Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
Author(s):  
Xingxing Jian ◽  
Ningchuan Li ◽  
Qian Chen ◽  
Qiang Hua
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Gene Amplification ◽  
Metabolic Networks ◽  
Computational Analysis ◽  
Succinate Production ◽  
Novel Gene ◽  
Metabolic Models ◽  
Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

scholarly journals Cellular determinants of metabolite concentration ranges

2018 ◽  
Author(s):  
Jeanne M. O. Eloundou-Mbebi ◽  
Anika Küken ◽  
Georg Basler ◽  
Zoran Nikoloski
Keyword(s):  
Escherichia Coli ◽  
Large Scale ◽  
Metabolic Networks ◽  
Reaction Rates ◽  
Mass Action ◽  
Cellular Mechanisms ◽  
Real World Data ◽  
A Genome ◽  
Metabolic Models ◽  
Genome Scale

AbstractCellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.Author SummaryWe present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters (which are difficult to specify at a genome scale), provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.

scholarly journals Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models

2019 ◽  
Author(s):  
Dikshant Pradhan ◽  
Jason A. Papin ◽  
Paul A. Jensen
Keyword(s):  
Metabolic Engineering ◽  
Mathematical Framework ◽  
Continuous Variables ◽  
Convex Constraints ◽  
Coupling Analysis ◽  
Flux Coupling ◽  
Scale Models ◽  
Metabolic Models ◽  
Genome Scale ◽  
Flux Coupling Analysis

AbstractFlux coupling identifies sets of reactions whose fluxes are “coupled" or correlated in genome-scale models. By identified sets of coupled reactions, modelers can 1.) reduce the dimensionality of genome-scale models, 2.) identify reactions that must be modulated together during metabolic engineering, and 3.) identify sets of important enzymes using high-throughput data. We present three computational tools to improve the efficiency, applicability, and biological interpretability of flux coupling analysis.The first algorithm (cachedFCF) uses information from intermediate solutions to decrease the runtime of standard flux coupling methods by 10-100 fold. Importantly, cachedFCF makes no assumptions regarding the structure of the underlying model, allowing efficient flux coupling analysis of models with non-convex constraints.We next developed a mathematical framework (FALCON) that incorporates enzyme activity as continuous variables in genome-scale models. Using data from gene expression and fitness assays, we verified that enzyme sets calculated directly from FALCON models are more functionally coherent than sets of enzymes collected from coupled reaction sets.Finally, we present a method (delete-and-couple) for expanding enzyme sets to allow redundancies and branches in the associated metabolic pathways. The expanded enzyme sets align with known biological pathways and retain functional coherence. The expanded enzyme sets allow pathway-level analyses of genome-scale metabolic models.Together, our algorithms extend flux coupling techniques to enzymatic networks and models with transcriptional regulation and other non-convex constraints. By expanding the efficiency and flexibility of flux coupling, we believe this popular technique will find new applications in metabolic engineering, microbial pathogenesis, and other fields that leverage network modeling.

scholarly journals Comparison of metabolic states using genome-scale metabolic models

2021 ◽  
Vol 17 (11) ◽  
pp. e1009522
Author(s):  
Chaitra Sarathy ◽  
Marian Breuer ◽  
Martina Kutmon ◽  
Michiel E. Adriaens ◽  
Chris T. Evelo ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Biological Significance ◽  
Tca Cycle ◽  
Cell Types ◽  
Knowledge Bases ◽  
Objective Functions ◽  
Functional Differences ◽  
Metabolic States ◽  
Metabolic Models ◽  
Genome Scale

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

scholarly journals Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Parizad Babaei ◽  
Tahereh Ghasemi-Kahrizsangi ◽  
Sayed-Amir Marashi
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Closely Related Species ◽  
Systematic Analysis ◽  
Reconstruction Process ◽  
Flux Balance ◽  
Wide Range ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of threePseudomonasmetabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related toP. aeruginosaPAO1,P. putidaKT2440, andP. fluorescensSBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable forin silicosimulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare thein silicoresults to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

scholarly journals Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Mee K. Lee ◽  
Mohd Saberi Mohamad ◽  
Yee Wen Choon ◽  
Kauthar Mohd Daud ◽  
Nurul Athirah Nasarudin ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
E Coli ◽  
A Cell ◽  
Wet Lab ◽  
Genome Level ◽  
Metabolites Production ◽  
Modelling Methods

AbstractThe metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

Application of Genome-Scale Metabolic Models in Metabolic Engineering

2013 ◽  
Vol 9 (4) ◽  
pp. 203-214 ◽  
Author(s):  
Manuel Alberto Garcia-Albornoz ◽  
Jens Nielsen
Keyword(s):  
Metabolic Engineering ◽  
Metabolic Models ◽  
Genome Scale
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