A combined statistical and in silico framework for analysis and characterization of microbial and mammalian metabolic networks

A COMBINED STATISTICAL AND IN SILICO FRAMEWORK FOR ANALYSIS AND CHARACTERIZATION OF MICROBIAL AND MAMMALIAN METABOLIC NETWORKS SELVARASU SURESH (B. Tech, University of Madras, Chennai, India) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF CHEMICAL & BIOMOLECULAR ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2009 Acknowledgements ______________________________________________________________________________ It is with great pleasure that I take this opportunity to express my gratitude to all those who have helped me in my research progress and more so in shaping my PhD into an enriching experience. The research guidance that I got through my advisors Prof. I. A. Karimi and Dr. Lee Dong-Yup at NUS was much more than what I had expected. With due respect, I express my sincere gratitude to them for being wonderful and inspiring supervisors. Without their immense support, timely inputs, guidance and encouragement my progress was impossible. There is no word to explain their influence on my research. I also wish to thank them for involving me in several projects and especially in collaborations with research institutes (BTI) which provided me a very good chance to learn more. It was indeed a privilege to work with them. I would like to thank Dr. Victor Wong and Dr. Dave Ow from BTI, Singapore for their immense help in providing me the experimental data. I appreciate their patience in explaining the nuances of experimental strategies whenever I approached them. I extend my thanks to A/P. Loh KaiChee and A/P. Sanjay Swarup for their kind acceptance to be on the panel of examiners and for valuable suggestions for planning this research during the qualifying exam. I also thank the final reviewers for spending time on evaluating this thesis. I also express my gratitude to Dr. Lakshminarayanan for his valuable suggestions at different times during my PhD. I wish to admire and thank all the unknown reviewers of our publications, who gave constructive feedbacks on all our manuscripts and helped us to bring the best out of this research. I also take this opportunity to appreciate and thank all those dedicated researchers who shared their research in the form of literature, website notes, and freely available online data. These informations have played a major part in strengthening this research work. I also express my gratitude to all the professors at ChBE/NUS whose valuable lectures/seminars have resulted into good ideas for this research. Special thanks to Prof. I. A. Karimi and Prof. Rangaiah for giving me an opportunity to teach undergraduate students (it was en enriching experience for me at NUS) that fetched me best tutor award. It is indeed an honor. I also thank ChBE department for financially supporting all my conference visits. Special thanks to all my labmates and other friends at NUS (if I start naming them the list would keep rolling) for their affectionate support and interactions that made my journey in NUS, a wonderful experience. I would also thank all the GSA office bearers for helping me in one way or the other and bringing the best out of me at different times during GSA activities. Lastly, I thank all my professors, students and affectionate friends who trained and inspired me to be what I am today. I will cherish this wonderful journey for long. And most importantly, I thank my parents (Mr. Selvarasu, and Mrs. Vijaya), my sister (Mrs. Veni), my niece and nephew (Dharani and Nirmal) for always being my source of inspiration. Their love, continued support and motivation were the main driving force for me during my PhD. I am ever grateful and indebted for their care and affection. Contents ________________________________________________________________________ Table of Contents List of Tables . v List of Figures . vi Nomenclature x Abbreviations . xi SUMMARY . xii Introduction . 1.1 Cellular organisms and their complex functioning . 1.2 Systems biology a new paradigm in biological research 1.2.1 Knowledge required for systems biology . 1.2.2 Approaches in systems biology 1.2.3 Opportunities to unravel biological functions . 1.3 Analysis techniques available in the data rich environment . 