified by scanning electron microscopy. The ion energy loss measured during a STIM-T experiment is related to the mass density of the specimen. Typically, biological specimens can be analysed without staining. Only shock freezing and freeze-drying is required to preserve the ultra-structure of the specimen. The radiation damage to the specimen during the experiment can be neglected. This is an advantage compared to other techniques like X-ray micro-tomography. At present, the spatial resolution is limited by beam position fluctuations and specimen vibrations.

LXtoo: an integrated live Linux distribution for the bioinformatics community

PubMed Central

2012-01-01

Background Recent advances in high-throughput technologies dramatically increase biological data generation. However, many research groups lack computing facilities and specialists. This is an obstacle that remains to be addressed. Here, we present a Linux distribution, LXtoo, to provide a flexible computing platform for bioinformatics analysis. Findings Unlike most of the existing live Linux distributions for bioinformatics limiting their usage to sequence analysis and protein structure prediction, LXtoo incorporates a comprehensive collection of bioinformatics software, including data mining tools for microarray and proteomics, protein-protein interaction analysis, and computationally complex tasks like molecular dynamics. Moreover, most of the programs have been configured and optimized for high performance computing. Conclusions LXtoo aims to provide well-supported computing environment tailored for bioinformatics research, reducing duplication of efforts in building computing infrastructure. LXtoo is distributed as a Live DVD and freely available at http://bioinformatics.jnu.edu.cn/LXtoo. PMID:22813356

LXtoo: an integrated live Linux distribution for the bioinformatics community.

PubMed

Yu, Guangchuang; Wang, Li-Gen; Meng, Xiao-Hua; He, Qing-Yu

2012-07-19

Recent advances in high-throughput technologies dramatically increase biological data generation. However, many research groups lack computing facilities and specialists. This is an obstacle that remains to be addressed. Here, we present a Linux distribution, LXtoo, to provide a flexible computing platform for bioinformatics analysis. Unlike most of the existing live Linux distributions for bioinformatics limiting their usage to sequence analysis and protein structure prediction, LXtoo incorporates a comprehensive collection of bioinformatics software, including data mining tools for microarray and proteomics, protein-protein interaction analysis, and computationally complex tasks like molecular dynamics. Moreover, most of the programs have been configured and optimized for high performance computing. LXtoo aims to provide well-supported computing environment tailored for bioinformatics research, reducing duplication of efforts in building computing infrastructure. LXtoo is distributed as a Live DVD and freely available at http://bioinformatics.jnu.edu.cn/LXtoo.

A spectral method to detect community structure based on distance modularity matrix

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

Computational Medicine: Translating Models to Clinical Care

PubMed Central

Winslow, Raimond L.; Trayanova, Natalia; Geman, Donald; Miller, Michael I.

2013-01-01

Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health. PMID:23115356

Hierarchy, determinism, and specificity in theories of development and evolution.

PubMed

Deichmann, Ute

2017-10-16

The concepts of hierarchical organization, genetic determinism and biological specificity (for example of species, biologically relevant macromolecules, or genes) have played a crucial role in biology as a modern experimental science since its beginnings in the nineteenth century. The idea of genetic information (specificity) and genetic determination was at the basis of molecular biology that developed in the 1940s with macromolecules, viruses and prokaryotes as major objects of research often labelled "reductionist". However, the concepts have been marginalized or rejected in some of the research that in the late 1960s began to focus additionally on the molecularization of complex biological structures and functions using systems approaches. This paper challenges the view that 'molecular reductionism' has been successfully replaced by holism and a focus on the collective behaviour of cellular entities. It argues instead that there are more fertile replacements for molecular 'reductionism', in which genomics, embryology, biochemistry, and computer science intertwine and result in research that is as exact and causally predictive as earlier molecular biology.

Computational biology approach to uncover hepatitis C virus helicase operation.

PubMed

Flechsig, Holger

2014-04-07

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

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.

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 programs made possible by the CCB from its inception until August, 2010, at the end of the final extension. Although DOE support for the center ended in August 2010, the CCB will continue to exist and support its original objectives. The research and academic programs fostered by the CCB have led to additional extramural funding from other agencies, and we anticipate that CCB will continue to provide support for quantitative and computational biology program at UC Merced for many years to come. Since its inception in fall 2004, CCB research projects have continuously had a multi-institutional collaboration with Lawrence Livermore National Laboratory (LLNL), and the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, as well as individual collaborators at other sites. CCB affiliated faculty cover a broad range of computational and mathematical research including molecular modeling, cell biology, applied math, evolutional biology, bioinformatics, etc. The CCB sponsored the first distinguished speaker series at UC Merced, which had an important role is spreading the word about the computational biology emphasis at this new campus. One of CCB's original goals is to help train a new generation of biologists who bridge the gap between the computational and life sciences. To archive this goal, by summer 2006, a new program - summer undergraduate internship program, have been established under CCB to train the highly mathematical and computationally intensive Biological Science researchers. By the end of summer 2010, 44 undergraduate students had gone through this program. Out of those participants, 11 students have been admitted to graduate schools and 10 more students are interested in pursuing graduate studies in the sciences. The center is also continuing to facilitate the development and dissemination of undergraduate and graduate course materials based on the latest research in computational biology.« less

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.

Modelling protein functional domains in signal transduction using Maude

NASA Technical Reports Server (NTRS)

Sriram, M. G.

2003-01-01

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

Applications of NMR and computational methodologies to study protein dynamics.

PubMed

Narayanan, Chitra; Bafna, Khushboo; Roux, Louise D; Agarwal, Pratul K; Doucet, Nicolas

2017-08-15

Overwhelming evidence now illustrates the defining role of atomic-scale protein flexibility in biological events such as allostery, cell signaling, and enzyme catalysis. Over the years, spin relaxation nuclear magnetic resonance (NMR) has provided significant insights on the structural motions occurring on multiple time frames over the course of a protein life span. The present review article aims to illustrate to the broader community how this technique continues to shape many areas of protein science and engineering, in addition to being an indispensable tool for studying atomic-scale motions and functional characterization. Continuing developments in underlying NMR technology alongside software and hardware developments for complementary computational approaches now enable methodologies to routinely provide spatial directionality and structural representations traditionally harder to achieve solely using NMR spectroscopy. In addition to its well-established role in structural elucidation, we present recent examples that illustrate the combined power of selective isotope labeling, relaxation dispersion experiments, chemical shift analyses, and computational approaches for the characterization of conformational sub-states in proteins and enzymes. Copyright © 2017 Elsevier Inc. All rights reserved.

The hair follicle enigma.

