Computational Biology

Computational Biology

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Mol Syst Biol. 3: 105 A key challenge of Systems Biology is that it must integrate several disciplines, each with a very different culture for disseminating results. Within biology, manuscripts describing new work are almost always published in peer‐reviewed periodicals. In contrast, within computer science and the engineering fields, new methods and results are typically presented as full‐length papers at meetings and workshops. Just as journals have Editorial Boards that handle review of manuscripts, such conferences assemble large and reputable Program Committees, which fulfill the same purpose. Publication in the best conferences, as for the best journals, is highly competitive. This past December, several hundred scientists convened in La Jolla, California for the Second Annual RECOMB Workshop on Systems Biology (December 1–3, 2006; http://chianti.ucsd.edu/recombsysbio06/). The meeting, which was held jointly with the RECOMB Workshop on Computational Proteomics, took place at the California Institute for Information Technology and Telecommunications in the University of California San Diego campus. RECOMB, which stands for Research in Computational Biology , has for a decade sponsored conferences that attract high‐quality papers in bioinformatics, primarily from computer science. In an effort to integrate the computational and experimental biology communities, RECOMB and Molecular Systems Biology entered into a partnership by which original, peer‐reviewed papers are presented orally at the Workshop on Systems Biology and then appear as full‐length manuscripts in the pages of the journal. The precise publication model was formulated after much discussion between the editors of the journal and the organizers of RECOMB. It is original and, we hope, will serve as a case study for future conferences. First, a list of 45 reviewers was …

During the last three decades, the research area of systems engineering has emerged as a domain of fundamental importance and major impact within chemical engineering, as well as a cornerstone area in interdisciplinary research initiatives with computer science, operations research, applied mathematics, materials and life sciences. This is attributed to the unique characteristics of systems engineering which are the combination of analysis and synthesis for the design, optimization, and operation of processes and products. The product and process discovery research efforts are founded on fundamental advances in mathematical modeling, optimization theory and algorithms, and insights derived either from existing operations and/or from biology, chemistry, and physics. The fundamental advances are epitomized through new theoretical, algorithmic, and modeling frameworks for (a) mixed-integer linear and nonlinear optimization, (b) deterministic global optimization, and (c) dynamic simulation and optimization. The proposed modeling and optimization approaches have multiscale applications ranging from macroscopic to mesoscopic to microscopic systems, and which provide a natural and fundamental link between systems engineering, computational chemistry, computational biology and systems biology. Approaches based on mixed-integer linear and nonlinear optimization which were typically identified with process synthesis, scheduling and planning applications, have entered the domains of gene regulatory networks, metabolic networks, signal transduction networks, beta-sheet topology prediction in proteins, de novo peptide and protein design, DNA recombination, phase problem in X-ray crystallography, side chain optimization in protein prediction, peptide identification via tandem mass spectroscopy. Approaches based on deterministic global optimization associated with process design, synthesis, scheduling, and pooling/blending applications, are now in the main stream of product design, structure prediction in protein folding, dynamics of protein folding, NMR protein structure refinement, and de novo protein design. Approaches based on dynamic models and large scale optimization which were identified with process models and their applications, are suited for metabolic and signal transduction networks. As a result, a synergism between systems engineering, computational biology, and systems biology has evolved gradually, opened new research avenues and has reached the stage where fascinating research contributions address important questions in computational biology with methods and tools from systems engineering which combine mathematical rigor with key biological insights. This article provides a perspective on the challenges and opportunities that emerge from the fundamental developments in the research fields of systems engineering and computational biology. The advances in the areas of deterministic global optimization and process scheduling are introduced first, followed by their respective research opportunities. The revolution of genomics is discussed next with a particular focus on the advances and challenges in the areas of structure prediction in protein folding, de novo peptide and protein design, and peptide and protein identification via tandem mass spectroscopy. This article is based on material presented as an invited talk at the session on “The Future of Chemical Engineering Research III” during the 2004 annual AIChE meeting.

The Intelligent Systems for Molecular Biology (ISMB) conference is the oldest and largest conference that specifically addresses bioinformatics and computational biology, i.e. interdisciplinary research that falls between computer science and biology, ISMB is 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 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. ISMB comprises five main types of presentations: tutorials, plenary presentations, poster presentations, topically focused satellite meetings and software demonstrations. These tutorials and fellows contribute to the development of human resources by allowing students and post-doctoral fellows to reach a state-of-the-art level rapidly, and to begin making contributions to the field. At ISMB 2002, 15 tutorials were held, 50 papers were presented, 498 posters were presented, 6 special interest group meetings here held, and 20 software demonstrations (both non-for-profit and corporate) were presented.