1.4 Motivation for research . 1.5 Scope of the present work . 10 1.6 Organization of the thesis 11 Modeling and analysis of biological systems: An overview . 14 2.1 Tools available for modeling biological systems 15 2.2 Genome-scale modeling 17 2.3 Constraints-based modeling approach . 18 2.4 Other metabolic network simulations 19 2.5 Algorithms available for characterizing metabolic networks 27 2.6 Systems biotechnology: An approach for systematic strain improvement . 29 2.7 In silico techniques available for strain improvement . 29 2.8 Tools for multivariate data analyses 31 2.9 Research directions 33 Framework for combined analysis using statistical and in silico approaches . 35 3.1 Introduction . 35 3.2 Experimental data and their trend . 36 3.3 Data preprocessing and elemental balancing 38 i Contents ________________________________________________________________________ 3.3.1 Cumulative consumption and specific rates calculation . 39 3.4 Multivariate statistical data analysis (PCA and PLS) . 42 3.4.1 Principal component analysis (PCA) 42 3.4.2 Partial least squares regression (PLS) . 43 3.5 In silico modeling and analysis . 43 3.5.1 Metabolic network reconstruction 45 3.5.2 Constraints-based flux analysis . 48 3.6 Application of the framework . 50 Application of framework for characterizing Escherichia coli DH5α growth and metabolism in a complex medium 51 4.1 Introduction . 51 4.2 Materials and methods . 53 4.2.1 Strains and culture conditions . 53 4.2.2 Analytical techniques 54 4.2.3 Data preprocessing for statistical analysis 55 4.2.4 Constraints-based flux analysis . 55 4.3 Results and discussion . 56 4.3.1 Growth, metabolite uptake and excretion profiles during batch culture . 56 4.3.2 Elemental balancing 60 4.3.3 Multivariate statistical analysis . 60 4.3.4 In silico metabolic flux analysis . 63 4.3.5 Sensitivity analysis of amino acid and glucose consumption . 71 4.3.6 Analysis of the metabolite consumption and utilization . 72 4.3.7 Availability of other nutrients in the medium . 80 4.3.8 Exploring the statistical analysis results using in silico analysis 82 4.4 Concluding remarks 84 Genome-scale modeling and in silico analysis of mouse cell metabolism 86 5.1 Introduction . 86 5.2 Materials and methods . 88 5.2.1 Metabolic network reconstruction 88 5.2.2 Network visualization . 91 ii Contents ________________________________________________________________________ 5.2.3 Statistical network analysis . 92 5.2.4 Constraints-based flux analysis . 92 5.3 Results and discussion . 93 5.3.1 Genome-scale reconstruction of mouse metabolic network . 93 5.3.2 Comparison of mouse model with yeast and E. coli genome-scale models 97 5.3.3 In silico model validation 99 5.3.4 Structural and functional characterization of mouse metabolism . 104 5.3.5 Important role of lipid pathway in mouse metabolism . 112 5.3.6 Alternate flux distributions and flux variations 114 5.4 Conclusion . 116 Application of framework to elucidate mouse hybridoma cell growth and metabolism in a fed-batch culture . 118 6.1 Introduction . 118 6.2 Materials and methods . 121 6.2.1 Cell line and culture medium 121 6.2.2 Analytical techniques 122 6.2.3 Data preprocessing for statistical analysis 122 6.2.4 Constraints-based flux analysis . 124 6.3 Results and discussion . 125 6.3.1 Fed batch cell culture 125 6.3.2 Elemental Balancing on Fed-batch Data . 130 6.3.3 Multivariate Statistical Analysis . 132 6.3.4 In silico metabolic flux analysis . 136 6.3.5 Other possible cellular objectives . 153 6.3.6 Understanding cellular behavior from combined analysis 154 6.4 Conclusion . 157 Identification of necessary genes and evaluating their perturbations for strain improvement in E. coli 159 7.1 Introduction . 159 7.2 Algorithm for identifying sufficient and necessary genes . 160 iii Contents ________________________________________________________________________ 7.2.1 Mathematical formulations and algorithm 161 7.2.2 Identifying set of necessary genes 163 7.3 Application of the algorithm . 165 7.3.1 Analysis in E. coli DH5α metabolic network . 165 7.4 Application of the necessary gene sets to identify knockout combinations for succinate production . 168 7.5 Concluding remarks 172 Contributions and future recommendations 173 8.1 Summary of the contributions . 