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Computational Biology and Bioinformatics in Nigeria  [PDF]
Segun A. Fatumo ,Moses P. Adoga,Opeolu O. Ojo,Olugbenga Oluwagbemi,Tolulope Adeoye,Itunuoluwa Ewejobi,Marion Adebiyi,Ezekiel Adebiyi ?,Clement Bewaji ?,Oyekanmi Nashiru ?
PLOS Computational Biology , 2014, DOI: doi/10.1371/journal.pcbi.1003516
Abstract: Over the past few decades, major advances in the field of molecular biology, coupled with advances in genomic technologies, have led to an explosive growth in the biological data generated by the scientific community. The critical need to process and analyze such a deluge of data and turn it into useful knowledge has caused bioinformatics to gain prominence and importance. Bioinformatics is an interdisciplinary research area that applies techniques, methodologies, and tools in computer and information science to solve biological problems. In Nigeria, bioinformatics has recently played a vital role in the advancement of biological sciences. As a developing country, the importance of bioinformatics is rapidly gaining acceptance, and bioinformatics groups comprised of biologists, computer scientists, and computer engineers are being constituted at Nigerian universities and research institutes. In this article, we present an overview of bioinformatics education and research in Nigeria. We also discuss professional societies and academic and research institutions that play central roles in advancing the discipline in Nigeria. Finally, we propose strategies that can bolster bioinformatics education and support from policy makers in Nigeria, with potential positive implications for other developing countries.
Bioconductor: open software development for computational biology and bioinformatics
Robert C Gentleman, Vincent J Carey, Douglas M Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, Laurent Gautier, Yongchao Ge, Jeff Gentry, Kurt Hornik, Torsten Hothorn, Wolfgang Huber, Stefano Iacus, Rafael Irizarry, Friedrich Leisch, Cheng Li, Martin Maechler, Anthony J Rossini, Gunther Sawitzki, Colin Smith, Gordon Smyth, Luke Tierney, Jean YH Yang, Jianhua Zhang
Genome Biology , 2004, DOI: 10.1186/gb-2004-5-10-r80
Abstract: The Bioconductor project [1] is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics (CBB). Biology, molecular biology in particular, is undergoing two related transformations. First, there is a growing awareness of the computational nature of many biological processes and that computational and statistical models can be used to great benefit. Second, developments in high-throughput data acquisition produce requirements for computational and statistical sophistication at each stage of the biological research pipeline. The main goal of the Bioconductor project is creation of a durable and flexible software development and deployment environment that meets these new conceptual, computational and inferential challenges. We strive to reduce barriers to entry to research in CBB. A key aim is simplification of the processes by which statistical researchers can explore and interact fruitfully with data resources and algorithms of CBB, and by which working biologists obtain access to and use of state-of-the-art statistical methods for accurate inference in CBB.Among the many challenges that arise for both statisticians and biologists are tasks of data acquisition, data management, data transformation, data modeling, combining different data sources, making use of evolving machine learning methods, and developing new modeling strategies suitable to CBB. We have emphasized transparency, reproducibility, and efficiency of development in our response to these challenges. Fundamental to all these tasks is the need for software; ideas alone cannot solve the substantial problems that arise.The primary motivations for an open-source computing environment for statistical genomics are transparency, pursuit of reproducibility and efficiency of development.High-throughput methodologies in CBB are extremely complex, and many steps are involved in the conversion of information from low-level information structures (for example,
Computational Modeling, Formal Analysis, and Tools for Systems Biology  [PDF]
Ezio Bartocci?,Pietro Lió
PLOS Computational Biology , 2016, DOI: 10.1371/journal.pcbi.1004591
Abstract: As the amount of biological data in the public domain grows, so does the range of modeling and analysis techniques employed in systems biology. In recent years, a number of theoretical computer science developments have enabled modeling methodology to keep pace. The growing interest in systems biology in executable models and their analysis has necessitated the borrowing of terms and methods from computer science, such as formal analysis, model checking, static analysis, and runtime verification. Here, we discuss the most important and exciting computational methods and tools currently available to systems biologists. We believe that a deeper understanding of the concepts and theory highlighted in this review will produce better software practice, improved investigation of complex biological processes, and even new ideas and better feedback into computer science.
