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Prediction of Metabolic Fluxes by Incorporating Genomic Context And Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses Jong Myoung Parka,b, Tae Yong Kima,b, and Sang Yup Leea,b,c,1 aMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Program), KAIST, Daejeon 305-701, Republic of Korea; bCenter for Systems and Synthetic Biotechnology, Institute for the BioCentury, KAIST, Daejeon 305-701, Republic of Korea; and cDepartment of Bio and Brain Engineering, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon 305-701, Republic of Korea Edited* by Bernhard Ø. Palsson, University of California at San Diego, La Jolla, CA, and accepted by the Editorial Board July 6, 2010 (received for review March 26, 2010) Flux balance analysis (FBA) of a genome-scale metabolic model However, this approach still works with a flux solution space that allows calculation of intracellular fluxes by optimizing an objective is too large compared with the biologically feasible flux solution function, such as maximization of cell growth, under given con- space of a real organism (15–17). Thus, additional information straints, and has found numerous applications in the field of systems and procedures, such as providing more constraints, are needed to biology and biotechnology. Due to the underdetermined nature of narrow down the set of candidate solutions. the system, however, it has limitations such as inaccurate prediction One example of a suitable constraint that can be applied is the of fluxes and existence of multiple solutions for an optimal objective experimentally measured metabolic flux data obtained from 13C- value. Here, we report a strategy for accurate prediction of meta- based flux analysis and fermentation data (18–21). Other constraints bolic fluxes by FBA combined with systematic and condition- that can be applied include transcriptome data under various en- independent constraints that restrict the achievable flux ranges of vironmental changes (22–26), thermodynamic constraints (27–30), grouped reactions by genomic context and flux-converging pattern and molecular crowding of various biomolecules in limited cyto- analyses. Analyses of three types of genomic contexts, conserved plasmic space (31). However, to apply these condition-dependent genomic neighborhood, gene fusion events, and co-occurrence of and objective function-specific constraints to the models, biolog- SYSTEMS BIOLOGY genesacrossmultiple organisms,wereperformed tosuggest a group ically meaningful knowledge and information on environmental and of fluxes that are likely on or off simultaneously. The flux ranges of genetic conditions are required. these grouped reactions were constrained by flux-converging The community has thus been actively pursuing the development pattern analysis. FBA of the Escherichia coli genome-scale metabolic of such methods. Previously, gene neighborhood methods were used model was carried out under several different genotypic (pykF, zwf, for gap filling in a metabolic network, finding new metabolic mod- ppc, and sucA mutants) and environmental (altered carbon source) ules and linking them to the genome, reconstructing pathway net- conditions by applying these constraints, which resulted in flux val- works, and refining the accuracy of metabolic flux prediction (32). ues that were in good agreement with the experimentally measured Also, the coupling of fluxes was previously studied in genome-scale 13 C-based fluxes. Thus, this strategy will be useful for accurately networks using methods such as the flux coupling finder (33, 34). predicting the intracellular fluxes of large metabolic networks when These methods were used to primarily investigate the topology of their experimental determination is difficult. the metabolic network and to supplement operon prediction tools. Here we present a unique strategy of using information gen- Escherichia coli | flux balance analysis | grouping reaction erated from genomic context and flux-converging pattern anal- 13 constraints | C-based flux | genome-scale metabolic model yses to generate and introduce unique constraints for more accurate simulation of the metabolic network. Three types of he accumulation of omics data, including genome, transcriptome, genomic context analyses consisting of conserved neighborhood, Tproteome, metabolome, and fluxome, provides an opportunity gene fusion, and co-occurrence were performed (Fig. 1A). The to understand cellular physiology and characteristics at multiple conserved neighborhood analysis predicts genes in close prox- levels (1, 2). Among them, the fluxome profiling allows quantifi- imity on various genomes repeatedly. The gene fusion analysis cation of metabolic fluxes, which collectively represent the cellular captures events of forming a