173 8.2 Future directions 177 8.2.1 Expanding the horizon of mouse cell metabolism 177 8.2.2 Reconstruction of metabolic network of CHO cell lines 180 References 182 Appendices . 197 List of Publications . 198 VITAE 200 iv Tables ________________________________________________________________________ List of Tables Table Page 2.1 List of available genome-scale models for various organisms . 20 3.1 List of public resources available for reconstruction of genome-scale metabolic models* 47 4.1 Comparison of metabolic reaction fluxes of amino acids biosynthetic reactions . 73 4.2 Sensitivity of amino acids, glucose and trehalose uptake on cell biomass production in phase 1a . 74 4.3 Sensitivity of amino acids, glucose and trehalose uptake on cell biomass production in phase 2a . 75 4.4 Consumption or production of amino acids for biosynthetic demand as well as for other metabolites production in phase 1a 77 4.5 Consumption or production of amino acids for biosynthetic demand as well as for other metabolites production in phase 2a 78 4.6 Comparison of ATP consuming metabolic pathways for complex and minimal medium conditions 79 5.1 Online resources for reconstructing genome-scale mouse metabolic network . 89 5.2 Characteristics of the mouse genome-scale metabolic network and its comparison with the previous generic model . 95 5.3 Comparison of mouse genome-scale network characteristics with yeast and E. coli networks 98 5.4 Comparison of conserved reactions among the three genome-scale metabolic models . 98 5.5 Comparison of essential genes for cell growth between in silico and in vivo experiments . 103 6.1 Summary of specific consumption or production rate of measured metabolites during the exponential growth phase of the cell culture a . 123 6.2 Production and utilization of pyruvate in central metabolism during the exponential growth phase of the cell culture a 141 6.3 Energy production from central carbon metabolism in all statesa . 144 7.1 List of necessary reactions for both cell growth and succinate production 167 7.2 List of double knockout gene combinations that enhances succinate production in E. coli DH5α . 170 v Figures ________________________________________________________________________ List of Figures Figure Page 1.1 Interaction of the different expertise in performing a systems biology research. . 1.2 Flowchart showing the major focus of the current research work and the organization of the addressed research issues in different chapters of the thesis. 13 2.1 Genome-scale reconstruction of metabolic network and elucidation of the systemic properties using constraints-based analysis approach . 22 3.1 Schematic illustration of the workflow involved in the analysis using combined statistical and in silico framework 37 4.1 Profiles of optical cell density and residual concentration of various nutrient components and products in the complex medium. Highlighted regions correspond to three different growing phases of the culture. Phase 1: initial exponential growth phase; phase 2: late exponential growth phase; phase 3: acetate consumption phase. A: Optical density values (OD600), concentration of glucose, trehalose and acetate. B: concentration of amino acids which were rapidly consumed; L-aspartate (ASP), glycine (GLY), L-proline (PRO), L-methionine (MET), L-serine (SER), Lasparagine (ASN), L-tyrosine (TYR), L-threonine (THR), L-glutamate (GLU) and L-alanine (ALA). C: concentration of amino acids which were not completely consumed; L-valine (VAL), L-lysine (LYS), L-isoleucine (ILE), L-leucine (LEU), L-phenylalanine (PHE), L-histidine (HIS) and L-arginine (ARG). 59 4.2 Results obtained from multivariate statistical analysis . 61 4.3 Results of PLS analysis. Black arrows indicate positive correlation between those amino acids and cell growth. Dotted arrows indicate positive correlation between those amino acids and acetate production. The negative effect of set of amino acids on acetate is shown using bold lines and on cell growth is shown with dashed line. A: correlation based on PLS and B: strategies for feed medium design for enhancing cell viability. 