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Frontiers of Electrical and Electronic Engineering in China 2015年第1期10卷 46-53页

作者： Ettore Murabito Riccardo Colombo Chengkun Wu Malkhey Verma Samrina Rehman Jacky Snoep Shao-Liang Peng Naiyang Guan Xiangke Liao Hans V. Westerhoff Manchester Institute of Biotechnology School of Computer Science Faculty of Engineering and Physical Sciences Manchester Centre for Integrative Systems Biology The University of Manchester Manchester M139PL United Kingdom. Department of Informatics Systems and Communication University of Milan-Bicocca Milan 20126 Italy. SYSBIO - Centre of systems biology Milan 20126 Italy. Manchester Institute of Biotechnology School of Chemical Engineering and Analytical Sciences Manchester Centre forIntegrative Systems Biology The University of Manchester Manchester M139PL United Kingdom. School of Computer Science National University of Defence Technology Changsha 410073China

In the second half of 2014 the Manchester Institute of Biotechnology, based in Manchester （UK）, hosted the first Suprabiology congress, an event attended by representa- tives of different academic institutions and industry based in both the UK and China. The congress was aimed to serve as a platform to discuss and promote potential collaborations between the UK and China on the subject of systems biology and High Performance Computing. The event, sponsored by the ＂BBSRC China Partnering Awards＂ and ISBE, was organised as a sequence of talks addressing the different aspects of systems biology that can benefit from High Performance Computing. A general discussion session followed where the scientific, techni- cal, and logistic aspects of the prospected UK-China collaborations were examined.

关键词： System biology High Performance Computing Flux Balance Analysis Nonnegative Matrix Factorization Biochemical Network

iPlant systems biology (iPSB): An International Network Hub in the Plant Community

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Molecular Plant 2019年第6期12卷 727-730页

作者： Pascal Falter-Braun Siobhan Brady Rodrigo A.Gutierrez Gloria M.Coruzzi Gabriel Krouk Institute of Network biology (INET) Helmholtz Zentrum Munchen (HMGU) 85764 Munchen-Neuherberg Germany Microbe-Host Interactions Faculty of Biology. Ludwig-Maximilians-University (LMU) Munich 82152 Planegg-Martinsried Germany Department of Plant biology and the Genome Center University of California Davis CA 95616 USA Departamento de Genetica Molecular y Microbiologia FONDAP Center for Genome Regulation Millennium Institute for Integrative Biology Pontificia Universidad Catolica de Chile Santiago 8331150 Chile Center for Genomics and systems biology Department of Biology New York University New York NY 10003 USA B&PMP Univ Montpellier CNRS INRA SupAgro Montpellier France

sequenced genome of a multicellular organism (Arabidopsis Genome, 2000) completed just after those of the nematode Caenorhabditis elegans (C. elegans Sequencing Consortium, 1998) and the fruit fly Drosophila melanogaster (Adams et al., 2000). Availability of a full whole-genome sequence for the reference plant opened to plant biologists what is commonly called the post-genomic era.

关键词： systems biology International Network Hub Plant Community

An Effective Numerical Calculation Method for Multi-Time-Scale Mathematical Models in systems biology

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Applied Mathematics 2016年第17期7卷 2241-2268页

作者： Yohei Motomura Hiroyuki Hamada Masahiro Okamoto Graduate School of systems Life Sciences Kyushu University Fukuoka Japan Synthetic systems biology Research Center Kyushu University Fukuoka Japan

The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In systems biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.

关键词： Finite Difference Method Stiff Equation Multi-Time-Scale systems biology Mathematical Analysis

In-Silico Analysis &In-Vivo Results Concur on Glutathione Depletion in Glyphosate Resistant GMO Soy, Advancing a systems biology Framework for Safety Assessment of GMOs

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American Journal of Plant Sciences 2016年第12期7卷 1571-1589页

作者： V. A. Shiva Ayyadurai Michael Hansen John Fagan Prabhakar Deonikar systems biology Group International Center for Integrative Systems Cambridge MA USA Consumers Union Yonkers NY USA Earth Open Source Institute Fairfield IA USA

