Analysis and design of metabolic reaction networks via mixed‐integer linear optimization

Hatzimanikatis, Vassily; Floudas, Christodoulos A.; Bailey, James E.

doi:10.1002/aic.690420509

Cited by 163 publications

(107 citation statements)

References 31 publications

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“…Future studies will benefit from using the models presented here and the multi-parametric, multi-objective optimization framework introduced by Hatzimanikatis et al to improve the cellular performance (Hatzimanikatis et al, 1996). …”

Section: Discussionmentioning

confidence: 99%

“…In analogous manner, it is possible to define the control coefficient for multiple enzyme changes for the byproduct formation, carbon uptake, and other metabolic functions. The above mentioned control coefficients for multiple enzyme changes are a special case of the general formulation of control coefficients of metabolic functions with respect to a vector of parameters as proposed in (Hatzimanikatis et al, 1996).…”

Section: Control Coefficients For Multiple Enzyme Changesmentioning

confidence: 99%

“…We use the general formalism presented in (Hatzimanikatis et al, 1996) to derive from Eq. (2) the product selectivity control coefficient, C q …”

Section: Control Coefficients For Product Selectivitymentioning

confidence: 99%

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Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Andreozzi

Chakrabarti

Soh

et al. 2016

Metabolic Engineering

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Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using largescale kinetic models, Metabolic Engineering, http://dx.doi.org/10. 1016/j.ymben.2016.01.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation.Independent earlier experiments on this strain confirmed that the computationally 3 obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

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Section: Discussionmentioning

confidence: 99%

Section: Control Coefficients For Multiple Enzyme Changesmentioning

confidence: 99%

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Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Andreozzi

Chakrabarti

Soh

et al. 2016

Metabolic Engineering

Self Cite

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“…Such mathematical descriptions enable the qualitative study of regulatory structure and lead to general analytical insights which can be usefully applied to the analysis of complex metabolic networks, but must be used in connection with other techniques to make truly quantitative predictions. Mixedinteger linear optimization has also been used to predict optimal regulatory structures for metabolic engineering (Hatzimanikatis et al, 1996). Another approach is the use of kinetic theory to solve systems of ordinary di!erential equations (Reich & Sel'kov, 1981;Shuler & Domach, 1983;Fell, 1996;Heinrich & Schuster, 1996;Stephanopoulos et al, 1998), as has been done to study E. coli growth on glucose and lactose (Wong et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Regulation of Gene Expression in Flux Balance Models of Metabolism

Covert

Schilling

Palsson

2001

Journal of Theoretical Biology

389

350

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Genome-scale metabolic networks can now be reconstructed based on annotated genomic data augmented with biochemical and physiological information about the organism. Mathematical analysis can be performed to assess the capabilities of these reconstructed networks. The constraints-based framework, with #ux balance analysis (FBA), has been used successfully to predict time course of growth and by-product secretion, e!ects of mutation and knock-outs, and gene expression pro"les. However, FBA leads to incorrect predictions in situations where regulatory e!ects are a dominant in#uence on the behavior of the organism. Thus, there is a need to include regulatory events within FBA to broaden its scope and predictive capabilities. Here we represent transcriptional regulatory events as time-dependent constraints on the capabilities of a reconstructed metabolic network to further constrain the space of possible network functions. Using a simpli"ed metabolic/regulatory network, growth is simulated under various conditions to illustrate systemic e!ects such as catabolite repression, the aerobic/anaerobic diauxic shift and amino acid biosynthesis pathway repression. The incorporation of transcriptional regulatory events in FBA enables us to interpret, analyse and predict the e!ects of transcriptional regulation on cellular metabolism at the systemic level.

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“…In the log-linear formulation, the control coefficient matrices are evaluated as (Reder 1988;Hatzimanikatis et al 1996;Wang et al 2004):…”

Section: Metabolic Control Analysismentioning

confidence: 99%

Erratum to: A coupled thermodynamic and metabolic control analysis methodology and its evaluation on glycerol biosynthesis in Saccharomyces cerevisiae

2014

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A coupled in silico thermodynamic and probabilistic metabolic control analysis methodology was verified by applying it to the glycerol biosynthetic pathway in Saccharomyces cerevisiae. The methodology allows predictions even when detailed knowledge of the enzyme kinetics is lacking. In a metabolic steady state, we found that glycerol-3-phosphate dehydrogenase operates far from thermodynamic equilibrium (D r G 0 1 -15.9 to -47.5 kJ mol -1 , where D r G 0 1 is the transformed Gibbs energy of the reaction). Glycerol-3-phosphatase operates in modes near the thermodynamic equilibrium, far from the thermodynamic equilibrium or in between (D r G 0 2 & 0 to -23.7 kJ mol -1 ). From the calculated distribution of the scaled flux control coefficients (median = 0.81), we inferred that the pathway flux is primarily controlled by glycerol-3-phosphate dehydrogenase. This prediction is consistent with previous findings, verifying the efficacy of the proposed methodology.

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Analysis and design of metabolic reaction networks via mixed‐integer linear optimization

Cited by 163 publications

References 31 publications

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Regulation of Gene Expression in Flux Balance Models of Metabolism

Erratum to: A coupled thermodynamic and metabolic control analysis methodology and its evaluation on glycerol biosynthesis in Saccharomyces cerevisiae

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