iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Andreozzi, Stefano; Mišković, Ljubiša; Hatzimanikatis, Vassily

doi:10.1016/j.ymben.2015.10.002

Cited by 79 publications

(81 citation statements)

References 48 publications

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“…We test the local stability of the steady state, and we reject samples for which their Jacobian matrix have positive eigenvalues (Andreozzi et al, 2015;Chakrabarti et al, 2013;Wang et al, 2004). This test is based on the assumption that the observable…”

Section: Stability and Consistency Verificationmentioning

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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Section: Stability and Consistency Verificationmentioning

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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“…In this work, we used the Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework [29–36] to construct a population of large-scale kinetic models of glucose–xylose co-utilizing S. cerevisiae that includes XR/XDH pathway, glycolysis, PPP, tricarboxylic cycle (TCA), and electron transport chain (ETC). ORACLE accounts explicitly for mechanistic properties of enzymes and integrates available experimental data, network thermodynamics, and physico-chemical constraints of metabolic networks.…”

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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“…As previously mentioned, ML algorithms can be used to inform aspects of hypothesis‐driven models to fill in missing information. In one such study, ML methods were used to identify feasible kinetic parameters in the hypothesis‐driven optimization and risk analysis of complex living entities (ORACLE) kinetic model of metabolism . When applied to 1,4‐butanediol (BDO) production in E. coli , the distribution of these feasible kinetic parameters was used to identify an enzyme expression level manipulation target.…”

Section: Review Of Instances Of Data‐driven Me Effortsmentioning

confidence: 99%

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Presnell

Alper

2019

Biotechnology Journal

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The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

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iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Cited by 79 publications

References 48 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

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

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

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