scholarly journals The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tuure Hameri ◽  
Georgios Fengos ◽  
Vassily Hatzimanikatis
Keyword(s):  
Escherichia Coli ◽  
Kinetic Models ◽  
Large Scale ◽  
Structural Changes ◽  
Scaling Up ◽  
Steady States ◽  
Model Complexity ◽  
Reduced Models ◽  
Genome Scale ◽  
Stoichiometric Models

Abstract Background Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems. Results We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes. Conclusions We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.

scholarly journals The effects of model complexity and size on metabolic flux distribution and control. Case study inE. coli

2019 ◽  
Author(s):  
Tuure Hameri ◽  
Georgios Fengos ◽  
Vassily Hatzimanikatis
Keyword(s):  
Metabolic Engineering ◽  
Kinetic Models ◽  
Large Scale ◽  
Ad Hoc ◽  
Scaling Up ◽  
Steady States ◽  
Model Complexity ◽  
Reduced Models ◽  
Genome Scale ◽  
Stoichiometric Models

AbstractSignificant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built aroundad hocreduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. It has not been studied previously how network complexity affects the Metabolic Sensitivity Coefficients (MSCs) of large-scale kinetic models build around consistently reduced models. We reduced the iJO1366Escherichia Coligenome-scale metabolic reconstruction (GEM) systematically to build three stoichiometric models of variable size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are modular. We propose a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of non-linear kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their MSCs were computed. The analysis of the populations of MSCs for the reduced models evidences that metabolic engineering strategies - independent of network complexity - can be designed using our proposed workflow. These findings suggest that we can successfully construct reduced kinetic models from a GEM, without losing information relevant to the scope of the study. Our proposed workflow can serve as an approach for testing the suitability of a model for answering certain study-specific questions.Author SummaryKinetic models of metabolism are very useful tools for metabolic engineering. However, they are generatedad hocbecause, to our knowledge, there exists no standardized procedure for constructing kinetic models of metabolism. We sought to investigate systematically the effect of model complexity and size on sensitivity characteristics. Hence, we used the redGEM and the lumpGEM algorithms to build the backbone of three consistently and modularly reduced stoichiometric models from the iJO1366 genome-scale model for aerobically grownE.coli. These three models were of increasing complexity in terms of network topology and served as basis for building populations of kinetic models. We proposed for the first time a way for scaling up steady-states of the metabolic fluxes and the metabolite concentrations from one kinetic model to another and developed a workflow for fixing kinetic parameters between the models in order to preserve equivalency. We performed metabolic control analysis (MCA) around the populations of kinetic models and used their MCA control coefficients as measurable outputs to compare the three models. We demonstrated that we can systematically reduce genome-scale models to construct kinetic models of different complexity levels for a phenotype that, independent of network complexity, lead to mostly consistent MCA-based metabolic engineering conclusions.

scholarly journals A structural property for reduction of biochemical networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anika Küken ◽  
Philipp Wendering ◽  
Damoun Langary ◽  
Zoran Nikoloski
Keyword(s):  
Steady State ◽  
Model Reduction ◽  
Structural Property ◽  
Large Scale ◽  
Biochemical Networks ◽  
Mass Action ◽  
Mass Action Kinetics ◽  
Reduced Models ◽  
Metabolic Phenotypes ◽  
Genome Scale

AbstractLarge-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.

scholarly journals New insights into the adaptive transcriptional response to nitrogen starvation in Escherichia coli

2018 ◽  
Vol 46 (6) ◽  
pp. 1721-1728 ◽  
Author(s):  
Amy Switzer ◽  
Daniel R. Brown ◽  
Sivaramesh Wigneshweraraj
Keyword(s):  
Escherichia Coli ◽  
Adaptive Response ◽  
Large Scale ◽  
Nitrogen Starvation ◽  
Transcriptional Response ◽  
Adaptive Responses ◽  
Nitrogenous Compounds ◽  
E Coli ◽  
N Starvation ◽  
Genome Scale

