System estimation from metabolic time-series data

MOTIVATION At the center of computational systems biology are mathematical models that capture the dynamics of biological systems and offer novel insights. The bottleneck in the construction of these models is presently the identification of model parameters that make the model consistent with observed data. Dynamic flux estimation (DFE) is a novel methodological framework for estimating parameters for models of metabolic systems from time-series data. DFE consists of two distinct phases, an entirely model-free and assumption-free data analysis and a model-based mathematical characterization of process representations. The model-free phase reveals inconsistencies within the data, and between data and the alleged system topology, while the model-based phase allows quantitative diagnostics of whether--or to what degree--the assumed mathematical formulations are appropriate or in need of improvement. Hallmarks of DFE are the facility to: diagnose data and model consistency; circumvent undue compensation of errors; determine functional representations of fluxes uncontaminated by errors in other fluxes and pinpoint sources of remaining errors. Our results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data. Its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.

[1]  H. Iba,et al.  Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  R. Heinrich,et al.  The Regulation of Cellular Systems , 1996, Springer US.

[3]  Ana Rute Neves,et al.  Metabolism of lactic acid bacteria studied by nuclear magnetic resonance , 2004, Antonie van Leeuwenhoek.

[4]  Eberhard O. Voit,et al.  Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists , 2000 .

[5]  J S Almeida,et al.  Metabolic characterization of Lactococcus lactis deficient in lactate dehydrogenase using in vivo 13C-NMR. , 2000, European journal of biochemistry.

[6]  Jun Kikuchi,et al.  Towards dynamic metabolic network measurements by multi-dimensional NMR-based fluxomics. , 2007, Phytochemistry.

[7]  Michiel Kleerebezem,et al.  Effect of Different NADH Oxidase Levels on Glucose Metabolism by Lactococcus lactis: Kinetics of Intracellular Metabolite Pools Determined by In Vivo Nuclear Magnetic Resonance , 2002, Applied and Environmental Microbiology.

[8]  Jan Kok,et al.  Overview on sugar metabolism and its control in Lactococcus lactis - the input from in vivo NMR. , 2005, FEMS microbiology reviews.

[9]  E O Voit,et al.  A pharmacodynamic model for the action of the antibiotic imipenem on Pseudomonas aeruginosa populations in vitro. , 1996, Bulletin of mathematical biology.

[10]  Kazuyuki Shimizu,et al.  Quantitative analysis of intracellular metabolic fluxes using GC-MS and two-dimensional NMR spectroscopy. , 2002, Journal of bioscience and bioengineering.

[11]  B. Palsson,et al.  Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli , 2003, Journal of bacteriology.

[12]  Jonas S. Almeida,et al.  Parameter optimization in S-system models , 2008, BMC Systems Biology.

[13]  M. Savageau Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology , 1976 .

[14]  Samuel Kaplan,et al.  A computational strategy to analyze label-free temporal bottom-up proteomics data. , 2008, Journal of proteome research.

[15]  Prospero C. Naval,et al.  Parameter estimation using Simulated Annealing for S-system models of biochemical networks , 2007, Bioinform..

[16]  Christoph Wittmann,et al.  Fluxome analysis using GC-MS , 2007, Microbial cell factories.

[17]  W. Knorre,et al.  M. A. Savageau, Biochemical Systems Analysis. A Study of Function and Design in Molecular Biology. 396 S., 115 Abb., 14 Tab. Reading, Mass. 1976. Addison‐Wesley Pbl. Co./Advanced Book Program. £ 26,50 , 1979 .

[18]  G. Gavalas Nonlinear Differential Equations of Chemically Reacting Systems , 1968 .

[19]  Nobuyoshi Ishii,et al.  Distinguishing enzymes using metabolome data for the hybrid dynamic/static method , 2007, Theoretical Biology and Medical Modelling.

[20]  Eberhard O. Voit,et al.  Flux-based estimation of parameters in S-systems , 1996 .

[21]  G. Stephanopoulos,et al.  Metabolic Engineering: Principles And Methodologies , 1998 .

