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The next frontier of systems biology: higher-order and interspecies interactions
Michael A Fischbach, Nevan J Krogan
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-5-208
Abstract: Systems biology means different things to different people, and one can envisage it more as a strategy for studying biological systems than as a field of biology. Systems approaches have been very successful in the realms of biochemistry and genetics, especially for genetically tractable organisms, and have led to a deluge of mechanistic insights into a variety of biological areas. The 'systematic' nature of the approach involves testing or assaying all components of a biological milieu simultaneously, in an unbiased fashion, with no prior assumptions of what will be found. However, these modern approaches are not so different when compared to more classical genetic and biochemical strategies. Finally, we anticipate that the next frontier of systems biology will involve both higher-order interactions and the study of interspecies relationships in a systematic fashion.A decade ago, Bruce Alberts, Andrew Murray and Lee Hartwell noted that cellular components are organized into functional groups, or modules, and that the reductionist approach of studying each component in isolation was limiting [1,2]. Recent efforts in systems biology have taken advantage of this observation by using unbiased approaches to define the protein complexes that comprise these modules. For example, two groups have used a systematic affinity tag/purification and mass spectrometry approach to identify hundreds of protein complexes in the budding yeast Saccharomyces cerevisiae, many of which were previously unknown [3,4] (Figure 1a). Global efforts to define protein complexes have been extended to the prokaryotes Escherichia coli [5,6] and Mycoplasma pneumoniae [7] as well as to mammalian cells [8,9]. Highlighting the power of these approaches to rapidly uncover new biology in mapping out the circuit diagram of a cell, Kühner et al. [7] characterized 62 homomultimeric and 116 heteromultimeric soluble protein complexes in M. pneumoniae, and the majority of these were novel. A similar proportion
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
Systems Biology, Bioinformatics, and Biomarkers in Neuropsychiatry  [PDF]
Ali Alawieh,Fadi A. Zaraket,Jian-Liang Li,Stefania Mondello,Amaly Nokkari,Mahdi Razafsha,Bilal Fadlallah,Firas H. Kobeissy
Frontiers in Neuroscience , 2012, DOI: 10.3389/fnins.2012.00187
Abstract: Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a “top-down” method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders’ complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software “omics”-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.
Systems Biology as an Integrated Platform for Bioinformatics, Systems Synthetic Biology, and Systems Metabolic Engineering  [PDF]
Bor-Sen Chen,Chia-Chou Wu
Cells , 2013, DOI: 10.3390/cells2040635
Abstract: Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.
Integration of Proteomics, Bioinformatics, and Systems Biology in Traumatic Brain Injury Biomarker Discovery  [PDF]
J.D. Guingab-Cagmat,R.L. Hayes,J. Anagli
Frontiers in Neurology , 2013, DOI: 10.3389/fneur.2013.00061
Abstract: Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed.
Review of three Recent Books on the Boundary of Bioinformatics and Systems Biology
Eric Bullinger, Monica Schliemann
BioMedical Engineering OnLine , 2010, DOI: 10.1186/1475-925x-9-33
Abstract: ? Systems Biology and Bioinformatics: A Computational Approach by K. Najarian, C. Eichelberger, S. Najarian and S. Gharibzadeh, CRC Press, April 2009, ISBN: 9781420046502 (SBB)? Biomolecular Networks: Methods and Applications in Systems Biology by L. Chen, R.-S. Wang and X.-S. Zhang, Wiley, July 2009, ISBN: 978-0470243732 (BN)Systems biology is a holistic approach combining experimental data on multiple levels, from the genome and proteome over the interactome to signalling and metabolism in single cells, organs and organisms with the use of computational methods and predictive mathematical models. Bioinformatics is the application of computer science to biological systems, in particular for genomic data. This point of view is shared by the three books, published within a few months last year. All three books target a general audience with basic knowledge of biology, mathematics and computer science and cover quite similarly topics. An obvious difference between these books is their size: there is roughly a two-fold increase from SBB to BN and from the latter to BSB, which distinguished itself from the other two by being a collection of chapters by different authors, with different writing and presentation styles.All books contain an introduction to biology and to the mathematics needed in the remainder. BSB presents in a complete, very detailed and illustrated manner the cellular organisation over gene expression up to protein synthesis and cell signalling, in over 100 pages. These chapters contain glossaries and further references and are even suited for biologists in need of a recap. The introduction also contains a chapter on the epigenetics of spermiogenesis, whose style and specificity seems somehow misplaced in this part of the book, as it is more a research article than a review or textbook article as the other chapters of Part I. Also the chapter "Genomic Analysis of Transcription" might have been better placed in Part III "Transcriptome Analysis".BN's biol
Systems biology in the frontier of cancer research: a report of the Second International Workshop of Cancer Systems Biology  [cached]
Juan Cui,Yan-Chun Liang,Ying Xu
Chinese Journal of Cancer , 2012, DOI: 10.5732/cjc.012.10205
Abstract: The report summarizes the Second International Workshop of Cancer Systems Biology held on July 5-6, 2012 in Changchun, China. The goal of the workshop was to bring together cancer researchers with different backgrounds to share their views about cancer and their experiences in fighting against cancer, and to gain new and systems-level understanding about cancer formation, progression, diagnosis, and treatment through exchanging ideas.
MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics  [PDF]
Jose A Egea,David Henriques,Thomas Cokelaer,Alejandro F Villaverde,Julio R Banga,Julio Saez-Rodriguez
Mathematics , 2013,
Abstract: Optimization is key to solve many problems in computational biology. Global optimization methods provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics: enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Both methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at \url{http://www.iim.csic.es/~gingproc/meigo.html}. Documentation and examples are included. The R package has been submitted to Bioconductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology, outperforming other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization, that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.
The next evolutionary synthesis: from Lamarck and Darwin to genomic variation and systems biology
Jonathan BL Bard
Cell Communication and Signaling , 2011, DOI: 10.1186/1478-811x-9-30
Abstract: In the latter half of the twentieth century, the view of the process by which new species originated was based on meshing Darwinian variation and selection with work on population genetics and mutation. This view, the evolutionary synthesis, suggested that mutation within individuals led to genetic variation within a population; should a subgroup of that population become genetically isolated in a novel environment, a mix of unbalanced variation and new mutation within the subgroup would lead to new phenotypes appearing. In due course, one of these might reproduce better than the original phenotype so that it would take over and eventually lead to the appearance of a new species with a novel genotype [1].The enormous amount of molecular information that has emerged during the last couple of decades is making us review this synthesis, partly because we now know that the relationship between the phenotype and genotype is not as simple as previously assumed, partly because the genome is a richer, more complicated world than the scientists who put together the modern synthesis could ever have supposed and partly because there is data that does not fit comfortably within the synthesis. The two interesting and important books under review here set out to examine aspects of the state of evolutionary science now, the one taking an unashamedly contemporary position, the other starting from 200 years ago. Before discussing what they have to say, it is worth taking a look at their context by considering the state of evolutionary biology today.The evidence for evolution itself is robust as it comes from the three independent lines that each tells the same story: history (fossil record and isotope dating), morphology (taxonomic relationship and comparative embryology in living organisms - evolutionary change starts off as developmental change) and molecular sequence relationships. While the evolutionary synthesis is of course compatible with evolution, the evidence to support it
Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”  [PDF]
Mengjin Liu, Bruno Bienfait, Oliver Sacher, Johann Gasteiger, Roland J. Siezen, Arjen Nauta, Jan M. W. Geurts
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0084769
Abstract: The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the “missing links” between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.
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