PubMed

Bernard, Bruno A

2017-06-01

The hair follicle is a mini-organ endowed with a unique structure and cyclic behaviour. Despite the intense research efforts which have been devoted at deciphering the hair follicle biology over the past 70 years, one must admit that hair follicle remains an enigma. In this brief review, various aspects of hair follicle biology will be addressed, and more importantly, unsolved questions and new possible research tracks will be highlighted, including hair follicle glycobiology and exosome-mediated cell-cell interactions. Even though bricks of knowledge are solidly being acquired, an integrative picture remains to emerge. One can predict that computer science, algorithms and bioinformatics will assist in fostering our understanding hair biology. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

PubMed Central

2011-01-01

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

Compact VLSI neural computer integrated with active pixel sensor for real-time ATR applications

NASA Astrophysics Data System (ADS)

Fang, Wai-Chi; Udomkesmalee, Gabriel; Alkalai, Leon

1997-04-01

A compact VLSI neural computer integrated with an active pixel sensor has been under development to mimic what is inherent in biological vision systems. This electronic eye- brain computer is targeted for real-time machine vision applications which require both high-bandwidth communication and high-performance computing for data sensing, synergy of multiple types of sensory information, feature extraction, target detection, target recognition, and control functions. The neural computer is based on a composite structure which combines Annealing Cellular Neural Network (ACNN) and Hierarchical Self-Organization Neural Network (HSONN). The ACNN architecture is a programmable and scalable multi- dimensional array of annealing neurons which are locally connected with their local neurons. Meanwhile, the HSONN adopts a hierarchical structure with nonlinear basis functions. The ACNN+HSONN neural computer is effectively designed to perform programmable functions for machine vision processing in all levels with its embedded host processor. It provides a two order-of-magnitude increase in computation power over the state-of-the-art microcomputer and DSP microelectronics. A compact current-mode VLSI design feasibility of the ACNN+HSONN neural computer is demonstrated by a 3D 16X8X9-cube neural processor chip design in a 2-micrometers CMOS technology. Integration of this neural computer as one slice of a 4'X4' multichip module into the 3D MCM based avionics architecture for NASA's New Millennium Program is also described.

Synthetic analog and digital circuits for cellular computation and memory.

PubMed

Purcell, Oliver; Lu, Timothy K

2014-10-01

Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss recent progress in designing gene networks that exhibit memory, and how memory and computation have been integrated to yield more complex systems that can both process and record information. Finally, we suggest new directions for engineering biological circuits capable of computation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling of Receptor Tyrosine Kinase Signaling: Computational and Experimental Protocols.

PubMed

Fey, Dirk; Aksamitiene, Edita; Kiyatkin, Anatoly; Kholodenko, Boris N

2017-01-01

The advent of systems biology has convincingly demonstrated that the integration of experiments and dynamic modelling is a powerful approach to understand the cellular network biology. Here we present experimental and computational protocols that are necessary for applying this integrative approach to the quantitative studies of receptor tyrosine kinase (RTK) signaling networks. Signaling by RTKs controls multiple cellular processes, including the regulation of cell survival, motility, proliferation, differentiation, glucose metabolism, and apoptosis. We describe methods of model building and training on experimentally obtained quantitative datasets, as well as experimental methods of obtaining quantitative dose-response and temporal dependencies of protein phosphorylation and activities. The presented methods make possible (1) both the fine-grained modeling of complex signaling dynamics and identification of salient, course-grained network structures (such as feedback loops) that bring about intricate dynamics, and (2) experimental validation of dynamic models.

Art in Science Competition invites artworks to the annual exhibition on ISMB 2018 in Chicago.

PubMed

Welch, Lonnie; Gaeta, Bruno; Kovats, Diane E; Frenkel Morgenstern, Milana

2018-01-01

The International Society of Computational Biology and Bioinformatics (ISCB) brings together scientists from a wide range of disciplines, including biology, medicine, computer science, mathematics and statistics. Practitioners in these fields are constantly dealing with information in visual form: from microscope images and photographs of gels to scatter plots, network graphs and phylogenetic trees, structural formulae and protein models to flow diagrams, visual aids for problem-solving are omnipresent. The ISCB Art in Science Competition 2017 at the ISCB/ECCB 2017 conference in Prague offered a way to show the beauty of science in art form. Past artworks in this annual exhibition at ISMB combined outstanding beauty and aesthetics with deep insight that perfectly validated the exhibit's approach or went beyond the problem's solution. Others were surprising and inspiring through the transition from science to art, opening eyes and minds to reflect on the work being undertaken.

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

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

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

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.

The Quality and Validation of Structures from Structural Genomics

PubMed Central

Domagalski, Marcin J.; Zheng, Heping; Zimmerman, Matthew D.; Dauter, Zbigniew; Wlodawer, Alexander; Minor, Wladek

2014-01-01

Quality control of three-dimensional structures of macromolecules is a critical step to ensure the integrity of structural biology data, especially those produced by structural genomics centers. Whereas the Protein Data Bank (PDB) has proven to be a remarkable success overall, the inconsistent quality of structures reveals a lack of universal standards for structure/deposit validation. Here, we review the state-of-the-art methods used in macromolecular structure validation, focusing on validation of structures determined by X-ray crystallography. We describe some general protocols used in the rebuilding and re-refinement of problematic structural models. We also briefly discuss some frontier areas of structure validation, including refinement of protein–ligand complexes, automation of structure redetermination, and the use of NMR structures and computational models to solve X-ray crystal structures by molecular replacement. PMID:24203341

De Novo Proteins with Life-Sustaining Functions Are Structurally Dynamic.

PubMed

Murphy, Grant S; Greisman, Jack B; Hecht, Michael H

2016-01-29

Designing and producing novel proteins that fold into stable structures and provide essential biological functions are key goals in synthetic biology. In initial steps toward achieving these goals, we constructed a combinatorial library of de novo proteins designed to fold into 4-helix bundles. As described previously, screening this library for sequences that function in vivo to rescue conditionally lethal mutants of Escherichia coli (auxotrophs) yielded several de novo sequences, termed SynRescue proteins, which rescued four different E. coli auxotrophs. In an effort to understand the structural requirements necessary for auxotroph rescue, we investigated the biophysical properties of the SynRescue proteins, using both computational and experimental approaches. Results from circular dichroism, size-exclusion chromatography, and NMR demonstrate that the SynRescue proteins are α-helical and relatively stable. Surprisingly, however, they do not form well-ordered structures. Instead, they form dynamic structures that fluctuate between monomeric and dimeric states. These findings show that a well-ordered structure is not a prerequisite for life-sustaining functions, and suggests that dynamic structures may have been important in the early evolution of protein function. Copyright © 2015 Elsevier Ltd. All rights reserved.

Vibrational Spectroscopy and Astrobiology

NASA Technical Reports Server (NTRS)

Chaban, Galina M.; Kwak, D. (Technical Monitor)

2001-01-01

Role of vibrational spectroscopy in solving problems related to astrobiology will be discussed. Vibrational (infrared) spectroscopy is a very sensitive tool for identifying molecules. Theoretical approach used in this work is based on direct computation of anharmonic vibrational frequencies and intensities from electronic structure codes. One of the applications of this computational technique is possible identification of biological building blocks (amino acids, small peptides, DNA bases) in the interstellar medium (ISM). Identifying small biological molecules in the ISM is very important from the point of view of origin of life. Hybrid (quantum mechanics/molecular mechanics) theoretical techniques will be discussed that may allow to obtain accurate vibrational spectra of biomolecular building blocks and to create a database of spectroscopic signatures that can assist observations of these molecules in space. Another application of the direct computational spectroscopy technique is to help to design and analyze experimental observations of ice surfaces of one of the Jupiter's moons, Europa, that possibly contains hydrated salts. The presence of hydrated salts on the surface can be an indication of a subsurface ocean and the possible existence of life forms inhabiting such an ocean.

