Metabolic modelling in the development of cell factories by synthetic biology

Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.

[1]  L. Quek,et al.  OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis , 2009, Microbial cell factories.

[2]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

[3]  Jason A. Papin,et al.  Genome-scale microbial in silico models: the constraints-based approach. , 2003, Trends in biotechnology.

[4]  Yaniv Lubling,et al.  An integrated open framework for thermodynamics of reactions that combines accuracy and coverage , 2012, Bioinform..

[5]  Eduardo Agosin,et al.  Expanding a dynamic flux balance model of yeast fermentation to genome-scale , 2011, BMC Systems Biology.

[6]  Intawat Nookaew,et al.  Genome-scale metabolic reconstructions of Pichia stipitis and Pichia pastoris and in silico evaluation of their potentials , 2012, BMC Systems Biology.

[7]  Steffen Klamt,et al.  CASOP: a computational approach for strain optimization aiming at high productivity. , 2010, Journal of biotechnology.

[8]  Masanori Arita.  In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism. , 2003, Genome research.

[9]  Ari Rantanen,et al.  FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data. , 2008, Rapid communications in mass spectrometry : RCM.

[10]  Desmond S. Lun,et al.  Truncated branch and bound achieves efficient constraint-based genetic design , 2012, Bioinform..

[11]  Reinhart Heinrich,et al.  A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. , 1974, European journal of biochemistry.

[12]  Michael A Henson,et al.  Optimization of Fed‐Batch Saccharomyces cerevisiae Fermentation Using Dynamic Flux Balance Models , 2006, Biotechnology progress.

[13]  Patrick F Suthers,et al.  Construction of an E. Coli genome‐scale atom mapping model for MFA calculations , 2011, Biotechnology and bioengineering.

[14]  Francisco J. Planes,et al.  Exploring metabolic pathways in genome-scale networks via generating flux modes , 2011, Bioinform..

[15]  Juho Rousu,et al.  An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments , 2008, BMC Bioinformatics.

[16]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010 .

[17]  G. Stephanopoulos,et al.  Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions. , 2007, Metabolic engineering.

[18]  Matthew D. Jankowski,et al.  Group contribution method for thermodynamic analysis of complex metabolic networks. , 2008, Biophysical journal.

[19]  Ahmad S. Khalil,et al.  A Synthetic Biology Framework for Programming Eukaryotic Transcription Functions , 2012, Cell.

[20]  T. Szyperski.  Biosynthetically Directed Fractional 13C‐labeling of Proteinogenic Amino Acids , 1995 .

[21]  I. Birol,et al.  Metabolic control analysis under uncertainty: framework development and case studies. , 2004, Biophysical journal.

[22]  Rick L Stevens,et al.  iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations , 2009, Genome Biology.

[23]  Bernhard O. Palsson,et al.  Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling , 2012, BMC Systems Biology.

[24]  Blake A. Simmons,et al.  Synthesis of three advanced biofuels from ionic liquid-pretreated switchgrass using engineered Escherichia coli , 2011, Proceedings of the National Academy of Sciences.

[25]  Neil Swainston,et al.  Towards a genome-scale kinetic model of cellular metabolism , 2010, BMC Systems Biology.

[26]  Jean-Charles Portais,et al.  The Ethylmalonyl-CoA Pathway Is Used in Place of the Glyoxylate Cycle by Methylobacterium extorquens AM1 during Growth on Acetate* , 2011, The Journal of Biological Chemistry.

[27]  W Wiechert,et al.  Bidirectional reaction steps in metabolic networks: IV. Optimal design of isotopomer labeling experiments. , 1999, Biotechnology and bioengineering.

[28]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[29]  Miguel Rocha,et al.  OptFlux: an open-source software platform for in silico metabolic engineering , 2010, BMC Systems Biology.

[30]  P. K. Ajikumar,et al.  The future of metabolic engineering and synthetic biology: towards a systematic practice. , 2012, Metabolic engineering.

[31]  Jonathan A. Kelner,et al.  Large-scale identification of genetic design strategies using local search , 2009, Molecular systems biology.

[32]  E. Klipp,et al.  Bringing metabolic networks to life: convenience rate law and thermodynamic constraints , 2006, Theoretical Biology and Medical Modelling.

[33]  J. Keasling,et al.  Application of targeted proteomics to metabolically engineered Escherichia coli , 2012, Proteomics.