62 4.4 Specific consumption rates of all the measured nutrients and specific growth rate during initial exponential phase (phase 1) and the late growth phase (phase 2). The value for histidine in phase corresponds to its specific production rate. The rates are ranked according to their specific consumption rates in phase 65 4.5 Schematic diagrams of metabolic flux distributions and flux-sum across the metabolites serine, pyruvate and acetate. A: Metabolic flux distribution across the central metabolic pathways and amino acids biosynthetic pathways during the exponential growth phase (phase 1: underlined) and late growth phase (phase 2: normal) of the microbial culture. Reactions with higher flux values are highlighted with red (phase 1) and green (phase 2). Serine, pyruvate and acetate are highlighted with squares. B: consumption and production of the metabolites serine, pyruvate and acetate are shown using the flux-sum values across each of the vi Figures ________________________________________________________________________ metabolites for phase and phase 2. Percentage contributions to each of the metabolites are also shown. PEP, Phosphoenolpyruvate; GLC, glucose; PYR, pyruvate; GLY, glycine; TRE, trehalose; MAL, L-malate; TRP, L-tryptopan; ALAC-S, (S)-2-acetolactate; ACCOA, acetyl coenzyme A; 23DHDP, 2,3dihydrodipicolinate; 2AHBUT, (S)-2-Aceto-2-hydroxybutanoate; ACSER, Oacetyl-L-serine; PS_EC, phosphatidylserine; CIT, citrate. Annotation of other metabolites follows that of the iJR904 model (Reed et al., 2003) 68 4.6 Interpretation of statistical and in silico analysis results. A: set of positively correlated amino acids with cell growth and acetate production and the intracellular conversion of amino acids into various metabolites. B: the plausible effect of reducing amino acids (gly, ile, val and his) in the complex medium at the intracellular level. Arrow with bold outline: positive correlation with cell growth and arrow with dashed line: positive correlation with acetate production 83 5.1 Schematic representation of the iterative approach employed in the reconstruction and analysis of genome-scale mouse model. The existing model was used as template and the network was expanded by compiling the information (genome, biochemical and mouse physiological data). Missing links and redundant reactions were then identified to refine the model with such available resources. The resultant expanded model underwent the validation process using constraints-based flux analysis with cell culture and in vivo gene essentiality data for verifying the prediction. The presence of knowledge gaps was explored and again the model can be improved interactively. Subsequently, the model was analyzed both structurally and functionally to characterize mouse metabolism and identify key pathways, reactions and metabolites. . 90 5.2 Functional classifications of metabolic reactions in mouse genome-scale model, (A) current updated model and (B) old model. Numbers on pie charts indicate reactions in each subsystem. Metabolic subsystems with number of gene and nongene associated reactions are detailed in the table 96 5.3 Comparison of metabolites across mouse, yeast and E. coli genome-scale models. Metabolites from cytosol were only considered for comparison 99 5.4 Comparison of in silico growth rate with experimentally observed growth rate during batch culture. Specific growth rate is in h-1; mAb production rate in mg gDCW-1 h-1. 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Glutamine - a major energy-source for cultured mammalian-cells. Fed. Proc. 43(1):121-125. Zinser ER, Kolter R. 2004. Escherichia coli evolution during stationary phase. Res. Microbiol. 155(5):328-336. Zupke C, Sinskey AJ, Stephanopoulos G. 1995. Intracellular flux analysis applied to the effect of dissolved oxygen on hybridomas. Appl. Microbiol. Biotechnol. 44:27-36. Zupke C, Stephanopoulos G. 1995. Intracellular flux analysis in hybridomas using mass balances and in vitro 13C NMR. Biotechnol. Bioeng. 45(4):292-303. 