hybrid gene from previously sepa- metabolic characteristics. For successful metabolic engineering, it rate genes in other organisms. The co-occurrence analysis pre- is important to understand and predict the changes of metabolic dicts presence or absence of linked proteins across organisms fluxes after modifying interesting genes, pathways, and regulatory (35). Flux-converging pattern analysis restricts the range of at- circuits. On the basis of systems-level understanding of cellular tainable flux distributions by considering the number of carbons status through fluxome profiling, rational and systematic develop- in the metabolite that participates in the reactions and the pat- fl ment of an improved strain becomes possible (3–6). terns of the uxes from a carbon source in the metabolic network fl One of the popular methods that has been used to examine by using the concept of ux-converging metabolite (Fig. 1B). cellular metabolic fluxes and predict their changes in perturbed These were all considered together in generating grouping re- conditions is flux balance analysis (FBA) (7–12). To calculate the optimal flux distribution in the multidimensional flux solution space of metabolic network, FBA optimizes an objective function, Author contributions: J.M.P., T.Y.K., and S.Y.L. designed research; J.M.P. and T.Y.K. per- formed research; J.M.P. contributed new reagents/analytic tools; J.M.P., T.Y.K., and S.Y.L. such as maximizing cell growth rate, under pseudosteady state analyzed data; and J.M.P., T.Y.K., and S.Y.L. wrote the paper. with mass balance and other constraints (13). Given the fact that The authors declare no conflict of interest. the information used to reconstruct the metabolic networks is *This article is a PNAS Direct Submission. B.Ø.P. is a guest editor invited by the Editorial incomplete, multiple equivalent solutions can be computed for Board. the states of these networks (14). One method that has been 1To whom correspondence should be addressed. E-mail: [email protected]. presented to overcome the problem of multiple solutions is to This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. explore the flux solution space by flux variability analysis (FVA). 1073/pnas.1003740107/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1003740107 PNAS Early Edition | 1of6 Downloaded by guest on September 26, 2021 Fig. 1. Schematic illustration of flux balance analysis with constraints of grouping of functionally and physically related reactions based on genomic context and flux-converging pattern analyses. (A) By considering protein–protein associations based on genomic context analyses consisting of conserved neighbor- hood, gene fusion and co-occurrence, significantly related reactions were organized in a group. The binary variable, y,is0or1.vj is the flux of reaction j and v1 and v2 belong to the same group. vj,min and vj,max are lower and upper bounds for the flux of reaction j.(B) The flux ranges of the grouped reactions based on genomic context analyses were further constrained by flux-converging pattern analyses based on the carbon number of metabolites that participate in each reaction (Ca) and the number of passing through flux-converging metabolites from a carbon source (Jb), for instance glucose in this study. A metabolite could be split into different metabolites or created by combining two metabolites. Each circle with different colors defines reaction groups that have a similar scale of flux values, determined by stoichiometric carbon balances for metabolites and fluxes through metabolic pathway. The flux-converging metabolite is defined as a metabolite that sums fluxes originated from a reaction that produces two or more metabolites into parallel pathways. δ denotes a constant defining the flux n n level of reactions in a reaction group. v1 , v2 are normalized flux of reaction 1, 2 by dividing each reaction by a carbon source uptake rate, such as glucose. (C) To restrict the flux solution space of the Escherichia coli genome-scale model, the simultaneous on/off constraints (Con/off) by genomic context analyses and flux scale constraints (Cscale)byflux-converging pattern analyses were applied to the E. coli central metabolism. (D) Grouping of reactions using genomic context analysis and clustering of the grouped reactions using flux-converging pattern analysis in the form of CxJy. Cx indicates the carbon number of metabolites that participate in each reaction and Jy indicates the type of fluxes through flux-converging metabolites from a carbon source. The red metabolites are flux- converging metabolites. The flux-converging metabolites correspond to metabolites where two split reactions combine, indicated by bold, transparent, and red arrows, such as glyceraldehyde-3-phosphate,
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