Bacterial adaptive responses to biotic and abiotic stresses often involve large-scale reprogramming of the transcriptome. Since nitrogen is an essential component of the bacterial cell, the transcriptional basis of the adaptive response to nitrogen starvation has been well studied. The adaptive response to N starvation in Escherichia coli is primarily a ‘scavenging response’, which results in the transcription of genes required for the transport and catabolism of nitrogenous compounds. However, recent genome-scale studies have begun to uncover and expand some of the intricate regulatory complexities that underpin the adaptive transcriptional response to nitrogen starvation in E. coli. The purpose of this review is to highlight some of these new developments.

scholarly journals A structural property for reduction of biochemical networks

2021 ◽  
Author(s):  
Anika Kueken ◽  
Philipp Wendering ◽  
Damoun Langary ◽  
Zoran Nikoloski
Keyword(s):  
Steady State ◽  
Model Reduction ◽  
Structural Property ◽  
Large Scale ◽  
Biochemical Networks ◽  
Mass Action ◽  
Mass Action Kinetics ◽  
Reduced Models ◽  
Metabolic Phenotypes ◽  
Genome Scale

Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that growth predictions from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks.

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.

Structural changes in 16S RNA from Escherichia coli upon unfolding by urea

Biopolymers ◽  
1993 ◽  
Vol 33 (11) ◽  
pp. 1747-1755 ◽  
Author(s):  
A. A. Timchenko ◽  
J. Langowski ◽  
I. N. Serdyuk
Keyword(s):  
Escherichia Coli ◽  
Structural Changes ◽  
16S Rna

scholarly journals Arabidopsis Genes Essential for Seedling Viability: Isolation of Insertional Mutants and Molecular Cloning

Genetics ◽  
2001 ◽  
Vol 159 (4) ◽  
pp. 1765-1778
Author(s):  
Gregory J Budziszewski ◽  
Sharon Potter Lewis ◽  
Lyn Wegrich Glover ◽  
Jennifer Reineke ◽  
Gary Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Translocation ◽  
Gene Families ◽  
Mutant Phenotype ◽  
Lethal Mutant ◽  
A Genome ◽  
Genes Encoding ◽  
High Level ◽  
Mutant Lines ◽  
Genome Scale

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified >500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for >200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.

scholarly journals Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jingru Zhou ◽  
Yingping Zhuang ◽  
Jianye Xia
Keyword(s):  
Aspergillus Niger ◽  
Large Scale ◽  
Measurement Techniques ◽  
Metabolic Model ◽  
System Level ◽  
Metabolic Phenotype ◽  
Omics Data ◽  
Prediction Ability ◽  
Phenotype Prediction ◽  
Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

scholarly journals Iron Acquisition of Urinary Tract Infection Escherichia coli Involves Pathogenicity in Caenorhabditis elegans

2021 ◽  
Vol 9 (2) ◽  
pp. 310
Author(s):  
Masayuki Hashimoto ◽  
Yi-Fen Ma ◽  
Sin-Tian Wang ◽  
Chang-Shi Chen ◽  
Ching-Hao Teng
Keyword(s):  
Escherichia Coli ◽  
Urinary Tract ◽  
Caenorhabditis Elegans ◽  
Large Scale ◽  
Iron Acquisition ◽  
Infection Model ◽  
High Throughput Analysis ◽  
E Coli ◽  
C Elegans ◽  
Tract Infections

Uropathogenic Escherichia coli (UPEC) is a major bacterial pathogen that causes urinary tract infections (UTIs). The mouse is an available UTI model for studying the pathogenicity; however, Caenorhabditis elegans represents as an alternative surrogate host with the capacity for high-throughput analysis. Then, we established a simple assay for a UPEC infection model with C. elegans for large-scale screening. A total of 133 clinically isolated E. coli strains, which included UTI-associated and fecal isolates, were applied to demonstrate the simple pathogenicity assay. From the screening, several virulence factors (VFs) involved with iron acquisition (chuA, fyuA, and irp2) were significantly associated with high pathogenicity. We then evaluated whether the VFs in UPEC were involved in the pathogenicity. Mutants of E. coli UTI89 with defective iron acquisition systems were applied to a solid killing assay with C. elegans. As a result, the survival rate of C. elegans fed with the mutants significantly increased compared to when fed with the parent strain. The results demonstrated, the simple assay with C. elegans was useful as a UPEC infectious model. To our knowledge, this is the first report of the involvement of iron acquisition in the pathogenicity of UPEC in a C. elegans model.

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