Systems Biology: The Next Frontier for Bioinformatics  [PDF]
Vladimir A. Liki?,Malcolm J. McConville,Trevor Lithgow,Antony Bacic
Advances in Bioinformatics , 2010, DOI: 10.1155/2010/268925
Abstract: Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems. 1. Introduction The term “systems biology” has emerged recently to describe the frontier of cross-disciplinary research in biology [1–5]. This term was propelled into the mainstream merely ten years ago [1–3], coinciding with the completion of the Human Genome Project (HGP) [6, 7] and the concomitant emergence of ‘omics technologies, namely transcriptomics [8, 9], proteomics [10], and metabolomics [11, 12]. However, the origins of modern systems biology can be traced back to the middle of last century [13–15], with history that is both conceptually complex and institutionally convoluted. For example, a general systems theory was developed and applied to biology in late 1960's [14, 15]. Independently, the theory of metabolic control was developed, and metabolic flux was recognized as a “systemic property” [16–18]. Here, we focus on the reemergence of “systems thinking” linked to the post-genomic era and the development of global molecular profiling methods collectively known as ‘omics technologies. The discussion of systems biology in the broader historical context can be found elsewhere (see, e.g., [4, 19] and
Clinical Bioinformatics.  [PDF]
Phei Lang Chang
Chang Gung Medical Journal , 2005,
Abstract: Clinical bioinformatics provides biological and medical information to allow for individualizedhealthcare. In this review, we describe the uses of clinical bioinformatics. Afterthe analysis of the complete human genome sequences, clinical bioinformatics enablesresearchers to search online biological databases and use the biological information in theirmedical practices. The data obtained from using microarray is extremely complicated. Inclinical bioinformatics, selecting appropriate software to analyze the microarray data formedical decision making is crucial. Proteomics strategy tools usually focus on similaritysearches, structure prediction, and protein modeling. In clinical bioinformatics, the proteomicdata only have meaning if they are integrated with clinical data. In pharmacogenomics,clinical bioinformatics includes elaborate studies of bioinformatics tools and various facetsof proteomics related to drug target identification and clinical validation. Using clinicalbioinformatics, researchers apply computational and high-throughput experimental techniquesto cancer research and systems biology. Meanwhile, researchers of bioinformaticsand medical information have incorporated clinical bioinformatics to improve health care,using biological and medical information. Using the high volume of biological informationfrom clinical bioinformatics will contribute to changes in practice standards in the healthcaresystem. We believe that clinical bioinformatics provides benefits of improving healthcare,disease prevention and health maintenance as we move toward the era of personalized medicine.
Computational models in systems biology
Laurence Loewe, Jane Hillston
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-12-328
Abstract: One of the chief goals of systems biology is to build mechanistic mathematical models of biological systems to further the understanding of biological detail. Such models often aim at predicting the outcome of potentially interesting biological experiments, and if such predictions are confirmed by wet-lab observations, an important step forward is made. How exactly such models are constructed and how predictions are computed were at the core of a recent conference on Computational Methods in Systems Biology that brought 80 participants to Rostock, Germany (for conference proceedings see volume 5307 of Lecture Notes in Bioinformatics http://dx.doi.org/10.1007/978-3-540-88562-7 webcite).A simplistic approach to model construction might be to capture everything that is known about a system and simulate it in supercomputers. While this is appropriate for some systems, it is impossible or highly impracticable for many others. This is mostly due to the complexity of biological systems, which demand simplification to make them amenable to modeling. Such simplifications have to capture the essence of the processes of interest, while neglecting as many of the less important details as possible. Thus, one can consider model building in systems biology as the art of building caricatures of life: capture the essence, ignore the rest.Two formalisms called process algebras and Petri nets offer alternative ways of constructing computational systems biology models. Both are concerned with how to specify (mostly quantitative) models of molecular reaction networks in an abstract way that is independent of particular mathematical techniques of analysis. Access to techniques such as differential equations or stochastic simulations is then facilitated automatically by software tools that translate the abstract model into a concrete model that is ready for computation. The advantage of this approach is that one needs to describe the model only once in order to access a variety of analyti
Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System  [PDF]
Nicolas Rapin,Ole Lund,Massimo Bernaschi,Filippo Castiglione
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0009862
Abstract: We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein–protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.
Bioinformatics resource manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools  [cached]
Tilton Susan C,Tal Tamara L,Scroggins Sheena M,Franzosa Jill A
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-311
Abstract: Background MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. Results BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs. Conclusions BRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm).
Bioinformatics meets systems biology
Carlos Salazar, Jana Schütze, Oliver Ebenh?h
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-1-303
Abstract: The efficient integration of bioinformatics and systems biology requires worldwide cooperation not only in the research of senior scientists but also in the research training of young scientists. To this end, a student-focused workshop on bioinformatics and systems biology http://www.biologie.hu-berlin.de/gk/ibsb2005 webcite was held last August at Humboldt University in Berlin, Germany. This was the fifth annual workshop held as part of a research collaboration between the Bioinformatics Program of Boston University in the USA, the Bioinformatics Center of Kyoto University in Japan, and the Berlin-located graduate program 'Dynamics and Evolution of Cellular and Macromolecular Processes'. This time the meeting had two main themes - the integration of genomic and chemical information in the analysis of the dynamics and topology of cellular regulatory networks, and the development of more accurate computational tools for the analysis of gene expression and the prediction of transcription-factor binding sites. Full papers accepted for the fifth workshop have been published in the Genome Informatics Series of the Japanese Society of Bioinformatics, edited by Satoru Miyano (University of Tokyo, Japan) http://www.jsbi.org/journal/GI16_1.html webcite.Trends in genome biology and bioinformatics were highlighted in the opening talk by Minoru Kanehisa (Kyoto University Bioinformatics Center, Japan), whose group is responsible for the Kyoto Encyclopedia of Genes and Genomes (KEGG) database http://www.genome.ad.jp/kegg webcite. This stores molecular interaction networks and graphics, including metabolic pathways, regulatory pathways and molecular complexes. Kanehisa emphasized the importance of an integrated analysis of genomic and chemical information to predict the complete functional behaviors of cells, organisms and ecosystems. While traditional genomics and other 'omics' have contributed to our knowledge of the genes and proteins that make up a biological system, new chemi
Advances in Bioinformatics and Computational Biology: Don't take them too seriously anyway  [PDF]
Emanuel Diamant
Computer Science , 2015,
Abstract: In the last few decades or so, we witness a paradigm shift in our nature studies - from a data-processing based computational approach to an information-processing based cognitive approach. The process is restricted and often misguided by the lack of a clear understanding about what information is and how it should be treated in research applications (in general) and in biological studies (in particular). The paper intend to provide some remedies for this bizarre situation.
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