After "human genome project" has been accomplished, the life science comes to a new era, the post-genome era. In the post-genome era, the "big sciences" such as genomics, proteomics and metabolomics (so-called "omics") gradually become a new popular research methodology to provide global pictures of cells or organisms, although the classical experimental biology (small sciences) such as molecular biology or cell biology is still the mainstream in life sciences. The concept and strategy of omics is completely different from the classical experimental biology. The omics is called a "discovery science", of which the goal is to identify all the genes or proteins in the organisms, whereas the classical experimental biology such as molecular biology is called a "hypothesis-driven science", since the researches of these disciplines are initiated based on the scientific hypothesis and focus on studying the structure and functions of individual gene or protein. Systems biology is a newly born discipline in the post-genome era, which integrates the research strategy of classical experimental biology such as molecular biology with the new research strategy of "omics". Systems biology is also a new interdisciplinary frontier based mainly on the integration of the "wet" experiments such as molecular biology or "omics" with the "dry" experiments such as bioinformatics and computational biology. Technology of systems biology includes the "omics" platforms such as proteomics-platform and the theoretical platforms for computing and modeling. From these properties, Systems biology is defined as an integrating methodology for analyzing the components and dynamical behavior of biological systems as a whole. More importantly, these properties have made systems biology as a powerful analytical tool to reveal the complex diseases such as cancer and diabetes. Although the complex diseases have been extensively studied for a long term, it is far beyond understanding the mechanisms of the disease-process and curing these diseases. The difficulties for dealing with the complex diseases arise from the aspects of the complex diseases: 1) the causes of the initiation and development of the complex diseases involve multiple genetic factors, environment factors and the interaction of these two kinds of factors. 2) the different kinds of cells or tissues involve in the diseases. For example, the brain, pancreas, liver, skeletal muscle and adipose tissue mainly involve in the type 2 diabetes. 3) the molecular defects for the complex disease disrupt the normal behaviors of the complex molecular networks of genes and proteins. The classical bio-medicine based on molecular biology, cell biology, genetics and other experimental biology has made significant progress for against disease in general. However, the researchers on the bio-medicine area still face the great challenge for against the complex diseases such as cancer and diabetes since the methodology of the classical experimental biology is based on studying individual gene and protein and treat the organisms as a simple and linear system, which is not good enough to solve such problems of the complex diseases. Therefore, it is clear that the methodology and techniques of system biology must be applied for analyzing the molecular mechanisms of the complex diseases, and provide new solutions for preventing and curing the diseases.

In science, the relationship between methods and discovery is symbiotic.As we discover more, we are able to construct more precise and sensitive tools and methods that enable further discovery.With better lens crafting came microscopes, and with them the discovery of living cells.In the last 40 years, advances in molecular biology, statistics, and computer science have ushered in the field of bioinformatics and the genomic era.Computational scientists enjoy developing new methods, and the community encourages them to do so.Indeed, the editorial guidelines for PLOS Computational Biology require manuscripts to apply novel methods.However, it is often confusing to know which method to choose: which method is best?And, in this context, what does "best" mean?To help choose an appropriate method for a particular task, scientists often form community-based challenges for the unbiased evaluation of methods in a given field.These challenges help evaluate existing and novel methods, while helping to coalesce a community and leading to new ideas and collaborations.In computational biology, the first of these challenges was arguably the Critical Assessment of protein Structure Prediction, or CASP [1], whose goal is to evaluate methods for predicting three-dimensional protein structure from amino acid sequence.The first CASP meeting was held in December of 1994, following a "prediction period" where members of the community were presented with protein amino acid sequences and asked to predict their three dimensional structures.The sequences that were chosen had recently been solved by X-ray crystallography but had not been not published or released until after the predictions from the community were made.Since the first CASP, we have seen many successful challenges, including Critical Assessment of Function Annotation (CAFA) for protein function prediction [2], Critical Assessment of Genome Interpretation (CAGI) (for genome interpretation) [3], Critical Assessment of Massive (originally "Microarray") Data Analysis (CAMDA) (for large-scale biological data) [4], BioCreative (for biomedical text mining) [5], the Assemblathon (for sequence assembly), and the NCI-DREAM Challenges (for various biomedical challenges), amongst others [6].Computational challenges also help solve new problems.While the original CASP experiment was developed to evaluate existing methods applied to current problems, other communities often look at other areas for which there are no existing tools.These challenges have spread successfully to industry, and companies such as Innocentive [7] and X-Prize [8] offer large prizes for solving novel questions.