A random walk in physical biology

NASA Astrophysics Data System (ADS)

Peterson, Eric Lee

Biology as a scientific discipline is becoming evermore quantitative as tools become available to probe living systems on every scale from the macro to the micro and now even to the nanoscale. In quantitative biology the challenge is to understand the living world in an in vivo context, where it is often difficult for simple theoretical models to connect with the full richness and complexity of the observed data. Computational models and simulations offer a way to bridge the gap between simple theoretical models and real biological systems; towards that aspiration are presented in this thesis three case studies in applying computational models that may give insight into native biological structures.The first is concerned with soluble proteins; proteins, like DNA, are linear polymers written in a twenty-letter "language" of amino acids. Despite the astronomical number of possible proteins sequences, a great amount of similarity is observed among the folded structures of globular proteins. One useful way of discovering similar sequences is to align their sequences, as done e.g. by the popular BLAST program. By clustering together amino acids and reducing the alphabet that proteins are written in to fewer than twenty letters, we find that pairwise sequence alignments are actually more sensitive to proteins with similar structures.The second case study is concerned with the measurement of forces applied to a membrane. We demonstrate a general method for extracting the forces applied to a fluid lipid bilayer of arbitrary shape and show that the subpiconewton forces applied by optical tweezers to vesicles can be accurately measured in this way.In the third and final case study we examine the forces between proteins in a lipid bilayer membrane. Due to the bending of the membrane surrounding them, such proteins feel mutually attractive forces which can help them to self-organize and act in concert. These finding are relevant at the areal densities estimated for membrane proteins such as the MscL mechanosensitive channel. The findings of the analytical studies were confirmed by a Monte Carlo Markov Chain simulation using the fully two-dimensional potentials between two model proteins in a membrane.Living systems present us with beautiful and intricate structures, from the helices and sheets of a folded protein to the dynamic morphology of cellular organelles and the self-organization of proteins in a biomembrane and a synergy of theoretical and it in silico approaches should enable us to build and refine models of in vivo biological data.

3D printing of novel osteochondral scaffolds with graded microstructure

NASA Astrophysics Data System (ADS)

Nowicki, Margaret A.; Castro, Nathan J.; Plesniak, Michael W.; Zhang, Lijie Grace

2016-10-01

Osteochondral tissue has a complex graded structure where biological, physiological, and mechanical properties vary significantly over the full thickness spanning from the subchondral bone region beneath the joint surface to the hyaline cartilage region at the joint surface. This presents a significant challenge for tissue-engineered structures addressing osteochondral defects. Fused deposition modeling (FDM) 3D bioprinters present a unique solution to this problem. The objective of this study is to use FDM-based 3D bioprinting and nanocrystalline hydroxyapatite for improved bone marrow human mesenchymal stem cell (hMSC) adhesion, growth, and osteochondral differentiation. FDM printing parameters can be tuned through computer aided design and computer numerical control software to manipulate scaffold geometries in ways that are beneficial to mechanical performance without hindering cellular behavior. Additionally, the ability to fine-tune 3D printed scaffolds increases further through our investment casting procedure which facilitates the inclusion of nanoparticles with biochemical factors to further elicit desired hMSC differentiation. For this study, FDM was used to print investment-casting molds innovatively designed with varied pore distribution over the full thickness of the scaffold. The mechanical and biological impacts of the varied pore distributions were compared and evaluated to determine the benefits of this physical manipulation. The results indicate that both mechanical properties and cell performance improve in the graded pore structures when compared to homogeneously distributed porous and non-porous structures. Differentiation results indicated successful osteogenic and chondrogenic manipulation in engineered scaffolds.

Basic Research Plan, February 2003

DTIC Science & Technology

2003-02-01

consistent. This effort includes the nitration , crystallization, and coating of CL–20. Under Army sponsor- ship, a process for the nitration of CL–20 has...actuators • Multiscale computational design of structural materials with embedded functionality • Materials with embedded electrical/magnetic/optical...the innovative use of biology to produce unique materials and processes of mili- tary relevance; to increase economic and environmental affordability

An Examination of Problem-Based Teaching and Learning in Population Genetics and Evolution Using EVOLVE, a Computer Simulation.

ERIC Educational Resources Information Center

Soderberg, Patti; Price, Frank

2003-01-01

Examines a lesson in which students are engaged in inquiry in evolutionary biology to develop better understanding of concepts and reasoning skills necessary to support knowledge claims about changes in the genetic structure of populations known as microevolution. Explains how a software simulation, EVOLVE, can be used to foster discussions about…

Developing PFC representations using reinforcement learning.

PubMed

Reynolds, Jeremy R; O'Reilly, Randall C

2009-12-01

From both functional and biological considerations, it is widely believed that action production, planning, and goal-oriented behaviors supported by the frontal cortex are organized hierarchically [Fuster (1991); Koechlin, E., Ody, C., & Kouneiher, F. (2003). Neuroscience: The architecture of cognitive control in the human prefrontal cortex. Science, 424, 1181-1184; Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York: Holt]. However, the nature of the different levels of the hierarchy remains unclear, and little attention has been paid to the origins of such a hierarchy. We address these issues through biologically-inspired computational models that develop representations through reinforcement learning. We explore several different factors in these models that might plausibly give rise to a hierarchical organization of representations within the PFC, including an initial connectivity hierarchy within PFC, a hierarchical set of connections between PFC and subcortical structures controlling it, and differential synaptic plasticity schedules. Simulation results indicate that architectural constraints contribute to the segregation of different types of representations, and that this segregation facilitates learning. These findings are consistent with the idea that there is a functional hierarchy in PFC, as captured in our earlier computational models of PFC function and a growing body of empirical data.

DelPhi webserver: Comprehensive suite for electrostatic calculations of biological macromolecules and their complexes

NASA Astrophysics Data System (ADS)

Witham, Shawn; Boylen, Brett; Owesen, Barr; Rocchia, Walter; Alexov, Emil

2011-03-01

Electrostatic forces and energies are two of the major components that contribute to the stability, function and interaction of biological macromolecules. The calculations of the electrostatic potential distribution in such systems, which are comprised of irregularly shaped objects immersed in a water phase, is not a trivial task. In addition, an accurate model requires any missing hydrogen atoms of the corresponding structural files (Protein Data Bank, or, PDB files) to be generated in silico and, if necessary, missing atoms or residues to be predicted as well. Here we report a comprehensive suite, an academic DelPhi webserver, which allows the users to upload their structural file, calculate the components of the electrostatic energy, generate the corresponding potential (and/or concentration/dielectric constant) distribution map, and choose the appropriate force field. The webserver utilizes modern technology to take user input and construct an algorithm that suits the users specific needs. The webserver uses Clemson University's Palmetto Supercomputer Cluster to handle the DelPhi calculations, which can range anywhere from small and short computation times, to extensive and computationally demanding runtimes. The work was supported by a grant from NIGMS, NIH, grant number 1R01GM093937-01.