[22]  Z. Kutalik,et al.  S-system parameter estimation for noisy metabolic profiles using newton-flow analysis. , 2007, IET systems biology.

[23]  Eberhard O Voit,et al.  Theoretical Biology and Medical Modelling , 2022 .

[24]  M. Kleerebezem,et al.  In vivo nuclear magnetic resonance studies of glycolytic kinetics in Lactococcus lactis. , 1999, Biotechnology and bioengineering.

[25]  Eberhard O. Voit,et al.  Power-Law Approach to Modeling Biological Systems : I. Theory , 1982 .

[26]  G. Stephanopoulos CHAPTER 1 – The Essence of Metabolic Engineering , 1998 .

[27]  Paul Horton,et al.  Inference of Scale-free Networks from Gene Expression Time Series , 2006, J. Bioinform. Comput. Biol..

[28]  S. Kimura,et al.  Inference of S-system Models of Genetic Networks from Noisy Time-series Data , 2004 .

[29]  Michael H. Kutner Applied Linear Statistical Models , 1974 .

[30]  Ana Rute Neves,et al.  Engineering Lactococcus lactis for Production of Mannitol: High Yields from Food-Grade Strains Deficient in Lactate Dehydrogenase and the Mannitol Transport System , 2004, Applied and Environmental Microbiology.

[31]  Shuhei Kimura,et al.  Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm , 2005, Bioinform..

[32]  B. Palsson Systems Biology: Properties of Reconstructed Networks , 2006 .

[33]  Eberhard O. Voit,et al.  Challenges for the identification of biological systems from in vivo time series data , 2004, Silico Biol..

[34]  Ana Rute Neves,et al.  Catabolism of mannitol in Lactococcus lactis MG1363 and a mutant defective in lactate dehydrogenase. , 2002, Microbiology.

[35]  Takanori Ueda,et al.  Inference of Genetic Network Using the Expression Profile Time Course Data of Mouse P19 Cells , 2002 .

[36]  Ana Rute Neves,et al.  Effect of pyruvate kinase overproduction on glucose metabolism of Lactococcus lactis. , 2004, Microbiology.

[37]  A. Neves,et al.  Is the glycolytic flux in Lactococcus lactis primarily controlled by the redox charge? Kinetics of NAD(+) and NADH pools determined in vivo by 13C NMR. , 2002, The Journal of biological chemistry.

[38]  Eberhard O Voit,et al.  Neural-network-based parameter estimation in S-system models of biological networks. , 2003, Genome informatics. International Conference on Genome Informatics.

[39]  Byoung-Tak Zhang,et al.  Multi-stage Evolutionary Algorithms for Efficient Identification of Gene Regulatory Networks , 2006, EvoWorkshops.

[40]  Eberhard O. Voit,et al.  Parameter estimation of S-distributions with alternating regression. , 2007 .

[41]  Jonas S. Almeida,et al.  Decoupling dynamical systems for pathway identification from metabolic profiles , 2004, Bioinform..

[42]  Jonas S. Almeida,et al.  Automated smoother for the numerical decoupling of dynamics models , 2007, BMC Bioinformatics.

[43]  Feng-Sheng Wang,et al.  Evolutionary optimization with data collocation for reverse engineering of biological networks , 2005, Bioinform..

[44]  Masaru Tomita,et al.  Dynamic modeling of genetic networks using genetic algorithm and S-system , 2003, Bioinform..

[45]  H. Bonarius,et al.  Flux analysis of underdetermined metabolic networks: the quest for the missing constraints. , 1997 .

[46]  Rui Oliveira,et al.  Combining metabolic flux analysis tools and 13C NMR to estimate intracellular fluxes of cultured astrocytes , 2008, Neurochemistry International.

[47]  Pei Yee Ho,et al.  Multiple High-Throughput Analyses Monitor the Response of E. coli to Perturbations , 2007, Science.

[48]  E. Voit,et al.  Regulation of glycolysis in Lactococcus lactis: an unfinished systems biological case study. , 2006, Systems biology.

[49]  Byoung-Tak Zhang,et al.  Identification of biochemical networks by S-tree based genetic programming , 2006, Bioinform..