196 Appendices ________________________________________________________________________ Appendices Appendices are available in the attached CD-ROM. The list includes the following. Appendix A: Mouse cell biomass composition Appendix B: Metabolic reactions in reconstructed mouse cell network Appendix C: Metabolites present in mouse cell network Appendix D: Dead end metabolites present in mouse cell network Appendix E: Lethal double knockout gene combinations for cell growth in mouse metabolism Appendix F: Growth and feed media compositions for mouse hybridoma fed-batch culture 197 Publications ________________________________________________________________________ List of Publications Journal Publications 1. Selvarasu S, Lee D-Y, Karimi I A. 2007. Identifying synergistically switching pathways for multi-product strain improvement using multiobjective flux balance analysis. Computer-Aided Chemical Engineering, 24: 1007-1012. 2. Selvarasu S, Ow D S-W, Lee S Y, Lee M M, Oh S K-W, Karimi I A, Lee D-Y. 2009. Characterizing Escherichia coli DH5α growth and metabolism in a complex medium using genome-scale flux analysis. Biotechnology and Bioengineering, 102(3):923-934. 3. Selvarasu S, Wong V. V. T, Karimi I. A, Lee D-Y. 2008. Elucidation of metabolism in hybridoma cells grown in fed-batch culture by genome-scale modeling. Biotechnology and Bioengineering, 102(5):1494-1504. 4. Selvarasu S, Karimi I. A, Lee D-Y. 2009. Genome-scale modeling and in silico analysis of mouse cell metabolic network. Molecular Biosystems, in press (DOI: 10.1039/B912865d). 5. Selvarasu S, Karimi I. A, Lee D-Y. Data preprocessing and multivariate statistical analysis of hybridoma cells grown in fed-batch culture. (to be submitted). 6. Selvarasu S, Karimi I. A, Lee D-Y. An efficient optimization technique to identify candidate gene sets for deletion studies and strain improvement. (to be submitted). 198 Publications ________________________________________________________________________ Conference proceedings and presentations 1. Selvarasu S, Karimi I A, Lee D-Y. Metabolic flux analysis of complex biological systems. 2nd Annual graduate students’ symposium 2005, NUS. Singapore. 2. Selvarasu S, Lee D-Y, Karimi I A. In silico Analysis of mouse hybridoma Cells for the enhanced production of monoclonal antibodies. 3rd Annual Graduate students’ symposium 2006, NUS, Singapore. 3. Selvarasu S, Lee D-Y, Karimi I A. Systemic analysis of reconstructed metabolic network of Mus musculus for IFN-γ production. INFORMS 2006, Hong Kong, China. 4. Selvarasu S, Lee D-Y, Wong V V T, Karimi I A. In silico modeling and simulation of mouse hybridoma cells for the enhanced production of monoclonal antibodies. AIChE 2006, San Francisco, USA. 5. Selvarasu S, Lee D-Y, Karimi I A. Multi-objective flux balance analysis of E. coli metabolic network for the enhanced production of biotechnological products. Joint third AOHPO and fourth Structural Biology and Functional Genomics Conference 2006, NUS, Singapore. 6. Saha R, Selvarasu S, Park W, Lee D-Y, Karimi I A. Microbial fuel cell in perspective of strain improvement and mediator selection. Joint third AOHPO and fourth Structural Biology and Functional Genomics Conference 2006, NUS, Singapore. 7. Selvarasu S, Lee D-Y, Karimi I A. Identifying Synergistically switching pathways for the multiproduct strain improvement using multiobjective flux balance analysis. ESCAPE-17, June 27-30, 2007, Bucharest, Romania. 8. Saha R, Selvarasu S, Park W, Lee D-Y, Karimi I A. Strain improvement and mediator selection for microbial fuel cell by genome scale in silico model. ESCAPE-17, June 27-30, 2007, Bucharest, Romania. 9. Selvarasu S, Lee D-Y, Karimi I A. Combined in silico model and data driven analysis for multi-product strain improvement. 2007 Annual Meeting and International Symposium, Korean Society for Microbial Metabolism. June 28-29, 2007, Korea. 10. Selvarasu S, Lee D-Y, Wong V. V. T, Karimi I. A. Genome-Scale Flux Analysis and Multivariate Data Analysis of Hybridoma Cells Producing Monoclonal Antibodies. Asia Pacific Biochemical Engineering Conference. Nov 4-7, 2007, Taiwan 11. Selvarasu S, Lee D-Y, Wong V. V. T, Karimi I. A. In silico Flux Analysis and Statistical Analysis of Hybridoma Cells Producing Monoclonal Antibodies. The 18th International Conference on Genome Informatics (GIW 2007). Dec 3-5, 2007, Singapore. 