Plant Identification Based on Leaf Midrib Cross-Section Images Using Fractal Descriptors.

PubMed

da Silva, Núbia Rosa; Florindo, João Batista; Gómez, María Cecilia; Rossatto, Davi Rodrigo; Kolb, Rosana Marta; Bruno, Odemir Martinez

2015-01-01

The correct identification of plants is a common necessity not only to researchers but also to the lay public. Recently, computational methods have been employed to facilitate this task, however, there are few studies front of the wide diversity of plants occurring in the world. This study proposes to analyse images obtained from cross-sections of leaf midrib using fractal descriptors. These descriptors are obtained from the fractal dimension of the object computed at a range of scales. In this way, they provide rich information regarding the spatial distribution of the analysed structure and, as a consequence, they measure the multiscale morphology of the object of interest. In Biology, such morphology is of great importance because it is related to evolutionary aspects and is successfully employed to characterize and discriminate among different biological structures. Here, the fractal descriptors are used to identify the species of plants based on the image of their leaves. A large number of samples are examined, being 606 leaf samples of 50 species from Brazilian flora. The results are compared to other imaging methods in the literature and demonstrate that fractal descriptors are precise and reliable in the taxonomic process of plant species identification.

Structural Identifiability of Dynamic Systems Biology Models

PubMed Central

Villaverde, Alejandro F.

2016-01-01

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas. PMID:27792726

A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

PubMed

de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

2018-07-01

The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis. Published by Elsevier Ltd.

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…

Hidden relationships between metalloproteins unveiled by structural comparison of their metal sites

NASA Astrophysics Data System (ADS)

Valasatava, Yana; Andreini, Claudia; Rosato, Antonio

2015-03-01

Metalloproteins account for a substantial fraction of all proteins. They incorporate metal atoms, which are required for their structure and/or function. Here we describe a new computational protocol to systematically compare and classify metal-binding sites on the basis of their structural similarity. These sites are extracted from the MetalPDB database of minimal functional sites (MFSs) in metal-binding biological macromolecules. Structural similarity is measured by the scoring function of the available MetalS2 program. Hierarchical clustering was used to organize MFSs into clusters, for each of which a representative MFS was identified. The comparison of all representative MFSs provided a thorough structure-based classification of the sites analyzed. As examples, the application of the proposed computational protocol to all heme-binding proteins and zinc-binding proteins of known structure highlighted the existence of structural subtypes, validated known evolutionary links and shed new light on the occurrence of similar sites in systems at different evolutionary distances. The present approach thus makes available an innovative viewpoint on metalloproteins, where the functionally crucial metal sites effectively lead the discovery of structural and functional relationships in a largely protein-independent manner.

Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach.

PubMed

Ng, Clara; Hauptman, Ruth; Zhang, Yinliang; Bourne, Philip E; Xie, Lei

2014-01-01

The emergence of multi-drug and extensive drug resistance of microbes to antibiotics poses a great threat to human health. Although drug repurposing is a promising solution for accelerating the drug development process, its application to anti-infectious drug discovery is limited by the scope of existing phenotype-, ligand-, or target-based methods. In this paper we introduce a new computational strategy to determine the genome-wide molecular targets of bioactive compounds in both human and bacterial genomes. Our method is based on the use of a novel algorithm, ligand Enrichment of Network Topological Similarity (ligENTS), to map the chemical universe to its global pharmacological space. ligENTS outperforms the state-of-the-art algorithms in identifying novel drug-target relationships. Furthermore, we integrate ligENTS with our structural systems biology platform to identify drug repurposing opportunities via target similarity profiling. Using this integrated strategy, we have identified novel P. falciparum targets of drug-like active compounds from the Malaria Box, and suggest that a number of approved drugs may be active against malaria. This study demonstrates the potential of an integrative chemical genomics and structural systems biology approach to drug repurposing.

Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh.

PubMed

Rizk, Aurélien; Paul, Grégory; Incardona, Pietro; Bugarski, Milica; Mansouri, Maysam; Niemann, Axel; Ziegler, Urs; Berger, Philipp; Sbalzarini, Ivo F

2014-03-01

Detection and quantification of fluorescently labeled molecules in subcellular compartments is a key step in the analysis of many cell biological processes. Pixel-wise colocalization analyses, however, are not always suitable, because they do not provide object-specific information, and they are vulnerable to noise and background fluorescence. Here we present a versatile protocol for a method named 'Squassh' (segmentation and quantification of subcellular shapes), which is used for detecting, delineating and quantifying subcellular structures in fluorescence microscopy images. The workflow is implemented in freely available, user-friendly software. It works on both 2D and 3D images, accounts for the microscope optics and for uneven image background, computes cell masks and provides subpixel accuracy. The Squassh software enables both colocalization and shape analyses. The protocol can be applied in batch, on desktop computers or computer clusters, and it usually requires <1 min and <5 min for 2D and 3D images, respectively. Basic computer-user skills and some experience with fluorescence microscopy are recommended to successfully use the protocol.

D2N: Distance to the native.

PubMed

Mishra, Avinash; Rana, Prashant Singh; Mittal, Aditya; Jayaram, B

2014-10-01

Root-mean-square-deviation (RMSD), of computationally-derived protein structures from experimentally determined structures, is a critical index to assessing protein-structure-prediction-algorithms (PSPAs). The development of PSPAs to obtain 0Å RMSD from native structures is considered central to computational biology. However, till date it has been quite challenging to measure how far a predicted protein structure is from its native - in the absence of a known experimental/native structure. In this work, we report the development of a metric "D2N" (distance to the native) - that predicts the "RMSD" of any structure without actually knowing the native structure. By combining physico-chemical properties and known universalities in spatial organization of soluble proteins to develop D2N, we demonstrate the ability to predict the distance of a proposed structure to within ±1.5Ǻ error with a remarkable average accuracy of 93.6% for structures below 5Ǻ from the native. We believe that this work opens up a completely new avenue towards assigning reliable structures to whole proteomes even in the absence of experimentally determined native structures. The D2N tool is freely available at http://www.scfbio-iitd.res.in/software/d2n.jsp. Copyright © 2014 Elsevier B.V. All rights reserved.