This study advances previous efforts towards development of computational systems biology, in silico, methods for biosafety assessment of genetically modified organisms (GMOs). C1 metabolism is a critical molecular system in plants, fungi, and bacteria. In our previous research, critical molecular systems of C1 metabolism were identified and modeled using CytoSolve?, a platform for in silico analysis. In addition, multiple exogenous molecular systems affecting C1 metabolism such as oxidative stress, shikimic acid metabolism, glutathione biosynthesis, etc. were identified. Subsequent research expanded the C1 metabolism computational models to integrate oxidative stress, suggesting glutathione (GSH) depletion. Recent integration of data from the EPSPS genetic modification of Soy, also known as Roundup Ready Soy (RRS), with C1 metabolism predicts similar GSH depletion and HCHO accumulation in RRS. The research herein incorporates molecular systems of glutathione biosynthesis and glyphosate catabolism to expand the extant in silico models of C1 metabolism. The in silico results predict that Organic Soy will have a nearly 250% greater ratio of GSH and GSSG, a measure of glutathione levels, than in RRS that are glyphosate-treated glyphosate-resistant Soy versus the Organic Soy. These predictions also concur with in vivo greenhouse results. This concurrence suggests that these in silico models of C1 metabolism may provide a viable and validated platform for biosafety assessment of GMOs, and aid in selecting rational criteria for informing in vitro and in vivo efforts to more accurately decide in the problem formulation phase whose parameters need to be assessed so that conclusion on “substantial equivalence” or material difference of a GMO and its non-GMO counterpart can be drawn on a well-grounded basis.

关键词： Glutathione Genetic Modification C1 Metabolism Biomarker Safety Assessment In-Silico Analysis Glyphosate CytoSolve systems biology Safety Assessment

C4 Rice-an Ideal Arena for systems biology Research

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Journal of Integrative Plant biology 2010年第8期52卷 762-770页

作者： Xin-Guang Zhu Lanlan Shan Yu Wang William Paul Quick Key Laboratory of Computational biology CAS-MPG Partner Institute for Computational Biology Shanghai Institutes for Biologica Sciences Chinese Academy of Sciences Shanghai 200031 China The International Rice Research Institute Los Banos Philippines

Engineering the C4 photosynthetic pathway into C3 crops has the potential to dramatically increase the yields of major C3 crops. The genetic control of features involved in C4 photosynthesis are still far from being understood; which partially explains why we have gained little success in C4 engineering thus far. Next generation sequencing techniques and other high throughput technologies are offering an unprecedented opportunity to elucidate the developmental and evolutionary processes of C4 photosynthesis. Two contrasting hypotheses about the evolution of C4 photosynthesis exist, i.e. the master switch hypothesis and the incremental gain hypothesis. These two hypotheses demand two different research strategies to proceed in parallel to maximize the success of C4 engineering. In either case, systems biology research will play pivotal roles in identifying key regulatory elements controlling development of C4 features, identifying essential biochemical and anatomical features required to achieve high photosynthetic efficiency, elucidating genetic mechanisms underlining C4 differentiation and ultimately identifying viable routes to engineer C4 rice. As a highly interdisciplinary project, the C4 rice project will have far-reaching impacts on both basic and applied research related to agriculture in the 21st century.

关键词： Engineering Engines Engineers Viverridae Genetics Crops Oesophagus Forecasting Rice Technology

systems biology of Plant-Microbiome Interactions

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Molecular Plant 2019年第6期12卷 804-821页

作者： Patricia A.Rodriguez Michael Rothballer Soumitra Paul Chowdhury Thomas Nussbaumer Caroline Gutjahr Pascal Falter-Braun Institute of Network biology (INET) Helmholtz Zentrum Munchen German Research Center for Environmental Health Munich Germany Institute of Environmental Medicine (IEM) UNIKA-T Technical University of Munich Augsburg Germany Plant Genetics TUM School of Life Science Weihenstephan Technical University of Munich (TUM) Freising Germany Microbe-Host Interactions Faculty of Biology Ludwig-Maximilians-Universitat (LMU) Mijnchen Munich Germany