12. Selvarasu S, Lee D-Y, Karimi I. A. Combined Statistical and Genome-Scale Analysis of Mammalian Cell Lines Producing Bio-Therapeutics. AIChE 2008, Philadelphia, Nov 1621, USA. 199 Vitae ________________________________________________________________________ VITAE SURESH SELVARASU Bioprocessing Technology Insititute, 20 Biopolis Way, #06-01, Centros, Singapore-138668 Tel: (HP): (65) 9116 9596 (Off): (65) 6407 0922 Email: suresh_selvarasu@bti.a-star.edu.sg RESEARCH INTERESTS Computational and systems biology, network optimization, multivariate statistics, mathematical modeling, and machine learning PERSONAL PROFILE Nationality Date of Birth Current Residence Gender Marital Status : : : : : Indian 24 Nov 1981 Singapore Male Single ACADEMIC PROFILE National University of Singapore, Singapore PhD Chemical & Biomolecular Engineering Principal Advisors: Prof. IA Karimi & Dr. Dong-Yup Lee Thesis: A combined statistical and in silico framework for analysis and characterization of microbial and mammalian metabolic networks Jan 2009 University of Madras, Chennai, India B.Tech Chemical Engineering CGPA – (3.74 / 4) July 2003 HONORS & ACTIVITIES NUS Research Fellowship – Jan 2005 to Dec 2008 Best Tutor Award (Sem I, 2006-07; Course : Design I) General Secretary, Graduate Students’ Association, National University of Singapore, Singapore (2007 – 2008). Treasurer, Graduate Students’ Association, National University of Singapore, Singapore (2005 – 2006). TEACHING EXPERIENCE Tutor / Lab Demonstrator, National University of Singapore TC1406 Computer Applications (Sem II, 2006-2007; 2007-2008) CN3121 Process Dynamics & Control Lab (Sem I, 2005-2006) CN4119 Design I (Sem I, 2005-2006; 2006-2007 & 2007-2008) 200 Vitae ________________________________________________________________________ CN5111 Optimization in Chemical Processes (Sem I, 2008-2009) RESEARCH EXPERIENCE Research Scientist (Jan 2009 onwards) Bioprocessing Technology Institute, Singapore Optimization and Computational Biology Group (April 2005 to Dec 2008) Thesis: A combined statistical and in silico framework for analysis and characterization of microbial and mammalian metabolic networks Institute: National University of Singapore, Singapore Advisors: Prof. I. A. Karimi & Dr. Dong-Yup Lee o Reconstructed a metabolic network of Mus musculus o Compared the in silico predictions with experimental data and identified strategies to improve the production of monoclonal antibodies (Hu-IgG1) o Analyzed E. coli metabolism under complex nutrient medium condition and identified strategies to improve cell growth o Obtained excellent exposure in using GAMS optimization tool and MATLAB o Developed novel optimization algorithms to analyze biological systems Division of Environmental Technology (July 2003 to Dec 2003) Research Project: Catalytic treatment of saline water from leather waste water Institute: Central Leather Research Institute, Chennai, India Advisor: Dr. G. Sekaran Department of Chemical Engineering (Apr 2002 to Apr 2003) Research Project: Simulation and Optimization of Ethanol Dehydration Institute: Sri Venkateswara College of Engineering, Tamil Nadu, India University: University of Madras, Chennai, India Advisor: Mr. A. K. Mohanasundaram PEER REVIEWED PUBLICATIONS Selvarasu S, Lee D-Y, Karimi I A. 2007. Identifying synergistically switching pathways for multi-product strain improvement using multiobjective flux balance analysis. Computer-Aided Chemical Engineering, 24: 1007-1012. Selvarasu S, Ow D S-W, Lee S Y, Lee M M, Oh S K-W, Karimi I A, Lee D-Y. 2009. Characterizing Escherichia coli DH5α growth and metabolism in a complex medium using genome-scale flux analysis. Biotechnology and Bioengineering, 102(3):923-934. Selvarasu S, Wong V. V. T, Karimi I. A, Lee D-Y. 2008. Elucidation of metabolism in hybridoma cells grown in fed-batch culture by genome-scale modeling. Biotechnology and Bioengineering, 102(5):1494-1504. Selvarasu S, Karimi I. A, Ghim G-H, Lee D-Y. Genome scale modeling and analysis of mouse cell metabolic network. Molecular Biosystems, in press (DOI: 10.1039/B912865d). 201 Vitae ________________________________________________________________________ Selvarasu S, Karimi I. A, Lee D-Y. Data preprocessing and multivariate statistical analysis of hybridoma cells grown in fed-batch culture (to be submitted). Selvarasu S, Karimi I. A, Lee D-Y. An efficient optimization technique to identify candidate gene sets for deletion studies and strain improvement (to be submitted). PROCEEDINGS/PRESENTATIONS Selvarasu S, Karimi I A, Lee D-Y. Metabolic flux analysis of complex biological systems. 