A new graph-based method for pairwise global network alignment

PubMed Central

Klau, Gunnar W

2009-01-01

Background In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature. Results We introduce the graph-based maximum structural matching formulation for pairwise global network alignment. We relate the formulation to previous work and prove NP-hardness of the problem. Based on the new formulation we build upon recent results in computational structural biology and present a novel Lagrangian relaxation approach that, in combination with a branch-and-bound method, computes provably optimal network alignments. The Lagrangian algorithm alone is a powerful heuristic method, which produces solutions that are often near-optimal and – unlike those computed by pure heuristics – come with a quality guarantee. Conclusion Computational experiments on the alignment of protein-protein interaction networks and on the classification of metabolic subnetworks demonstrate that the new method is reasonably fast and has advantages over pure heuristics. Our software tool is freely available as part of the LISA library. PMID:19208162

De novo self-assembling collagen heterotrimers using explicit positive and negative design.

PubMed

Xu, Fei; Zhang, Lei; Koder, Ronald L; Nanda, Vikas

2010-03-23

We sought to computationally design model collagen peptides that specifically associate as heterotrimers. Computational design has been successfully applied to the creation of new protein folds and functions. Despite the high abundance of collagen and its key role in numerous biological processes, fibrous proteins have received little attention as computational design targets. Collagens are composed of three polypeptide chains that wind into triple helices. We developed a discrete computational model to design heterotrimer-forming collagen-like peptides. Stability and specificity of oligomerization were concurrently targeted using a combined positive and negative design approach. The sequences of three 30-residue peptides, A, B, and C, were optimized to favor charge-pair interactions in an ABC heterotrimer, while disfavoring the 26 competing oligomers (i.e., AAA, ABB, BCA). Peptides were synthesized and characterized for thermal stability and triple-helical structure by circular dichroism and NMR. A unique A:B:C-type species was not achieved. Negative design was partially successful, with only A + B and B + C competing mixtures formed. Analysis of computed versus experimental stabilities helps to clarify the role of electrostatics and secondary-structure propensities determining collagen stability and to provide important insight into how subsequent designs can be improved.

A first-principle calculation of the XANES spectrum of Cu2+ in water

NASA Astrophysics Data System (ADS)

La Penna, G.; Minicozzi, V.; Morante, S.; Rossi, G. C.; Stellato, F.

2015-09-01

The progress in high performance computing we are witnessing today offers the possibility of accurate electron density calculations of systems in realistic physico-chemical conditions. In this paper, we present a strategy aimed at performing a first-principle computation of the low energy part of the X-ray Absorption Spectroscopy (XAS) spectrum based on the density functional theory calculation of the electronic potential. To test its effectiveness, we apply the method to the computation of the X-ray absorption near edge structure part of the XAS spectrum in the paradigmatic, but simple case of Cu2+ in water. In order to keep into account the effect of the metal site structure fluctuations in determining the experimental signal, the theoretical spectrum is evaluated as the average over the computed spectra of a statistically significant number of simulated metal site configurations. The comparison of experimental data with theoretical calculations suggests that Cu2+ lives preferentially in a square-pyramidal geometry. The remarkable success of this approach in the interpretation of XAS data makes us optimistic about the possibility of extending the computational strategy we have outlined to the more interesting case of molecules of biological relevance bound to transition metal ions.

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.

Building polyhedra by self-assembly: theory and experiment.

PubMed

Kaplan, Ryan; Klobušický, Joseph; Pandey, Shivendra; Gracias, David H; Menon, Govind

2014-01-01

We investigate the utility of a mathematical framework based on discrete geometry to model biological and synthetic self-assembly. Our primary biological example is the self-assembly of icosahedral viruses; our synthetic example is surface-tension-driven self-folding polyhedra. In both instances, the process of self-assembly is modeled by decomposing the polyhedron into a set of partially formed intermediate states. The set of all intermediates is called the configuration space, pathways of assembly are modeled as paths in the configuration space, and the kinetics and yield of assembly are modeled by rate equations, Markov chains, or cost functions on the configuration space. We review an interesting interplay between biological function and mathematical structure in viruses in light of this framework. We discuss in particular: (i) tiling theory as a coarse-grained description of all-atom models; (ii) the building game-a growth model for the formation of polyhedra; and (iii) the application of these models to the self-assembly of the bacteriophage MS2. We then use a similar framework to model self-folding polyhedra. We use a discrete folding algorithm to compute a configuration space that idealizes surface-tension-driven self-folding and analyze pathways of assembly and dominant intermediates. These computations are then compared with experimental observations of a self-folding dodecahedron with side 300 μm. In both models, despite a combinatorial explosion in the size of the configuration space, a few pathways and intermediates dominate self-assembly. For self-folding polyhedra, the dominant intermediates have fewer degrees of freedom than comparable intermediates, and are thus more rigid. The concentration of assembly pathways on a few intermediates with distinguished geometric properties is biologically and physically important, and suggests deeper mathematical structure.

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)

Geometric modeling of subcellular structures, organelles, and multiprotein complexes

PubMed Central

Feng, Xin; Xia, Kelin; Tong, Yiying; Wei, Guo-Wei

2013-01-01

SUMMARY Recently, the structure, function, stability, and dynamics of subcellular structures, organelles, and multi-protein complexes have emerged as a leading interest in structural biology. Geometric modeling not only provides visualizations of shapes for large biomolecular complexes but also fills the gap between structural information and theoretical modeling, and enables the understanding of function, stability, and dynamics. This paper introduces a suite of computational tools for volumetric data processing, information extraction, surface mesh rendering, geometric measurement, and curvature estimation of biomolecular complexes. Particular emphasis is given to the modeling of cryo-electron microscopy data. Lagrangian-triangle meshes are employed for the surface presentation. On the basis of this representation, algorithms are developed for surface area and surface-enclosed volume calculation, and curvature estimation. Methods for volumetric meshing have also been presented. Because the technological development in computer science and mathematics has led to multiple choices at each stage of the geometric modeling, we discuss the rationales in the design and selection of various algorithms. Analytical models are designed to test the computational accuracy and convergence of proposed algorithms. Finally, we select a set of six cryo-electron microscopy data representing typical subcellular complexes to demonstrate the efficacy of the proposed algorithms in handling biomolecular surfaces and explore their capability of geometric characterization of binding targets. This paper offers a comprehensive protocol for the geometric modeling of subcellular structures, organelles, and multiprotein complexes. PMID:23212797

Automated analysis of biological oscillator models using mode decomposition.

PubMed

Konopka, Tomasz

2011-04-01

Oscillating signals produced by biological systems have shapes, described by their Fourier spectra, that can potentially reveal the mechanisms that generate them. Extracting this information from measured signals is interesting for the validation of theoretical models, discovery and classification of interaction types, and for optimal experiment design. An automated workflow is described for the analysis of oscillating signals. A software package is developed to match signal shapes to hundreds of a priori viable model structures defined by a class of first-order differential equations. The package computes parameter values for each model by exploiting the mode decomposition of oscillating signals and formulating the matching problem in terms of systems of simultaneous polynomial equations. On the basis of the computed parameter values, the software returns a list of models consistent with the data. In validation tests with synthetic datasets, it not only shortlists those model structures used to generate the data but also shows that excellent fits can sometimes be achieved with alternative equations. The listing of all consistent equations is indicative of how further invalidation might be achieved with additional information. When applied to data from a microarray experiment on mice, the procedure finds several candidate model structures to describe interactions related to the circadian rhythm. This shows that experimental data on oscillators is indeed rich in information about gene regulation mechanisms. The software package is available at http://babylone.ulb.ac.be/autoosc/.