[34]  J A Asenjo,et al.  Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters , 2012, Biotechnology and bioengineering.

[35]  Hyun Uk Kim,et al.  Flux variability scanning based on enforced objective flux for identifying gene amplification targets , 2012, BMC Systems Biology.

[36]  Juho Rousu,et al.  Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism , 2011, J. Comput. Biol..

[37]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[38]  S. Schuster,et al.  ON ELEMENTARY FLUX MODES IN BIOCHEMICAL REACTION SYSTEMS AT STEADY STATE , 1994 .

[39]  Jörg Stelling,et al.  Large-scale computation of elementary flux modes with bit pattern trees , 2008, Bioinform..

[40]  H. Kacser,et al.  The control of flux. , 1995, Biochemical Society transactions.

[41]  K. Prather,et al.  Engineering alternative butanol production platforms in heterologous bacteria. , 2009, Metabolic engineering.

[42]  F. Doyle,et al.  Dynamic flux balance analysis of diauxic growth in Escherichia coli. , 2002, Biophysical journal.

[43]  Christoph Wittmann,et al.  Flux Design: In silico design of cell factories based on correlation of pathway fluxes to desired properties , 2009, BMC Systems Biology.

[44]  Tom Ellis,et al.  Rational Diversification of a Promoter Providing Fine-Tuned Expression and Orthogonal Regulation for Synthetic Biology , 2012, PloS one.

[45]  Judith B. Zaugg,et al.  Bacterial adaptation through distributed sensing of metabolic fluxes , 2010, Molecular systems biology.

[46]  Rainer Breitling,et al.  Computational tools for the synthetic design of biochemical pathways , 2012, Nature Reviews Microbiology.

[47]  B. Palsson,et al.  Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use , 1994, Bio/Technology.

[48]  Pamela A Silver,et al.  Designing biological compartmentalization. , 2012, Trends in cell biology.

[49]  Matthias Reuss,et al.  13C-Labeled metabolic flux analysis of a fed-batch culture of elutriated Saccharomyces cerevisiae. , 2007, FEMS yeast research.

[50]  J. Liao,et al.  Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux. , 2011, Metabolic engineering.

[51]  Volker Sandig,et al.  Metabolite channeling and compartmentation in the human cell line AGE1.HN determined by 13C labeling experiments and 13C metabolic flux analysis. , 2011, Journal of bioscience and bioengineering.

[52]  Ron Milo,et al.  Hydrophobicity and Charge Shape Cellular Metabolite Concentrations , 2011, PLoS Comput. Biol..

[53]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[54]  Bas Teusink,et al.  Accelerating the reconstruction of genome-scale metabolic networks , 2006, BMC Bioinformatics.

[55]  Costas D Maranas,et al.  OptStrain: a computational framework for redesign of microbial production systems. , 2004, Genome research.

[56]  J. Liao,et al.  Ensemble Modeling for Aromatic Production in Escherichia coli , 2009, PloS one.

[57]  Nicola Zamboni,et al.  FiatFlux – a software for metabolic flux analysis from 13C-glucose experiments , 2005, BMC Bioinformatics.

[58]  Elizabeth Brunk,et al.  Integrating computational methods to retrofit enzymes to synthetic pathways , 2012, Biotechnology and bioengineering.

[59]  Sang Yup Lee,et al.  Escherichia coli W as a new platform strain for the enhanced production of L‐Valine by systems metabolic engineering , 2011, Biotechnology and bioengineering.

[60]  J. Liao,et al.  Metabolic ensemble modeling for strain engineers , 2012, Biotechnology journal.

[61]  V. Hatzimanikatis,et al.  Modeling of uncertainties in biochemical reactions , 2011, Biotechnology and bioengineering.

[62]  C. Wittmann,et al.  From zero to hero--design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production. , 2011, Metabolic engineering.

[63]  N. Price,et al.  Genome-Scale Consequences of Cofactor Balancing in Engineered Pentose Utilization Pathways in Saccharomyces cerevisiae , 2011, PloS one.

[64]  M. Savageau.  Biochemical systems analysis. III. Dynamic solutions using a power-law approximation , 1970 .

[65]  J. Nielsen,et al.  It Is All about MetabolicFluxes , 2003, Journal of bacteriology.

[66]  J. Heijnen,et al.  An in vivo data-driven framework for classification and quantification of enzyme kinetics and determination of apparent thermodynamic data. , 2011, Metabolic engineering.

