Keng Cher Soh

Manipulating redox and ATP balancing for improved production of succinate in E. coli

Redox and energy balance plays a key role in determining microbial fitness. Efforts to redirect bacterial metabolism often involve overexpression and deletion of genes surrounding key central metabolites, such as pyruvate and acetyl-coA. In the case of metabolic engineering of Escherichia coli for succinate production, efforts have mainly focused on the manipulation of key pyruvate metabolizing enzymes. E. coli AFP111 strain lacking ldhA, pflB and ptsG encoded activities accumulates acetate and ethanol as well as shows poor anaerobic growth on rich and minimal media. To address these issues, we first deleted genes (adhE, ackA-pta) involved in byproduct formation downstream of acetyl-CoA followed by the deletion of iclR and pdhR to activate the glyoxylate pathway. Based on data from these studies, we hypothesized that the succinate productivity was limited by the insufficient ATP generation. Genome-scale thermodynamics-based flux balance analysis indicated that overexpression of ATP-forming PEPCK from Actinobacillus succinogenes in an ldhA, pflB and ptsG triple mutant strain could result in an increase in biomass and succinate flux. Testing of this prediction confirmed that PEPCK overexpression resulted in a 60% increase in biomass and succinate formation in the ldhA, pflB, ptsG mutant strain.

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints.

Mathematical modeling is an essential tool for the comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information on enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works considered only mass action kinetics for reactions in metabolic networks. Herein, we applied the optimization and risk analysis of complex living entities (ORACLE) framework and constructed a large-scale mechanistic kinetic model of optimally grown Escherichia coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of metabolic fluxes and metabolite concentrations. Our results further suggest that enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of cellular metabolism.

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome‐scale metabolic networks

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment.

Network thermodynamics in the post-genomic era

Network models have been used to study the underlying processes and principles of biological systems for decades, providing many insights into the complexity of life. Biological systems require a constant flow of free energy to drive these processes that operate away from thermodynamic equilibrium. With the advent of high-throughput omics technologies, more and more thermodynamic knowledge about the biological components, processes and their interactions are surfacing that we can integrate using large-scale biological network models. This allows us to ask many fundamental questions about these networks, such as, how far away from equilibrium must the reactions in a network be displaced in order to allow growth, or what are the possible thermodynamic objectives of the cell.

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

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia 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 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.

Constraining the flux space using thermodynamics and integration of metabolomics data.

Flux balance analysis of stoichiometric metabolic models has become one of the most common methods for estimating intracellular fluxes. However most of these networks are underdetermined and can have multiple alternate optimal flux distributions. Thermodynamic constraints can reduce the solution space significantly and at the same time provide a platform for the integration of metabolomics data. Here we go through the procedure to incorporate thermodynamic constraints and perform thermodynamic analysis of metabolic networks.

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

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.

A computational framework for integration of lipidomics data into metabolic pathways

Lipids are important compounds for human physiology and as renewable resources for fuels and chemicals. In lipid research, there is a big gap between the currently available pathway-level representations of lipids and lipid structure databases in which the number of compounds is expanding rapidly with high-throughput mass spectrometry methods. In this work, we introduce a computational approach to bridge this gap by making associations between metabolic pathways and the lipid structures discovered increasingly thorough lipidomics studies. Our approach, called NICELips (Network Integrated Computational Explorer for Lipidomics), is based on the formulation of generalized enzymatic reaction rules for lipid metabolism, and it employs the generalized rules to postulate novel pathways of lipid metabolism. It further integrates all discovered lipids in biological networks of enzymatic reactions that consist their biosynthesis and biodegradation pathways. We illustrate the utility of our approach through a case study of bis(monoacylglycero)phosphate (BMP), a biologically important glycerophospholipid with immature synthesis and catabolic route(s). Using NICELips, we were able to propose various synthesis and degradation pathways for this compound and several other lipids with unknown metabolism like BMP, and in addition several alternative novel biosynthesis and biodegradation pathways for lipids with known metabolism. NICELips has potential applications in designing therapeutic interventions for lipid-associated disorders and in the metabolic engineering of model organisms for improving the biobased production of lipid-derived fuels and chemicals.

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite‐level studies to atom‐level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom‐mapped reactions to atom‐mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom‐level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom‐mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom‐mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom‐mapped reactions of the KEGG database and we provide an example of an atom‐level representation of the core metabolic network of E. coli.