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

PubMed Central

2010-01-01

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

Computational Models of Cognitive Control

PubMed Central

O’Reilly, Randall C.; Herd, Seth A.; Pauli, Wolfgang M.

2010-01-01

Cognitive control refers to the ability to perform task-relevant processing in the face of other distractions or other forms of interference, in the absence of strong environmental support. It depends on the integrity of the prefrontal cortex and associated biological structures (e.g., the basal ganglia). Computational models have played an influential role in developing our understanding of this system, and we review current developments in three major areas: dynamic gating of prefrontal representations, hierarchies in the prefrontal cortex, and reward, motivation, and goal-related processing in prefrontal cortex. Models in these and other areas are advancing the field further forward. PMID:20185294

Designer drugs: the evolving science of drug discovery.

PubMed

Wanke, L A; DuBose, R F

1998-07-01

Drug discovery and design are fundamental to drug development. Until recently, most drugs were discovered through random screening or developed through molecular modification. New technologies are revolutionizing this phase of drug development. Rational drug design, using powerful computers and computational chemistry and employing X-ray crystallography, nuclear magnetic resonance spectroscopy, and three-dimensional quantitative structure activity relationship analysis, is creating highly specific, biologically active molecules by virtual reality modeling. Sophisticated screening technologies are eliminating all but the most active lead compounds. These new technologies promise more efficacious, safe, and cost-effective medications, while minimizing drug development time and maximizing profits.

FPGA accelerator for protein secondary structure prediction based on the GOR algorithm

PubMed Central

2011-01-01

Background Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. Results In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330. Conclusions The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs. PMID:21342582

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

Holographic femtosecond laser processing and its application to biological materials (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hayasaki, Yoshio

2017-02-01

Femtosecond laser processing is a promising tool for fabricating novel and useful structures on the surfaces of and inside materials. An enormous number of pulse irradiation points will be required for fabricating actual structures with millimeter scale, and therefore, the throughput of femtosecond laser processing must be improved for practical adoption of this technique. One promising method to improve throughput is parallel pulse generation based on a computer-generated hologram (CGH) displayed on a spatial light modulator (SLM), a technique called holographic femtosecond laser processing. The holographic method has the advantages such as high throughput, high light use efficiency, and variable, instantaneous, and 3D patterning. Furthermore, the use of an SLM gives an ability to correct unknown imperfections of the optical system and inhomogeneity in a sample using in-system optimization of the CGH. Furthermore, the CGH can adaptively compensate in response to dynamic unpredictable mechanical movements, air and liquid disturbances, a shape variation and deformation of the target sample, as well as adaptive wavefront control for environmental changes. Therefore, it is a powerful tool for the fabrication of biological cells and tissues, because they have free form, variable, and deformable structures. In this paper, we present the principle and the experimental setup of holographic femtosecond laser processing, and the effective way for processing the biological sample. We demonstrate the femtosecond laser processing of biological materials and the processing properties.

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.

Examining ion channel properties using free-energy methods.

PubMed

Domene, Carmen; Furini, Simone

2009-01-01

Recent advances in structural biology have revealed the architecture of a number of transmembrane channels, allowing for these complex biological systems to be understood in atomistic detail. Computational simulations are a powerful tool by which the dynamic and energetic properties, and thereby the function of these protein architectures, can be investigated. The experimentally observable properties of a system are often determined more by energetic than dynamics, and therefore understanding the underlying free energy (FE) of biophysical processes is of crucial importance. Critical to the accurate evaluation of FE values are the problems of obtaining accurate sampling of complex biological energy landscapes, and of obtaining accurate representations of the potential energy of a system, this latter problem having been addressed through the development of molecular force fields. While these challenges are common to all FE methods, depending on the system under study, and the questions being asked of it, one technique for FE calculation may be preferable to another, the choice of method and simulation protocol being crucial to achieve efficiency. Applied in a correct manner, FE calculations represent a predictive and affordable computational tool with which to make relevant contact with experiments. This chapter, therefore, aims to give an overview of the most widely implemented computational methods used to calculate the FE associated with particular biochemical or biophysical events, and to highlight their recent applications to ion channels. Copyright © 2009 Elsevier Inc. All rights reserved.

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.

Integrating interactive computational modeling in biology curricula.

PubMed

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

2015-03-01

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

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

PubMed Central

Chen, Jui-Le; Yang, Chu-Sing

2013-01-01

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result. PMID:23762864

Computational modeling of in vitro biological responses on polymethacrylate surfaces

PubMed Central

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

2011-01-01

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

Rule-Based Design of Plant Expression Vectors Using GenoCAD.

PubMed

Coll, Anna; Wilson, Mandy L; Gruden, Kristina; Peccoud, Jean

2015-01-01

Plant synthetic biology requires software tools to assist on the design of complex multi-genic expression plasmids. Here a vector design strategy to express genes in plants is formalized and implemented as a grammar in GenoCAD, a Computer-Aided Design software for synthetic biology. It includes a library of plant biological parts organized in structural categories and a set of rules describing how to assemble these parts into large constructs. Rules developed here are organized and divided into three main subsections according to the aim of the final construct: protein localization studies, promoter analysis and protein-protein interaction experiments. The GenoCAD plant grammar guides the user through the design while allowing users to customize vectors according to their needs. Therefore the plant grammar implemented in GenoCAD will help plant biologists take advantage of methods from synthetic biology to design expression vectors supporting their research projects.

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

Hill, David P.; D’Eustachio, Peter; Berardini, Tanya Z.

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes inmore » the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.« less

High-contrast differentiation resolution 3D imaging of rodent brain by X-ray computed microtomography

NASA Astrophysics Data System (ADS)

Zikmund, T.; Novotná, M.; Kavková, M.; Tesařová, M.; Kaucká, M.; Szarowská, B.; Adameyko, I.; Hrubá, E.; Buchtová, M.; Dražanová, E.; Starčuk, Z.; Kaiser, J.