2nd Annual graduate students’ symposium 2005, NUS. Singapore. Selvarasu S, Lee D-Y, Karimi I A. In silico Analysis of mouse hybridoma Cells for the enhanced production of monoclonal antibodies. 3rd Annual Graduate students’ symposium 2006, NUS, Singapore. Selvarasu S, Lee D-Y, Karimi I A. Systemic analysis of reconstructed metabolic network of Mus musculus for IFN-γ production. INFORMS 2006, Hong Kong, China. Selvarasu S, Lee D-Y, Wong V V T, Karimi I A. In silico modeling and simulation of mouse hybridoma cells for the enhanced production of monoclonal antibodies. AIChE 2006, San Francisco, USA. Selvarasu S, Lee D-Y, Karimi I A. Multi-objective flux balance analysis of E. coli metabolic network for the enhanced production of biotechnological products. Joint third AOHPO and fourth Structural Biology and Functional Genomics Conference 2006, NUS, Singapore. Saha R, Selvarasu S, Park W, Lee D-Y, Karimi I A. Microbial fuel cell in perspective of strain improvement and mediator selection. Joint third AOHPO and fourth Structural Biology and Functional Genomics Conference 2006, NUS, Singapore. Selvarasu S, Lee D-Y, Karimi I A. Identifying Synergistically switching pathways for the multiproduct strain improvement using multiobjective flux balance analysis. ESCAPE-17, June 27-30, 2007, Bucharest, Romania. Saha R, Selvarasu S, Park W, Lee D-Y, Karimi I A. Strain improvement and mediator selection for microbial fuel cell by genome scale in silico model. ESCAPE-17, June 27-30, 2007, Bucharest, Romania. Selvarasu S, Lee D-Y, Karimi I A. Combined in silico model and data driven analysis for multi-product strain improvement. 2007 Annual Meeting and International Symposium, Korean Society for Microbial Metabolism. June 28-29, 2007, Korea. Selvarasu S, Lee D-Y, Wong V. V. T, Karimi I. A. Genome-Scale Flux Analysis and Multivariate Data Analysis of Hybridoma Cells Producing Monoclonal Antibodies. Asia Pacific Biochemical Engineering Conference. Nov 4-7, 2007, Taiwan Selvarasu S, Lee D-Y, Wong V. V. T, Karimi I. A. In silico Flux Analysis and Statistical Analysis of Hybridoma Cells Producing Monoclonal Antibodies. The 18th International Conference on Genome Informatics (GIW 2007). Dec 3-5, 2007, Singapore. Selvarasu S, Lee D-Y, Karimi I. A. Combined Statistical and Genome-Scale Analysis of Mammalian Cell Lines Producing Bio-Therapeutics. AIChE 2008, Philadelphia, Nov 1621, USA. 202 Vitae ________________________________________________________________________ TECHNICAL SKILLS & COURSE WORK Graduate coursework includes: Advanced Chemical Engineering Thermodynamics, Advanced Chemical Reaction Engineering, Computational Systems Biology, Advanced Separation Process, Mathematical Methods in Chemical & Environmental Engineering Undergraduate coursework includes: Fluid Mechanics, Heat transfer, Mass Transfer I & II, Transport Phenomena, Process Calculations, Numerical Methods in Chemical Engineering, Physical Chemistry, Organic Chemistry, Reaction Engineering, Unit Operations in Chemical Engineering, Chemical Engineering Thermodynamics, Biochemical Engineering, Environmental Engineering C and FORTRAN programming experience, hands on knowledge with FLEX Extensive knowledge and familiarity with GAMS, MetaFluxNet, MATLAB and HYSYS tools ADDITIONAL TRAINING Communication skills workshop, NUS, March, 2007 Microteaching and Tutoring skills, August, 2006 REFERENCES Prof. I. A. Karimi E5-02-12, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore-117576 Tel: (65) 6516 6359 Email: cheiak@nus.edu.sg Dr. Dong-Yup Lee E5-03-16, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore-117576 Tel: (65) 6516 6907 Email: cheld@nus.edu.sg Dr. Lakshminarayanan S. E5-02-23, Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore-117576 Tel: (65) 6516 8484 Email: chels@nus.edu.sg 203 [...]