Comparative multi-goal tradeoffs in systems engineering of microbial metabolism

BMC Systems Biology

Daniel Segrè²  Alexandra Dumitriu¹  David Byrne¹ 

[1] Bioinformatics Program, Boston University, Boston, MA, 02215, USA;Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA

关键词: Constraint-based modeling;    Metabolic engineering;    Microorganisms;    Metabolism;   

Others  :  1143597
DOI  :  10.1186/1752-0509-6-127

 received in 2012-04-29, accepted in 2012-08-29,  发布年份 2012

【 摘 要 】

Background

Metabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs.

Results

Here, we use constraint-based modeling to systematically design multiple combinations of medium compositions and gene-deletion strains for three microorganisms (Escherichia coli, Saccharomyces cerevisiae, and Shewanella oneidensis) and six industrially important byproducts (acetate, D-lactate, hydrogen, ethanol, formate, and succinate). We evaluated over 435 million simulated conditions and 36 engineering metabolic traits, including product rates, costs, yields and purity.

Conclusions

The resulting metabolic phenotypes can be classified into dominant clusters (meta-phenotypes) for each organism. These meta-phenotypes illustrate global phenotypic variation and sensitivities, trade-offs associated with multiple engineering goals, and fundamental differences in organism-specific capabilities. Given the increasing number of sequenced genomes and corresponding stoichiometric models, we envisage that the proposed strategy could be extended to address a growing range of biological questions and engineering applications.