2018-02-01

The biomedically focused brain research is largely performed on laboratory mice considering a high homology between the human and mouse genomes. A brain has an intricate and highly complex geometrical structure that is hard to display and analyse using only 2D methods. Applying some fast and efficient methods of brain visualization in 3D will be crucial for the neurobiology in the future. A post-mortem analysis of experimental animals' brains usually involves techniques such as magnetic resonance and computed tomography. These techniques are employed to visualize abnormalities in the brains' morphology or reparation processes. The X-ray computed microtomography (micro CT) plays an important role in the 3D imaging of internal structures of a large variety of soft and hard tissues. This non-destructive technique is applied in biological studies because the lab-based CT devices enable to obtain a several-micrometer resolution. However, this technique is always used along with some visualization methods, which are based on the tissue staining and thus differentiate soft tissues in biological samples. Here, a modified chemical contrasting protocol of tissues for a micro CT usage is introduced as the best tool for ex vivo 3D imaging of a post-mortem mouse brain. This way, the micro CT provides a high spatial resolution of the brain microscopic anatomy together with a high tissue differentiation contrast enabling to identify more anatomical details in the brain. As the micro CT allows a consequent reconstruction of the brain structures into a coherent 3D model, some small morphological changes can be given into context of their mutual spatial relationships.

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.

A Practical Approach to Protein Crystallography.

PubMed

Ilari, Andrea; Savino, Carmelinda

2017-01-01

Macromolecular crystallography is a powerful tool for structural biology. The resolution of a protein crystal structure is becoming much easier than in the past, thanks to developments in computing, automation of crystallization techniques and high-flux synchrotron sources to collect diffraction datasets. The aim of this chapter is to provide practical procedures to determine a protein crystal structure, illustrating the new techniques, experimental methods, and software that have made protein crystallography a tool accessible to a larger scientific community.It is impossible to give more than a taste of what the X-ray crystallographic technique entails in one brief chapter and there are different ways to solve a protein structure. Since the number of structures available in the Protein Data Bank (PDB) is becoming ever larger (the protein data bank now contains more than 100,000 entries) and therefore the probability to find a good model to solve the structure is ever increasing, we focus our attention on the Molecular Replacement method. Indeed, whenever applicable, this method allows the resolution of macromolecular structures starting from a single data set and a search model downloaded from the PDB, with the aid only of computer work.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

PubMed Central

Antunes, Deborah; Jorge, Natasha A. N.; Caffarena, Ernesto R.; Passetti, Fabio

2018-01-01

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications. PMID:29403526

Reconstruction of Mammary Gland Structure Using Three-Dimensional Computer-Based Microscopy

DTIC Science & Technology

2001-08-01

Segmentation of Mammary Gland Ductal Structure Using Geometric Methods. P.l.’s Malladi R . and Ortiz de Solorzano C. Submitted to the LBNL Laboratory...mammary gland biology". Fernandez-Gonzalez, R ., Jones A., Garcia-Rodriguez E., Knowles D., Sudar D. Ortiz de Solorzano, C. Proceedings of Microscopy...the text. 25 3DRcn4rclr FieC4 eto m oosOtosMCUCP suto 5 2 3p4 eto 6 ’lw r 26o W ~Fl. Case Section Area Tools Opt~ons Micoscope

Nanotechnology and dentistry

PubMed Central

Ozak, Sule Tugba; Ozkan, Pelin

2013-01-01

Nanotechnology deals with the physical, chemical, and biological properties of structures and their components at nanoscale dimensions. Nanotechnology is based on the concept of creating functional structures by controlling atoms and molecules on a one-by-one basis. The use of this technology will allow many developments in the health sciences as well as in materials science, bio-technology, electronic and computer technology, aviation, and space exploration. With developments in materials science and biotechnology, nanotechnology is especially anticipated to provide advances in dentistry and innovations in oral health-related diagnostic and therapeutic methods. PMID:23408486

Reflectivity of the gyroid biophotonic crystals in the ventral wing scales of the Green Hairstreak butterfly, Callophrys rubi.

PubMed

Michielsen, K; De Raedt, H; Stavenga, D G

2010-05-06

We present a comparison of the computer simulation data of gyroid nanostructures with optical measurements (reflectivity spectra and scattering diagrams) of ventral wing scales of the Green Hairstreak butterfly, Callophrys rubi. We demonstrate that the omnidirectional green colour arises from the gyroid cuticular structure grown in the domains of different orientation. We also show that this three-dimensional structure, operating as a biophotonic crystal, gives rise to various polarization effects. We briefly discuss the possible biological utility of the green coloration and polarization effects.

DECOMP: a PDB decomposition tool on the web.

PubMed

Ordog, Rafael; Szabadka, Zoltán; Grolmusz, Vince

2009-07-27

The protein databank (PDB) contains high quality structural data for computational structural biology investigations. We have earlier described a fast tool (the decomp_pdb tool) for identifying and marking missing atoms and residues in PDB files. The tool also automatically decomposes PDB entries into separate files describing ligands and polypeptide chains. Here, we describe a web interface named DECOMP for the tool. Our program correctly identifies multi-monomer ligands, and the server also offers the preprocessed ligand-protein decomposition of the complete PDB for downloading (up to size: 5GB) AVAILABILITY: http://decomp.pitgroup.org.

Student conceptions about the DNA structure within a hierarchical organizational level: Improvement by experiment- and computer-based outreach learning.

PubMed

Langheinrich, Jessica; Bogner, Franz X

2015-01-01

As non-scientific conceptions interfere with learning processes, teachers need both, to know about them and to address them in their classrooms. For our study, based on 182 eleventh graders, we analyzed the level of conceptual understanding by implementing the "draw and write" technique during a computer-supported gene technology module. To give participants the hierarchical organizational level which they have to draw, was a specific feature of our study. We introduced two objective category systems for analyzing drawings and inscriptions. Our results indicated a long- as well as a short-term increase in the level of conceptual understanding and in the number of drawn elements and their grades concerning the DNA structure. Consequently, we regard the "draw and write" technique as a tool for a teacher to get to know students' alternative conceptions. Furthermore, our study points the modification potential of hands-on and computer-supported learning modules. © 2015 The International Union of Biochemistry and Molecular Biology.

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…

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.

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

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...

Reviews.

ERIC Educational Resources Information Center

Science Teacher, 1989

1989-01-01

Reviews seven software programs: (1) "Science Baseball: Biology" (testing a variety of topics); (2) "Wildways: Understanding Wildlife Conservation"; (3) "Earth Science Computer Test Bank"; (4) "Biology Computer Test Bank"; (5) "Computer Play & Learn Series" (a series of drill and test…

Biomolecular computing systems: principles, progress and potential.

PubMed

Benenson, Yaakov

2012-06-12

The task of information processing, or computation, can be performed by natural and man-made 'devices'. Man-made computers are made from silicon chips, whereas natural 'computers', such as the brain, use cells and molecules. Computation also occurs on a much smaller scale in regulatory and signalling pathways in individual cells and even within single biomolecules. Indeed, much of what we recognize as life results from the remarkable capacity of biological building blocks to compute in highly sophisticated ways. Rational design and engineering of biological computing systems can greatly enhance our ability to study and to control biological systems. Potential applications include tissue engineering and regeneration and medical treatments. This Review introduces key concepts and discusses recent progress that has been made in biomolecular computing.