A selection of papers prepared by participants of 7th Young Scientists School SBB’15 is presented in three Supplementary Issues to the journals of BMC series, namely, BMC Genomics, BMC Genetics and BMC Microbiology. SBB’15 took place June 22nd-25th in Novosibirsk, Russia (http://conf.nsc.ru/sbb2015). The series of Young Scientists Schools in Systems Biology and Bioinformatics (SBB) started several years ago, in 2008, as educational workshop associated with the large international conference series on bioinformatics known as BGRS\SB (Bioinformatics of Genome Regulation and Structure\Systems Biology) (http://conf.bionet.nsc.ru/bgrssb2016). After its inauguration year of 1998, BGRS/SBB held biannually at the Institute of Cytology and Genetics SB RAS in Novosibirsk. In a recent decade, it became a prime meeting venue for biologists, computer scientists, physicists, mathematicians and biochemists working in an interdisciplinary field of systems biology and computer genomics. Being the largest system biology meeting series in Russia, BGRS\SB-14 attracted participants from 27 countries. In the past BioMedCentral had published series of special issues based on best materials presented at the conference in BMC Genomics (http://www.biomedcentral.com/bmcgenomics/supplements/15/S12), BMC Evolutionary Biology (http://www.biomedcentral.com/bmcevolbiol/supplements/15/S1), BMC Genetics (http://www.biomedcentral.com/bmcgenet/supplements/16/S1) and BMC Systems biology (http://www.biomedcentral.com/bmcsystbiol/supplements/9/S2). In addition, the protein structure related studies presented by BGRS/SBB participants were collated as special issues in Journal of Bioinformatics and Computational Biology (http://www.worldscientific.com/toc/jbcb/4113/01; http://www.worldscientific.com/toc/jbcb/11/01). [1] In addition, the SBB School series has its own history to be proud of. The first generation of its participants already matured enough to substantially advance in the field. A majority of the participants graduated with PhDs, and now are contributing to the school as the lecturers and to a number of world universities as professors. For some students, the conference became instrumental in obtaining their post-doc or first independent position in academic world. In many cases, the informal atmosphere of the school helped to spark a collaboration that later led to publications in leading journals or the dramatic changes in their science career. The Program Committee of the conference and SBB Schools series includes data analysis, bioinformatics and computational biology professionals from different countries. For the last decade, the SBB conference was co-chaired by Academician Prof. Nikolay A. Kolchanov of the Institute of Cytology and Genetics SB RAS, Russia and Prof. Dr. Ralf Hofestadt of Bielefeld University, Germany. System biology and bioinformatics are rapidly developing and wide fields of science knowledge. For Young Scientists Schools, every year is different as it comprises the topics for the trainings that are the hottest at the time of the event. In 2015, the SBB School concentrated on the modern genomics and an analysis of high-throughput sequencing data. The materials presented in the current special issue provide examples of seamless integration of experimental studies performed in the lab and the computer-assisted inquiries into in the complex patterns of the organization and the functioning of biologic systems at the molecular, cell, tissue, organ, and body levels. The scientific topics discussed in 2015 were presented at the following sections: Next generation sequencing (NGS) and data analysis Evolutionary bioinformatics Systems biology and gene network modeling In SBB’15 Supplements to BMC journals Genomics, we collected the best studies presented at the conference. Due to the general breadth of the field of Systems Biology and Bioinformatic, the manuscripts were accepted into three separate tracks, thus forming three Supplementary Issues, BMC Genomics, BMC Genetics and BMC Microbiology. Below we will describe contents of these issues.

During the last decade, the Human Genome Project [1], Human Proteome Project [2], Human Physiome Project [3], The International HapMap Project, Genome-Wide Associate Study (GWAS), The 1000 Genomes Project, The Human Microbiome Project, Epigenome, Metabolome, LIPID MAPS, and Next Generation Sequencing have generated large volumes of molecular data about human genes, proteins, physiology, SNPs (single nucleotide polymorphism), metabolites, and lipids etc. This has led to the fast growth of several research sub-areas: 1) high-volume molecular data mining and interpretation; 2) systems biology based on molecular features (i.e., biomarkers) extracted from high-throughput data. Examples include molecular interaction network reconstruction and pathway modeling; 3) synthetic biology, where researchers use engineering approach to make artificial cell and organism; and 4) translational bioinformatics that identifies genomic and proteomic biomarkers of disease to understand molecular mechanisms and guide clinical decision making. This field has grown so rapidly that there are a dozen bioinformatics conferences each year organized by researchers with different backgrounds such as biological or medical sciences, computing or computer science, and engineering. In 2004, the IEEE Engineering in Medicine and Biology Society (EMBS) started the theme “Bioinformatics and Computational Biology, Systems Biology, Modeling Methodologies.” It started with high throughput omic data analysis, structural bioinformatics, physiological modeling, then moved to systems biology, and recently included synthetic biology, with future movement towards translational bioinformatics in biomedical applications such as cancer prevention, therapeutics, clinical data mining and decision making. In this spotlight, we summarize key topics of importance for the EMBS community.