【 授权许可】

   
2012 Byrne et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

【 参考文献 】

• [1]Keasling JD: Manufacturing molecules through metabolic engineering. Science 2010, 330:1355-1358.
• [2]Kim I-K, Roldão A, Siewers V, Nielsen J: A systems-level approach for metabolic engineering of yeast cell factories. FEMS Yeast Res 2011, 12:228-248.
• [3]Lee JW, Kim TY, Jang Y-S, Choi S, Lee SY: Systems metabolic engineering for chemicals and materials. Trends Biotechnol 2011, 29:370-378.
• [4]Millard CS, Chao YP, Liao JC, Donnelly MI: Enhanced production of succinic acid by overexpression of phosphoenolpyruvate carboxylase in escherichia coli. Appl Environ Microbiol 1996, 62:1808-1810.
• [5]Stols L, Donnelly MI: Production of succinic acid through overexpression of NAD(+)-dependent malic enzyme in an escherichia coli mutant. Appl Environ Microbiol 1997, 63:2695-2701.
• [6]Hong SH, Lee SY: Metabolic flux analysis for succinic acid production by recombinant escherichia coli with amplified malic enzyme activity. Biotechnol Bioeng 2001, 74:89-95.
• [7]Bunch PK, Mat-Jan F, Lee N, Clark DP: The ldhA gene encoding the fermentative lactate dehydrogenase of escherichia coli. Microbiology (Reading, Engl) 1997, 143(Pt 1):187-195.
• [8]Lee SJ, Lee D-Y, Kim TY, Kim BH, Lee J, Lee SY: Metabolic engineering of escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 2005, 71:7880-7887.
• [9]Price ND, Reed JL, Palsson BØ: Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2004, 2:886-897.
• [10]Jarboe LR, Zhang X, Wang X, Moore JC, Shanmugam KT, Ingram LO: Metabolic engineering for production of biorenewable fuels and chemicals: contributions of synthetic biology. J Biomed Biotechnol 2010, 2010:761042.
• [11]Tang YJ, Meadows AL, Keasling JD: A kinetic model describing shewanella oneidensis MR-1 growth, substrate consumption, and product secretion. Biotechnol Bioeng 2007, 96:125-133.
• [12]Segrè D, Zucker J, Katz J, Lin X, D’haeseleer P, Rindone WP, Kharchenko P, Nguyen DH, Wright MA, Church GM: From annotated genomes to metabolic flux models and kinetic parameter fitting. OMICS 2003, 7:301-316.
• [13]Edwards JS, Palsson BO: Metabolic flux balance analysis and the in silico analysis of escherichia coli K-12 gene deletions. BMC Bioinforma 2000, 1:1. BioMed Central Full Text
• [14]Edwards JS, Ibarra RU, Palsson BO: In silico predictions of escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 2001, 19:125-130.
• [15]Famili I, Forster J, Nielsen J, Palsson BO: Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. Proc Natl Acad Sci U S A 2003, 100:13134-13139.
• [16]Orth JD, Thiele I, Palsson BØ: What is flux balance analysis? Nat Biotechnol 2010, 28:245-248.
• [17]Suthers PF, Zomorrodi A, Maranas CD: Genome-scale gene/reaction essentiality and synthetic lethality analysis. Mol Syst Biol 2009, 5:301.
• [18]Kim J, Reed JL, Maravelias CT: Large-scale bi-level strain design approaches and mixed-integer programming solution techniques. PLoS One 2011, 6:e24162.
• [19]Burgard AP, Pharkya P, Maranas CD: Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 2003, 84:647-657.
• [20]Pharkya P, Maranas CD: An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab Eng 2006, 8:1-13.
• [21]Ranganathan S, Suthers PF, Maranas CD: OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput Biol 2010, 6:e1000744.
• [22]Price ND, Reed JL, Papin JA, Famili I, Palsson BO: Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices. Biophys J 2003, 84:794-804.
• [23]Price ND, Schellenberger J, Palsson BO: Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 2004, 87:2172-2186.
• [24]Oh Y-G, Lee D-Y, Lee SY, Park S: Multiobjective flux balancing using the NISE method for metabolic network analysis. Biotechnol Prog 2009, 25:999-1008.
• [25]Lee FC, Pandu Rangaiah G, Lee D-Y: Modeling and optimization of a multi-product biosynthesis factory for multiple objectives. Metab Eng 2010, 12:251-267.
• [26]Schuetz R, Zamboni N, Zampieri M, Heinemann M, Sauer U: Multidimensional Optimality of Microbial Metabolism. Science. 2012, 336:601-604.
• [27]Shanmugam KT, Ingram LO: Engineering biocatalysts for production of commodity chemicals. J Mol Microbiol Biotechnol 2008, 15:8-15.
• [28]Bro C, Regenberg B, Förster J, Nielsen J: In silico aided metabolic engineering of saccharomyces cerevisiae for improved bioethanol production. Metab Eng 2006, 8:102-111.
• [29]Otero JM, Panagiotou G, Olsson L: Fueling industrial biotechnology growth with bioethanol. Adv Biochem Eng Biotechnol 2007, 108:1-40.
• [30]Waks Z, Silver PA: Engineering a synthetic dual-organism system for hydrogen production. Appl Environ Microbiol 2009, 75:1867-1875.
• [31]Meshulam-Simon G, Behrens S, Choo AD, Spormann AM: Hydrogen metabolism in shewanella oneidensis MR-1. Appl Environ Microbiol 2007, 73:1153-1165.
• [32]Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO: In silico design and adaptive evolution of escherichia coli for production of lactic acid. Biotechnol Bioeng 2005, 91:643-648.
• [33]Cox SJ, Shalel Levanon S, Sanchez A, Lin H, Peercy B, Bennett GN, San K-Y: Development of a metabolic network design and optimization framework incorporating implementation constraints: a succinate production case study. Metab Eng 2006, 8:46-57.
• [34]Zeikus JG, Jain MK, Elankovan P: Biotechnology of succinic acid production and markets for derived industrial products. Appl Microbiol Biotechnol 1999, 51:545-552.
• [35]Branduardi P, Smeraldi C, Porro D: Metabolically engineered yeasts: “potential” industrial applications. J Mol Microbiol Biotechnol 2008, 15:31-40.
