DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis

BMC Bioinformatics

Jose A Gomez¹  Kai Höffner¹  Paul I Barton¹ 

[1] Process Systems Engineering Laboratory, Massachusetts Institute of Technology, Cambridge 02139, MA, USA

关键词: Lexicographic optimization;    Linear programming;    Nonsmooth dynamic systems;    Dynamic flux balance analysis;   

Others  :  1084290
DOI  :  10.1186/s12859-014-0409-8

 received in 2014-07-08, accepted in 2014-12-03,  发布年份 2014

【 摘 要 】

Background

Dynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. DFBA can be performed using different approaches such as static optimization (SOA), dynamic optimization (DOA), and direct approaches (DA). Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). Both are common sources of failure and inefficiencies for these simulators.

Results

DFBAlab, a MATLAB-based simulator that uses the LP feasibility problem to obtain an extended system and lexicographic optimization to yield unique exchange fluxes, is presented. DFBAlab is able to simulate complex dynamic cultures with multiple species rapidly and reliably, including differential-algebraic equation (DAE) systems. In addition, DFBAlab’s running time scales linearly with the number of species models. Three examples are presented where the performance of COBRA, DyMMM and DFBAlab are compared.

Conclusions

Lexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system. DFBAlab does not fail during numerical integration due to infeasible LPs. The extended system obtained through the LP feasibility problem in DFBAlab provides a penalty function that can be used in optimization algorithms.

【 授权许可】

   
2014 Gomez et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Orth JD, Thiele I, Palsson BØ: What is flux balance analysis? Nat Biotechnol 2010, 28(3):245-248.
• [2]Mahadevan R, Edwards JS, Doyle FJ III: Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 2002, 83:1331-1340.
• [3]Höffner K, Harwood SM, Barton PI: A reliable simulator for dynamic flux balance analysis. Biotechnol Bioeng 2013, 110(3):792-802.
• [4]Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011, 6:1290-1307.
• [5]Hanly TJ, Henson MA: Dynamic flux balance modeling of microbial co-cultures for efficient batch fermentation of glucose and xylose mixtures. Biotechnol Bioeng 2011, 108(2):376-385.
• [6]Mao L, Verwoerd WS: ORCA: a COBRA toolbox extension for model-driven discovery and analysis. Bioinformatics 2014, 30(4):584-585.
• [7]Zhuang K, Izallalen M, Mouser P, Richter H, Risso C, Mahadevan R, Lovley DR: Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 2011, 5:305-316.
• [8]Zhuang K, Ma E, Lovley DR, Mahadevan R: The design of long-term effective uranium bioremediation strategy using a community metabolic model. Biotechnol Bioeng 2012, 109:2475-2483.
• [9]Mahadevan R, Schilling C: The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 2003, 5:264-276.
• [10]CPLEX Optimizer, IBM ILOG. http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/.
• [11]Gurobi Optimization I: Gurobi optimizer reference manual2014. [http://www.gurobi.com]
• [12]ApS M: The MOSEK optimization toolbox for MATLAB manual. Version 7.0 (Revision 128). [http://docs.mosek.com/7.0/toolbox/]
• [13]Bertsimas D, Tsitsiklis JN: Introduction to Linear Optimization . Athena Scientific, Belmont, MA; 1997.
• [14]Reed JL, Vo TD, Schilling CH, Palsson BØ: An expanded genome-scale model of Escherichia coli K-12 ( i JR904 GSM/GPR). Genome Biol 2003, 4(9):R54. BioMed Central Full Text
• [15]Höffner K, Barton PI: Design of microbial consortia for industrial biotechnology. In Proceedings of the Eight International Conference on Foundations of Computer-Aided Process Design . Edited by Eden M, Siirola JD, Towler GP. Elsevier, Cle Elum, Washington, USA; 2014.
• [16]Chang RL, Ghamsari L, Manichaikul A, Hom EF, Balaji S, Fu W, Shen Y, Hao T, Palsson BØ, Salehi-Ashtiani K, Papin JA: Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol Syst Biol 2011, 7:518.
• [17]Duarte NC, Herrgård MJ, Palsson BØ: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Res 2004, 14(7):1298-1309.
• [18]Buhr HO, Miller SB: A dynamic model of the high-rate algal-bacterial wastewater treatment pond. Water Res 1983, 17:29-37.
• [19]Zhang XW, Chen F, Johns MR: Kinetic models for heterotrophic growth of Chlamydomonas reinhardtii in batch and fed-batch cultures. Process Biochem 1999, 35:385-389.
• [20]Balkos KD, Colman B: Mechanism of CO2 acquisition in an acid-tolerant Chlamydomonas. Plant Cell Environ 2007, 30:745-752.
• [21]Yang A: Modeling and evaluation of CO2 supply and utilization in algal ponds. Ind Eng Chem Res 2011, 50:11181-11192.
• [22]Gomes de Oliveira Dal’Molin C, Quek LE, Palfreyman RW, Nielsen LK: AlgaGEM - a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome. BMC Genomics 2011, 12(Suppl 4):S5. BioMed Central Full Text
• [23]Charles River Watershed Association: Total Maximum Daily Load for Nutrients in the Upper/Middle Charles River, Massachusetts. http://www.mass.gov/eea/docs/dep/water/resources/n-thru-y/ucharles.pdf. 190 Park Rd, Weston, MA 02453 2011.