... computational analysis of complex networks Statistical analysis techniques - various techniques available for performing data mining and preprocessing of experimental data obtained from different cell culture experiments Microbial and mammalian metabolism - available genomic and biochemical information for microbial and mammalian metabolic systems, their biotechnological applications, and limitations 8 Chapter... biotechnological and biomedical processes Huge amount of data sets are available in the public databases and it is possible to do vast database searches and data mining to extract the information of biological interest Increasing number of genomic projects has also accelerated the availability of datasets that provide information on gene, protein and physiological data of multitude of organisms Most of these... understanding of the behavior of the biological systems in particular, microbial and mammalian systems • Challenges in addressing the complexity of data obtained from experiments such as batch and fed-batch fermentation cultures • Challenges in integrating and applying the data analysis techniques that are available for these fermentation processes • Challenges involved in the reconstruction of genome-scale... development of the combined framework using multivariate statistical analysis techniques and in silico modeling approaches for characterizing cell culture fermentation and exploring the internal cell metabolism The most relevant statistical methods for examining the experimental data are described Subsequently, various steps and procedure involved in reconstructing a genome-scale metabolic model and conducting... biological system (to start with a small subsystem of a single cell) and then predicting dynamics over time Figure 1.1 Interaction of the different expertise in performing a systems biology research Advanced technical expertise from bioinformatics, computation, statistical analysis, and mathematical modeling are all pivotal for integrating and making sense of large and complex datasets generated through high... Abbreviations FBA Flux Balance Analysis GAMS The General Algebraic Modeling System IgG1 Immunoglobulin G LP Linear Programming mAb Monoclonal Antibody MDS Multidimensional Scaling MFA Metabolic Flux Analysis MILP Mixed Integer Linear Programming MINLP Mixed Integer Nonlinear Programming MOMA Minimization of Metabolic Adjustments OMNI Optimal Metabolic network identification PCA Principal Component... objectives of the current research work It involves developing a frame work for modeling and analyzing microbial and mammalian systems using a combined statistical and in silico approach to gain insights about the effect of external cellular environment on the internal cell metabolic behavior This would enable us to infer the systemic properties of the networks and propose testable hypotheses for cellular... grown in a complex medium Highly correlated nutrients from the culture media were obtained using statistical analysis and the effect of nutrient consumption on intracellular metabolism was explored using constraints-based genome-scale modeling Application to mammalian system: The third part of the thesis considers analysis and characterization of mammalian metabolic system In this case, mouse cell lines... concentrations C: Concentration profiles of all essential amino acids D: Non-essential amino acids concentrations mAb- monoclonal antibodies (IgG1); ARG- arginine; THRthreonine; SER- serine; GLY- glycine; TYR- tyrosine; PHE- phenylalanine; METmethionine; HIS- histidine; ASN- asparagine; ASP- aspartate; LYS- lysine; VALvaline; ILE- isoleucine; GLU- glutamate; LEU- leucine; ALA- alanine; GLNglutamine; GLC-... is mainly on the modeling and analysis approaches, this section reviews some of the important tools and techniques that are used for modeling of biological systems and in particular metabolic networks Many modeling approaches are currently being used to model cellular processes Due to the presence of many parameters, variables and constraints a variety of numerical and computational techniques are . A COMBINED STATISTICAL AND IN SILICO FRAMEWORK FOR ANALYSIS AND CHARACTERIZATION OF MICROBIAL AND MAMMALIAN METABOLIC NETWORKS SELVARASU SURESH (B. Tech, University of Madras,. Elemental balancing 60 4.3.3 Multivariate statistical analysis 60 4.3.4 In silico metabolic flux analysis 63 4.3.5 Sensitivity analysis of amino acid and glucose consumption 71 4.3.6 Analysis of. for multivariate data analyses 31 2.9 Research directions 33 3 Framework for combined analysis using statistical and in silico approaches 35 3.1 Introduction 35 3.2 Experimental data and their