• [36]Varma A, Boesch BW, Palsson BO: Biochemical production capabilities of escherichia coli. Biotechnol Bioeng 1993, 42:59-73.
• [37]Fredrickson JK, Romine MF, Beliaev AS, Auchtung JM, Driscoll ME, Gardner TS, Nealson KH, Osterman AL, Pinchuk G, Reed JL, Rodionov DA, Rodrigues JLM, Saffarini DA, Serres MH, Spormann AM, Zhulin IB, Tiedje JM: Towards environmental systems biology of shewanella. Nat Rev Microbiol 2008, 6:592-603.
• [38]Heidelberg JF, Paulsen IT, Nelson KE, Gaidos EJ, Nelson WC, Read TD, Eisen JA, Seshadri R, Ward N, Methe B, Clayton RA, Meyer T, Tsapin A, Scott J, Beanan M, Brinkac L, Daugherty S, DeBoy RT, Dodson RJ, Durkin AS, Haft DH, Kolonay JF, Madupu R, Peterson JD, Umayam LA, White O, Wolf AM, Vamathevan J, Weidman J, Impraim M, Lee K, Berry K, Lee C, Mueller J, Khouri H, Gill J, Utterback TR, McDonald LA, Feldblyum TV, Smith HO, Venter JC, Nealson KH, Fraser CM: Genome sequence of the dissimilatory metal ion-reducing bacterium shewanella oneidensis. Nat Biotechnol 2002, 20:1118-1123.
• [39]Lovley DR: The microbe electric: conversion of organic matter to electricity. Curr Opin Biotechnol 2008, 19:564-571.
• [40]Lun DS, Rockwell G, Guido NJ, Baym M, Kelner JA, Berger B, Galagan JE, Church GM: Large-scale identification of genetic design strategies using local search. Mol Syst Biol 2009, 5:296.
• [41]Patil KR, Rocha I, Förster J, Nielsen J: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinforma 2005, 6:308. BioMed Central Full Text
• [42]Xu P, Ranganathan S, Fowler ZL, Maranas CD, Koffas MAG: Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metab Eng 2011, 13:578-587.
• [43]Brochado AR, Matos C, Møller BL, Hansen J, Mortensen UH, Patil KR: Improved vanillin production in baker’s yeast through in silico design. Microb Cell Fact 2010, 9:84. BioMed Central Full Text
• [44]Pinchuk GE, Hill EA, Geydebrekht OV, De Ingeniis J, Zhang X, Osterman A, Scott JH, Reed SB, Romine MF, Konopka AE, Beliaev AS, Fredrickson JK, Reed JL: Constraint-based model of shewanella oneidensis MR-1 metabolism: a tool for data analysis and hypothesis generation. PLoS Comput Biol 2010, 6:e1000822.
• [45]Sauer U: Metabolic networks in motion: 13C-based flux analysis. Mol Syst Biol 2006, 2:62.
• [46]Yuan J, Bennett BD, Rabinowitz JD: Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nat Protoc 2008, 3:1328-1340.
• [47]Aguilera A: Deletion of the phosphoglucose isomerase structural gene makes growth and sporulation glucose dependent in saccharomyces cerevisiae. Mol Gen Genet 1986, 204:310-316.
• [48]Gavrilescu M, Chisti Y: Biotechnology-a sustainable alternative for chemical industry. Biotechnol Adv 2005, 23:471-499.
• [49]Clarke BL: Stoichiometric network analysis. Cell Biophys 1988, 12:237-253.
• [50]Bertsimas D, Tsitsiklis JN: Introduction to Linear Optimization. Athena Scientific, New Hampshire; 1997.
• [51]Feist AM, Palsson BO: The biomass objective function. Curr Opin Microbiol 2010, 13:344-349.
• [52]Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ: A genome-scale metabolic reconstruction for escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 2007, 3:121.
• [53]Mo ML, Palsson BO, Herrgård MJ: Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol 2009, 3:37. BioMed Central Full Text
• [54]Barrett CL, Herring CD, Reed JL, Palsson BO: The global transcriptional regulatory network for metabolism in escherichia coli exhibits few dominant functional states. Proc Natl Acad Sci U S A 2005, 102:19103-19108.
• [55]GNU Linear Programming Kit (GLPK)[ http://www.gnu.org/software/glpk/ webcite]
• [56]Becker SA, Palsson BO: Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 2008, 4:e1000082.
• [57]Irizarry RA, Wu Z, Jaffee HA: Comparison of affymetrix geneChip expression measures. Bioinformatics 2006, 22:789-794.
• [58]Hartigan J, Wong M: Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C Appl Stat. Series C (Applied Statistics) 1979, 28:100-108.
• [59]Tibshirani R, Walther G, Hastie T: Estimating the number of clusters in a dataset via the Gap statistic. J R Stat Soc 2000, 63:411-423.
• [60]Solovieff N, Hartley SW, Baldwin CT, Perls TT, Steinberg MH, Sebastiani P: Clustering by genetic ancestry using genome-wide SNP data. BMC Genet 2010, 11:108.
• [61]Li M, Reilly MP, Rader DJ, Wang L-S: Correcting population stratification in genetic association studies using a phylogenetic approach. Bioinformatics 2010, 26:798-806.
• [62]Stegmayer G, Milone DH, Kamenetzky L, Lopez MG, Carrari F: A biologically inspired validity measure for comparison of clustering methods over metabolic data sets. IEEE/ACM Trans Comput Biol Bioinform 2012, 9:706-716.
• [63]Bertsekas DP: Nonlinear programming. 1st edition. Athena Scientific, New Hampshire; 1995.
• [64]Censor Y: Pareto optimality in multiobjective problems. Appl Math Optim 1977, 4:41-59.
• [65]Deb K: Multi-objective optimization using evolutionary algorithms. John Wiley and Sons, New Jersey; 2001.
• [66]Deb K, Pratap A, Agarwal S, Meyarivan T: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 2002, 6:182-197.
• [67]Patnaik PR: Intelligent models of the quantitative behavior of microbial systems. Food Bioprocess Technol. 2009, 2:122-137.
• [68]Tarafder A, Rangaiah GP, Ray AK: A study of finding many desirable solutions in multiobjective optimization of chemical processes. Comput Chem Eng 2007, 31:1257-1271.
• [69]Lee FC, Rangaiah GP, Ray AK: Multi-objective optimization of an industrial penicillin V bioreactor train using non-dominated sorting genetic algorithm. Biotechnol Bioeng 2007, 98:586-598.