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

PubMed

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

2018-01-01

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

The biophysical basis of Benveniste experiments: Entropy, structure, and information in water

NASA Astrophysics Data System (ADS)

Widom, Allan; Srivastava, Yogendra; Valenzi, Vincenzo

Benveniste had observed that highly dilute (and even in the absence of physical molecules) biological agents still triggered relevant biological systems. Some of these experiments were reproduced in three other laboratories who cosigned the article, (Davenas et al., Nature 1988, 333, 816). Further works, [(Medical Hypotheses 2000, 54, 33), (Rivista di Biologia/Biology Forum 97, 2004, 169)], showed that molecular activity in more than 50 biochemical systems and even in bacteria could be induced by electromagnetic signals transferred through water solutes. The sources of the electromagnetic signals were recordings of specific biological activities. These results suggest that electromagnetic transmission of biochemical information can be stored in the electric dipole moments of water in close analogy to the manner in which magnetic moments store information on a computer disk. The electromagnetic transmission would enable in vivo transmissions of the specific molecular information between two functional biomolecules. In the present work, the physical nature of such biological information storage and retrieval in ordered quantum electromagnetic domains of water will be discussed.

AN OVERVIEW OF COMPUTATIONAL LIFE SCIENCE DATABASES & EXCHANGE FORMATS OF RELEVANCE TO CHEMICAL BIOLOGY RESEARCH

PubMed Central

Hall, Aaron Smalter; Shan, Yunfeng; Lushington, Gerald; Visvanathan, Mahesh

2016-01-01

Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities. PMID:22934944

An overview of computational life science databases & exchange formats of relevance to chemical biology research.

PubMed

Smalter Hall, Aaron; Shan, Yunfeng; Lushington, Gerald; Visvanathan, Mahesh

2013-03-01

Databases and exchange formats describing biological entities such as chemicals and proteins, along with their relationships, are a critical component of research in life sciences disciplines, including chemical biology wherein small information about small molecule properties converges with cellular and molecular biology. Databases for storing biological entities are growing not only in size, but also in type, with many similarities between them and often subtle differences. The data formats available to describe and exchange these entities are numerous as well. In general, each format is optimized for a particular purpose or database, and hence some understanding of these formats is required when choosing one for research purposes. This paper reviews a selection of different databases and data formats with the goal of summarizing their purposes, features, and limitations. Databases are reviewed under the categories of 1) protein interactions, 2) metabolic pathways, 3) chemical interactions, and 4) drug discovery. Representation formats will be discussed according to those describing chemical structures, and those describing genomic/proteomic entities.

Synthetic biology: insights into biological computation.

PubMed

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

2016-04-18

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

The Gene Ontology (GO) project: structured vocabularies for molecular biology and their application to genome and expression analysis.

PubMed

Blake, Judith A; Harris, Midori A

2008-09-01

Scientists wishing to utilize genomic data have quickly come to realize the benefit of standardizing descriptions of experimental procedures and results for computer-driven information retrieval systems. The focus of the Gene Ontology project is three-fold. First, the project goal is to compile the Gene Ontologies: structured vocabularies describing domains of molecular biology. Second, the project supports the use of these structured vocabularies in the annotation of gene products. Third, the gene product-to-GO annotation sets are provided by participating groups to the public through open access to the GO database and Web resource. This unit describes the current ontologies and what is beyond the scope of the Gene Ontology project. It addresses the issue of how GO vocabularies are constructed and related to genes and gene products. It concludes with a discussion of how researchers can access, browse, and utilize the GO project in the course of their own research. Copyright 2008 by John Wiley & Sons, Inc.

The centrality of RNA for engineering gene expression

PubMed Central

Chappell, James; Takahashi, Melissa K; Meyer, Sarai; Loughrey, David; Watters, Kyle E; Lucks, Julius

2013-01-01

Synthetic biology holds promise as both a framework for rationally engineering biological systems and a way to revolutionize how we fundamentally understand them. Essential to realizing this promise is the development of strategies and tools to reliably and predictably control and characterize sophisticated patterns of gene expression. Here we review the role that RNA can play towards this goal and make a case for why this versatile, designable, and increasingly characterizable molecule is one of the most powerful substrates for engineering gene expression at our disposal. We discuss current natural and synthetic RNA regulators of gene expression acting at key points of control – transcription, mRNA degradation, and translation. We also consider RNA structural probing and computational RNA structure predication tools as a way to study RNA structure and ultimately function. Finally, we discuss how next-generation sequencing methods are being applied to the study of RNA and to the characterization of RNA's many properties throughout the cell. PMID:24124015

Ionic scattering factors of atoms that compose biological molecules

PubMed Central

Matsuoka, Rei; Yamashita, Yoshiki; Yamane, Tsutomu; Kidera, Akinori; Maki-Yonekura, Saori

2018-01-01

Ionic scattering factors of atoms that compose biological molecules have been computed by the multi-configuration Dirac–Fock method. These ions are chemically unstable and their scattering factors had not been reported except for O−. Yet these factors are required for the estimation of partial charges in protein molecules and nucleic acids. The electron scattering factors of these ions are particularly important as the electron scattering curves vary considerably between neutral and charged atoms in the spatial-resolution range explored in structural biology. The calculated X-ray and electron scattering factors have then been parameterized for the major scattering curve models used in X-ray and electron protein crystallography and single-particle cryo-EM. The X-ray and electron scattering factors and the fitting parameters are presented for future reference. PMID:29755750

Modeling a synthetic multicellular clock: repressilators coupled by quorum sensing.

PubMed

Garcia-Ojalvo, Jordi; Elowitz, Michael B; Strogatz, Steven H

2004-07-27

Diverse biochemical rhythms are generated by thousands of cellular oscillators that somehow manage to operate synchronously. In fields ranging from circadian biology to endocrinology, it remains an exciting challenge to understand how collective rhythms emerge in multicellular structures. Using mathematical and computational modeling, we study the effect of coupling through intercell signaling in a population of Escherichia coli cells expressing a synthetic biological clock. Our results predict that a diverse and noisy community of such genetic oscillators interacting through a quorum-sensing mechanism should self-synchronize in a robust way, leading to a substantially improved global rhythmicity in the system. As such, the particular system of coupled genetic oscillators considered here might be a good candidate to provide the first quantitative example of a synchronization transition in a population of biological oscillators.

Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing

NASA Astrophysics Data System (ADS)

Garcia-Ojalvo, Jordi; Elowitz, Michael B.; Strogatz, Steven H.

2004-07-01

Diverse biochemical rhythms are generated by thousands of cellular oscillators that somehow manage to operate synchronously. In fields ranging from circadian biology to endocrinology, it remains an exciting challenge to understand how collective rhythms emerge in multicellular structures. Using mathematical and computational modeling, we study the effect of coupling through intercell signaling in a population of Escherichia coli cells expressing a synthetic biological clock. Our results predict that a diverse and noisy community of such genetic oscillators interacting through a quorum-sensing mechanism should self-synchronize in a robust way, leading to a substantially improved global rhythmicity in the system. As such, the particular system of coupled genetic oscillators considered here might be a good candidate to provide the first quantitative example of a synchronization transition in a population of biological oscillators.