How uncertainty in kinetic parameters affects metabolic control analysis of optimally grown E.coli

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the kinetic parameters affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale non-linear kinetic model built using integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are multiple kinetic parameters that can render the model feasible within the selected operational configuration. To account for the variability of the kinetic parameters within the designated operational configuration, we selected a reference vector of fluxes and concentrations close to their nominal values. Then, we used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. From these, we built non-linear models to test how the system’s response to perturbations changed with respect to the chosen kinetic parameters. This allowed us to quantify the effect of the uncertainty in the kinetic parameters on the robustness of the regulatory properties of the system.

Study on how underlying uncertainty in the flux values affects metabolic control analysis of optimally grown E. coli

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Ljubisa Miskovic

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the flux values affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale kinetic model and integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are infinite existing flux solutions within the selected operational configuration. To account for the flux variability within the designated operational configuration, we selected a reference vector of concentrations close to their nominal value. We used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. We next performed a systematic analysis of the effect that the flux profiles have on the robustness of the regulatory properties of the system. We used the mean PCA (principle component analysis) value of the flux samples as reference when selecting flux profiles. Flux profiles across the main components of the PCA were studied. We used ORACLE to generate populations of kinetic models for these flux profiles. Then, we computed the distributions of their flux control coefficients (FCCs) along the dimensions with the highest variance. Finally, by comparing the changes among the distributions of the FCCs versus those corresponding to the reference flux profile, we were able to quantify the robustness of the regulatory predictions within a specific operational configuration.

2015

Optimization of the production of methyl ethyl ketone in recombinant Pseudomonas putida using large-scale kinetic models

Meriç Ataman, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

The production of second generation of biofuels by conversion of inexpensive feedstocks is a basis for future bio-sustainable economy. Among many proposed molecules, methyl ethyl ketone (MEK) emerged as one of the most prominent fuel candidates due to its high-energy density, low emissions, and good transport properties. There is no known natural producer of MEK, but there were some recent studies to express its production pathway in different organisms. In all of these attempts, reported yields were very low. Among industrial workhorses such as E. coli and S. cerevisiae, P. putida has emerged as one of the most promising biofuel production hosts due to its tolerance to high toxicity compounds. For instance, it is reported that P. putida can grow in the presence of high concentrations of butanol1. This highly adaptive bacterium has been found to survive and grow on a broad range of substrates from pure caffeine to toxic industrial waste. In this study, we used the ORACLE framework to build, for the first time, a population of large-scale kinetic models of recombinant P. putida producing MEK. ORACLE2,3 (Optimization and Risk Analysis of Complex Living Entities) is a suite of computational tools capable of identifying enzymes with the highest impact on the production of target molecules. We started by embedding the MEK production pathway in the genome-scale model iJN7464 of P. putida, and then we derived a consistently reduced stoichiometric model. The obtained core model consists of 302 reactions and 210 mass balances distributed over cytosol, periplasm and extracellular environment. We then performed a series of tests to verify the consistency of the core model with its genome-scale counterpart. We next integrated fluxomics and metabolomics data from experiments and literature and performed Thermodynamic-Based Flux Analysis (TFA) to compute the thermodynamically feasible flux profiles that are consistent with the observed data. We further integrated the available information about the kinetic properties of enzymes involved in the modeled metabolic network. For reactions with missing or incomplete information about kinetic parameters, we performed a Monte Carlo sampling to generate missing information. We then constructed a population of 250’000 models, and we used these models to simultaneously optimize specific productivity of MEK and yield of MEK from glucose as a sole carbon source while taking into consideration the metabolic parameters such as catabolic and anabolic reduction charges. This way, we were able to identify enzymes that are potential candidates for metabolic engineering strategies towards obtaining a P. putida strain with improved specific productivity of MEK and its yield from glucose. This work demonstrates the potential and usefulness of ORACLE for optimizing production of biofuels and biochemicals in metabolic engineering and synthetic biology applications. 1. Rühl, J., Schmid, A. & Blank, L. M. Selected Pseudomonas putida strains able to grow in the presence of high butanol concentrations. Appl. Environ. Microbiol. 75, 4653–6 (2009). 2. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology 28, 391–7 (2010). 3. Chakrabarti, A., Miskovic, L., Soh, K. C. & Hatzimanikatis, V. Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotechnol. J. 8, 1043–57 (2013). 4. Nogales, J., Palsson, B. Ø. & Thiele, I. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst. Biol. 2, 79 (2008).

2017

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Noushin Hadadi, Vassily Hatzimanikatis, Vikash Kumar Pandey

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

2019

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Noushin Hadadi, Vassily Hatzimanikatis, Vikash Kumar Pandey

2019

Optimization of the production of methyl ethyl ketone in recombinant Pseudomonas putida using large-scale kinetic models

Meriç Ataman, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

2017

Study on how underlying uncertainty in the flux values affects metabolic control analysis of optimally grown E. coli

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Ljubisa Miskovic

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the flux values affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale kinetic model and integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are infinite existing flux solutions within the selected operational configuration. To account for the flux variability within the designated operational configuration, we selected a reference vector of concentrations close to their nominal value. We used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. We next performed a systematic analysis of the effect that the flux profiles have on the robustness of the regulatory properties of the system. We used the mean PCA (principle component analysis) value of the flux samples as reference when selecting flux profiles. Flux profiles across the main components of the PCA were studied. We used ORACLE to generate populations of kinetic models for these flux profiles. Then, we computed the distributions of their flux control coefficients (FCCs) along the dimensions with the highest variance. Finally, by comparing the changes among the distributions of the FCCs versus those corresponding to the reference flux profile, we were able to quantify the robustness of the regulatory predictions within a specific operational configuration.

2015