Designing optimal cell factories: integer programming couples elementary mode analysis with regulation

BMC Systems Biology

Jürgen Zanghellini¹  Christian Jungreuthmayer¹ 

[1] Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria

关键词: Gene regulation;    Minimal functionality;    Ethanol production;    Strain optimization;    Integer programming;    Minimal cut sets;    Elementary modes;    Metabolic engineering;   

Others  :  1143701
DOI  :  10.1186/1752-0509-6-103

 received in 2012-05-01, accepted in 2012-07-31,  发布年份 2012

【 摘 要 】

Background

Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information.

Results

Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible.

Conclusions

We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in E. coli.

【 授权许可】

   
2012 Jungreuthmayer and Zanghellini; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Park JM, Kim TY, Lee SY: Constraints-based genome-scale metabolic simulation for systems metabolic engineering. [http://www.sciencedirect.com/science/article/pii/S0734975009001141] webciteBiotechnol Adv 2009, 27(6):979-988.
• [2]Orth JD, Thiele I, Palsson BO: What is flux balance analysis? [http://dx.doi.org/10.1038/nbt.1614] webciteNat Biotech 2010, 28(3):245-248.
• [3]Burgard AP, Pharkya P, Maranas CD: OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. [http://onlinelibrary.wiley.com/doi/10.1002/bit.10803/abstract] webciteBiotechnology and Bioengineering 2003, 84(6):647-657.
• [4]Pharkya P, Maranas CD: An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. [http://www.sciencedirect.com/science/article/pii/S1096717605000704] webciteMetab Eng 2006, 8:1-13.
• [5]Patil KR, Rocha I, Förster J, Nielsen J: Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics 2005, 6:308. [PMID: 16375763] [http://www.ncbi.nlm.nih.gov/pubmed/16375763 webcite] BioMed Central Full Text
• [6]Pharkya P, Burgard AP, Maranas CD: OptStrain: A computational framework for redesign of microbial production systems. [http://genome.cshlp.org/content/14/11/2367.abstract] webciteGenome Res 2004, 14(11):2367-2376.
• [7]Ranganathan S, Suthers PF, Maranas CD: OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions. [http://dx.doi.org/10.1371/journal.pcbi.1000744] webcitePLoS Comput Biol 2010, 6(4):e1000744.
• [8]Trinh CT, Li J, Blanch HW, Clark DS: Redesigning Escherichia coli Metabolism for Anaerobic Production of Isobutanol. Appl and Environ Microbiol 2011, 77(14):4894-4904.
• [9]Boghigian BA, Shi H, Lee K, Pfeifer BA: Utilizing elementary mode analysis, pathway thermodynamics, and a genetic algorithm for metabolic flux determination and optimal metabolic network design. [http://www.biomedcentral.com/1752-0509/4/49] webciteBMC Syst Biol 2010, 4:49. BioMed Central Full Text
• [10]Unrean P, Trinh CT, Srienc F: Rational design and construction of an efficient E. coli for production of diapolycopendioic acid. [http://www.sciencedirect.com/science/article/pii/S1096717609001050] webciteMetab Eng 2010, 12(2):112-122.
• [11]Trinh CT, Srienc F: Metabolic Engineering of Escherichia coli for Efficient Conversion of Glycerol to Ethanol. [http://aem.asm.org/content/75/21/6696.abstract] webciteAppl and Environ Microbiol 2009, 75(21):6696-6705.
• [12]Trinh CT, Unrean P, Srienc F: Minimal Escherichia coli Cell for the Most Efficient Production of Ethanol from Hexoses and Pentoses. [http://aem.asm.org/content/74/12/3634.abstract] webciteAppl Environ Microbiol 2008, 74(12):3634-3643.
• [13]Carlson R, Fell D, Srienc F: Metabolic pathway analysis of a recombinant yeast for rational strain development. [http://onlinelibrary.wiley.com/doi/10.1002/bit.10305/abstract] webciteBiotechnol Bioeng 2002, 79(2):121-134.
• [14]Schuster S, Fell DA, Dandekar T: A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. [http://dx.doi.org/10.1038/73786] webciteNat Biotech 2000, 18(3):326-332.
• [15]Schuster S, Dandekar T, Fell DA: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. [http://www.sciencedirect.com/science/article/pii/S0167779998012906] webciteTrends in Biotechnol 1999, 17(2):53-60.
• [16]Hädicke O Klamt S: Computing complex metabolic intervention strategies using constrained minimal cut sets. [http://www.sciencedirect.com/science/article/pii/S1096717610001084] webciteMetab Eng 2011, 13(2):204-213.
• [17]Balas E, Jeroslow R: Canonical cuts on the unit hypercube. [http://www.jstor.org/stable/2099623] webciteSIAM J Appl Mathematics 1972, 23:61-69.
• [18]Tsai JF, M-H Lin YCH: Finding multiple solutions to general integer linear programs. [http://dx.doi.org/10.1016/j.ejor.2006.11.024] webciteEur J Operational Res 2008, 184(2):802-809.
• [19]Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BO: Integrating high-throughput and computational data elucidates bacterial networks. [http://dx.doi.org/10.1038/nature02456] webciteNature 2004, 429(6987):92-96.
• [20]Chen D, Batson RG, Dang Y: Applied Integer Programming: Modeling and Solution. Hoboken, New Jersey: John Wiley & Sons, Inc; 2010. ISBN:978-0-470-37306-4
• [21]Alexeeva S, De Kort B, Sawers G, Hellingwerf KJ, De Mattos MJT: Effects of Limited Aeration and of the ArcAB System on Intermediary Pyruvate Catabolism in Escherichia Coli. [http://jb.asm.org/content/182/17/4934] webciteJ Bacteriol 2000, 182(17):4934-4940.
• [22]Kornberg HL: The role and control of the glyoxylate cycle in Escherichia coli. Biochem J 1966, 99:1-11. [PMID: 5337756 PMCID: 1264949]
• [23]Kornberg HL: The role and maintenance of the tricarboxylic acid cycle in Escherichia coli. Biochem Soc Symp 1970, 30:155-171. [PMID: 4322317] [http://www.ncbi.nlm.nih.gov/pubmed/4322317 webcite]
• [24]Orth JD, Fleming RMT, Palsson BO: 10.2.1 - Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. In EcoSal - Escherichia coli and Salmonella Cellular and Molecular Biology. Edited by Karp P D. 10.2.1 Washington DC: ASM Press; 2010.
• [25]Klamt S, Stelling J: Combinatorial Complexity of Pathway Analysis in Metabolic Networks. [http://www.springerlink.com/content/x2m6215394074117/abstract/] webciteMol Biol Reports 2002, 29:233-236.
• [26]Haus U, Klamt S, Stephen T: Computing Knock-Out Strategies in Metabolic Networks. [http://www.liebertonline.com/doi/abs/10.1089/cmb.2007.0229] webciteJ Comput Biol 2008, 15(3):259-268.
• [27]Jevremović D, Trinh CT, Srienc F, Sosa CP, Boley D: Parallelization of Nullspace Algorithm for the computation of metabolic pathways. [http://www.sciencedirect.com/science/article/pii/S0167819111000378] webciteParallel Comput 2011, 37(6–7):261-278.
• [28]Terzer M, Stelling J: Large-scale computation of elementary flux modes with bit pattern trees. [http://bioinformatics.oxfordjournals.org/content/24/19/2229.abstract] webciteBioinformatics 2008, 24(19):2229-2235.