In natural environments,plants are exposed to diverse microbiota that they interact with in complex *** plant-pathogen interactions have been intensely studied to understand defense mechanisms in plants,many microbes and microbial communities can have substantial beneficial effects on their plant *** beneficial effects include improved acquisition of nutrients,accelerated growth,resilience against pathogens,and improved resistance against abiotic stress conditions such as heat,drought,and ***,the beneficial effects of bacterial strains or consortia on their host are often cultivar and species specific,posing an obstacle to their general ***,many of the signals that trigger plant immune responses are molecularly highly similar and often identical in pathogenic and beneficial ***,it is unclear what determines the outcome of a particular microbe-host interaction and which factors enable plants to distinguish beneficials from *** unravel the complex network of genetic,microbial,and metabolic interactions,including the signaling events mediating microbe-host interactions,comprehensive quantitative systems biology approaches will be needed.

关键词： plant systems biology plant microbiome microbial communities SynComs microbe-host interactions

systems Approaches to biology and Disease Enable Translational systems Medicine

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Genomics, Proteomics & Bioinformatics 2012年第4期10卷 181-185页

作者： Leroy Hood Qiang Tian Institute for systems biology SeattleWA 98109-5234USA

The development and application of systems strategies to biology and disease are transforming medical research and clinical practice in an unprecedented rate. In the foreseeable future, clinicians, medical researchers, and ultimately the consumers and patients will be increasingly equipped with a deluge of personal health information, e.g., whole genome sequences, molecular profiling of diseased tissues, and periodic multi-analyte blood testing of biomarker panels for disease and wellness. The convergence of these practices will enable accurate prediction of disease susceptibility and early diagnosis for actionable preventive schema and personalized treatment regimes tailored to each individual. It will also entail proactive participation from all major stakeholders in the health care system. We are at the dawn of predictive, preventive, personalized, and participatory （P4） medicine, the fully implementation of which requires marrying basic and clinical researches through advanced systems thinking and the employment of high-throughput technologies in genomics, pro- teomics, nanofluidics, single-cell analysis, and computation strategies in a highly-orchestrated discipline we termed translational systems medicine.

关键词： systems biology P4 medicine Family genome sequencing Targeted proteomics Single-cell analysis

Building digital life systems for future biology and medicine

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Quantitative biology 2023年第3期11卷 207-213页

作者： Xuegong Zhang Lei Wei Rui Jiang Xiaowo Wang Jin Gu Zhen Xie Hairong Lv MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Center of Synthetic and systems biology Tsinghua UniversityBeijing 100084China School of Life Sciences and School of Medicine Tsinghua UniversityBeijing 100084China

The rapid development of biological technology (BT) and information technology (IT) especially of genomics and artificial intelligence (AI) is bringing great potential for revolutionizing future medicine. We propose the concept and framework of Digital Life systems or dLife as a new paradigm to unleash this potential. It includes the multi-scale and multi-granule measure and representation of life in the digital space, the mathematical and/or computational modeling of the biology behind physiological and pathological processes, and ultimately cyber twins of healthy or diseased human body in the virtual space that can be used to simulate complex biological processes and deduce effects of medical treatments. We advocate that dLife is the route toward future AI precision medicine and should be the new paradigm for future biological and medical research.

关键词： digital life systems digital twin aritificial intelligence precision medicine

Multiomics metabolic and epigenetics regulatory network in cancer:A systems biology perspective

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Journal of Genetics and Genomics 2021年第7期48卷 520-530页

作者： Xuezhu Wang Yucheng Dong Yongchang Zheng Yang Chen The State Key Laboratory of Medical Molecular biology Department of Biochemistry and Molecular BiologyInstitute of Basic Medical SciencesSchool of Basic MedicineChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100005China Department of Liver Surgery Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China

Genetic,epigenetic,and metabolic alterations are all hallmarks of ***,the epigenome and metabolome are both highly complex and dynamic biological networks in *** interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer *** this perspective,we first review the state of high-throughput biological data acquisition(*** data)and analysis(*** tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput *** conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database ***,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics *** propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.

关键词： Metabolome Epigenetics Epigenome Multiomics Biological network Deep learning