[67]  Jong Myoung Park,et al.  Constraints-based genome-scale metabolic simulation for systems metabolic engineering. , 2009, Biotechnology advances.

[68]  Matthias Müller-Hannemann,et al.  In silico fragmentation for computer assisted identification of metabolite mass spectra , 2010, BMC Bioinformatics.

[69]  N. Wierckx,et al.  Engineering of Solvent-Tolerant Pseudomonas putida S12 for Bioproduction of Phenol from Glucose , 2005, Applied and Environmental Microbiology.

[70]  J. Selbig,et al.  Kinetic hybrid models composed of mechanistic and simplified enzymatic rate laws – a promising method for speeding up the kinetic modelling of complex metabolic networks , 2009, The FEBS journal.

[71]  Lydia E. Kavraki,et al.  Finding metabolic pathways using atom tracking , 2010, Bioinform..

[72]  W Wiechert,et al.  A universal framework for 13C metabolic flux analysis. , 2001, Metabolic engineering.

[73]  Ping Ji,et al.  Decomposing flux distributions into elementary flux modes in genome-scale metabolic networks , 2011, Bioinform..

[74]  Jens Nielsen,et al.  Synergies between synthetic biology and metabolic engineering , 2011, Nature Biotechnology.

[75]  R. Steuer,et al.  Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth. , 2012, Journal of experimental botany.

[76]  U. Sauer,et al.  High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived 13C constraints. , 2004, Analytical biochemistry.

[77]  P. Dürre,et al.  Pathway engineering and synthetic biology using acetogens , 2012, FEBS letters.

[78]  Kelly P. Nevin,et al.  Electrosynthesis of Organic Compounds from Carbon Dioxide Is Catalyzed by a Diversity of Acetogenic Microorganisms , 2011, Applied and Environmental Microbiology.

[79]  Merja Penttilä,et al.  Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A , 2008, BMC Systems Biology.

[80]  Jay D. Keasling,et al.  A model for improving microbial biofuel production using a synthetic feedback loop , 2010, Systems and Synthetic Biology.

[81]  M. A. Henson,et al.  Genome‐scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed‐batch culture , 2007, Biotechnology and bioengineering.

[82]  Juho Rousu,et al.  BMC Systems Biology BioMed Central Methodology article , 2009 .

[83]  V. Hatzimanikatis,et al.  Thermodynamics-based metabolic flux analysis. , 2007, Biophysical journal.

[84]  Chunhui Li,et al.  Exploring the diversity of complex metabolic networks , 2005, Bioinform..

[85]  Hiroshi Shimizu,et al.  An in silico platform for the design of heterologous pathways in nonnative metabolite production , 2012, BMC Bioinformatics.

[86]  J. Heijnen,et al.  Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics. , 2003, Metabolic engineering.

[87]  Scott B. Crown,et al.  Rational design of 13C-labeling experiments for metabolic flux analysis in mammalian cells , 2012, BMC Systems Biology.

[88]  Jeffrey C Way,et al.  Engineering cyanobacteria to generate high-value products. , 2011, Trends in biotechnology.

[89]  Radhakrishnan Mahadevan,et al.  Genome-scale metabolic modeling of a clostridial co-culture for consolidated bioprocessing. , 2010, Biotechnology journal.

[90]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[91]  K. Patil,et al.  Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. , 2009, Metabolic engineering.

[92]  U. Sauer,et al.  Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast , 2005, Genome Biology.

[93]  Pablo Carbonell,et al.  Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms , 2012, BMC Systems Biology.

[94]  T. Szyperski.  Biosynthetically directed fractional 13C-labeling of proteinogenic amino acids. An efficient analytical tool to investigate intermediary metabolism. , 1995, European journal of biochemistry.

[95]  Eugénio C. Ferreira,et al.  Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis-Menten and approximate kinetic equations , 2010, Biosyst..

[96]  V. Hatzimanikatis,et al.  Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3‐hydroxypropanoate , 2010, Biotechnology and bioengineering.

[97]  C. Maranas,et al.  Improved computational performance of MFA using elementary metabolite units and flux coupling. , 2010, Metabolic engineering.

[98]  Merja Penttilä,et al.  Dynamic flux balance analysis of the metabolism of Saccharomyces cerevisiae during the shift from fully respirative or respirofermentative metabolic states to anaerobiosis , 2012, The FEBS journal.