Bioinformatics is an extremely important field of biological science that also includes in-depth knowledge and skill in statistics and computer science. With the advance of new sequencing projects, bioinformatics helps to comprehend biological processes to primarily serve the agriculture and healthcare sectors with various spinoffs. To address the advances and awareness in bioinformatics to students and researchers this book will serve as a quick reference book on the subject. Bioinformatics is essential in all the fields of biotechnology and molecular biology that deal with molecular data. Thus the subject caters to divergent disciplines of biological science. The book on Concepts in Bioinformatics, Basics to Advances is a compilation of basic information on bioinformatics and also includes advanced areas that are required by students and professionals. The authors have great knowledge and experience in putting together updated information on animal biotechnology through eminent experts. The beginning of the book familiarizes the readers with the concept of bioinformatics and its history. Then some important concepts of basic bioinformatics have been discussed in the subsequent five chapters, that includes, databases, multiple sequence alignment, primer designing, and molecular phylogeny. This section is important to the postgraduate students of bioinformatics, biotechnology, and molecular biology. Chapters 9 to chapter 14 discusses BLAST and FASTA, protein structure prediction through homology modeling and molecular modeling, which are equally important for acquiring skills for in silico analysis. The last three chapters of the book discuss some advanced components of bioinformatics, namely, drug designing, systems biology, and synthetic biology. In general, the book is meant as a “short introduction” to bioinformatics and can be used as a sensitizer to study the subject. The book provides a comprehensive introduction to the field of bioinformatics, covering a range of topics from basic concepts to advanced techniques. The book is aimed at students, researchers, and educators in the field of biotechnology and bioinformatics, and is designed to be a valuable resource for those just starting out in the field. The topics covered in the book include molecular data analysis, multiple sequence alignment, primer design, phylogenomics, omics, molecular modeling, drug design, and synthetic biology. Overall, it seems like the book would be a useful tool for anyone looking to gain a solid foundation in the field of bioinformatics.

The emerging science of sequences: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, edited by Andreas Baxevanis and B. F. Francis Ouellette

91Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle uponTyne, United Kingdom, 2School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom, 3QB3 Institute, University of California, Berkeley,California, United States of America, 4Harvard School of Public Health, Department of Biostatistics, Boston, Massachusetts, United States of America, 5Department ofComputer Science, Free University Berlin, Berlin, Germany, 6Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin,Germany, 7Biotec, TU Dresden, Dresden, Germany, 8International Society for Computational Biology, La Jolla, California, United States of America, 9Computational andStructural Biology Unit, EMBL, Heidelberg, Germany

The onset of addiction is marked with drug induced positive experiences that keep being repeated. During that time, adaptation occurs and addiction is stabilized. Interruption of those processes induces polysymptomatic withdrawal syndromes. Abstinence is accompanied by risks of relapse. These features of addiction suggest adaptive brain dynamics with common pathways in complex neuronal networks. Addiction research has used animal models, where some of those phenomena could be reproduced, to find correlates of addictive behavior. The major thrust of those approaches has been on the involvement of genes and proteins. Recently, an enormous amount of data has been obtained by high throughput technologies in these fields. Therefore, (Computational) "Systems Biology" had to be implemented as a new approach in molecular biology and biochemistry. Conceptually, Systems Biology can be understood as a field of theoretical biology that tries to identify patterns in complex data sets and that reconstructs the cell and cellular networks as complex dynamic, self-organizing systems. This approach is embedded in systems science as an interdisciplinary effort to understand complex dynamical systems and belongs to the field of theoretical neuroscience (Computational Neuroscience). Systems biology, in a similar way as computational neuroscience is based on applied mathematics, computer-based computation and experimental simulation. In terms of addiction research, building up "computational molecular systems biology of the (addicted) neuron" could provide a better molecular biological understanding of addiction on the cellular and network level. Some key issues are addressed in this article.

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Australia enjoys a high international reputation for research in experimental genetics, molecular and cell biology, animal and plant sciences, biotechnology, medicine, biodiversity, and ecological modelling. Computational research is broadly established in these domain areas and others relevant to bioscience. Combined with strong traditions in mathematics and statistics, and national and state investment in computational infrastructure, computational biology Down Under is simply too rich and diverse to be done justice in these few pages. Here we (see Box 1 Authors' Biographies) focus more narrowly, attempting to provide a snapshot of bioinformatic and computational genome-scale biology in Australia circa 2008.