Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes

Mišković, Ljubiša; Tokic, Milenko; Fengos, Georgios; Hatzimanikatis, Vassily

doi:10.1016/j.copbio.2015.08.019

Cited by 45 publications

(64 citation statements)

References 51 publications

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“…By exploiting this property of lumpGEM, we built a reduced model that has an ad hoc defined core with a biomass yield very close to its parent GEM model. With a systematic approach to define the core [48], we can generate representative reduced models that are consistent with their GEM for different studies, such as kinetic modelling [49–51], in where it is crucial to base the analysis on models that do not sacrifice stoichiometric, thermodynamic and physiological constraints.…”

Section: Resultsmentioning

confidence: 99%

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

Ataman

Hatzimanikatis

2017

PLoS Comput Biol

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In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

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Section: Resultsmentioning

confidence: 99%

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

Ataman

Hatzimanikatis

2017

PLoS Comput Biol

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“…We used ORACLE (Chakrabarti et al, 2013;Miskovic and Hatzimanikatis, 2010;Miskovic et al, 2015;Soh et al, 2012;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b) to perform a metabolic control analysis (MCA) around the representative steady-state flux vector at the LG phase (see Section 2.4.1). We generated a population of more than 370000 models, we rejected the ones that did not pass the stability test (see Section 2.4.5) and we obtained a final population of 238000 stable models, i.e., approximately 64% of the generated models were stable.…”

Section: Improving Bdo Production: Insights From Mcamentioning

confidence: 99%

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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“…These methods allow quantification of the impact of individual enzymes on the overall performance of the metabolic network through response analysis of metabolic fluxes with respect to various enzymatic modifications [24]. A mathematical framework named metabolic control analysis (MCA) has been introduced for this purpose [25].…”

Section: Introductionmentioning

confidence: 99%

“…One of the steps in the ORACLE workflow involves pruning , where the populations of the generated models are further classified into subpopulations with distinct characteristics based on existing or follow-up experiments. The basic principles of ORACLE have been introduced in [32, 33, 37], and the method was developed and extended in [24, 29–31, 34, 36]. …”

Section: Introductionmentioning

confidence: 99%

A design–build–test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

Mišković

Alff-Tuomala

Soh

et al. 2017

Biotechnol Biofuels

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BackgroundRecent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies.ResultsWe used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose–xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain.ConclusionsWe present a design–build–test cycle composed of modeling efforts and experiments with a glucose–xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated “modeling and experiments” systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0838-5) contains supplementary material, which is available to authorized users.

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Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes

Cited by 45 publications

References 51 publications

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

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

A design–build–test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

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