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

Mapping Intimacies ◽

10.1101/666859 ◽

2019 ◽

Cited By ~ 2

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.

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

BMC Bioinformatics ◽

10.1186/s12859-021-04066-y ◽

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.

A structural property for reduction of biochemical networks

Scientific Reports ◽

10.1038/s41598-021-96835-1 ◽

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.

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

10.1101/437822 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tuure Hameri ◽

Georgios Fengos ◽

Meric Ataman ◽

Ljubisa Miskovic ◽

Vassily Hatzimanikatis

Keyword(s):

Steady State ◽

Metabolic Engineering ◽

Kinetic Models ◽

Large Scale ◽

Cellular Growth ◽

Reverse Direction ◽

Control Analysis ◽

Omics Data ◽

E Coli ◽

Steady State Solutions

AbstractLarge-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grownEscherichia coliinto a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of theE. coliaerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grownE. coli.

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

10.1101/569152 ◽

2019 ◽

Cited By ~ 1

Author(s):

Milenko Tokic ◽

Ljubisa Miskovic ◽

Vassily Hatzimanikatis

Keyword(s):

Metabolic Engineering ◽

Kinetic Models ◽

Rational Design ◽

Large Scale ◽

Single Gene ◽

Central Carbon Metabolism ◽

Scale Model ◽

Metabolic Responses ◽

Design And Optimization ◽

Genetic Perturbations

AbstractA high tolerance ofPseudomonas putidato toxic compounds and its ability to grow on a wide variety of substrates makes it a promising candidate for the industrial production of biofuels and biochemicals. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. In this work, we developed large-scale kinetic models ofP. putidato predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model ofP. putidaKT2440. We then applied the redGEM and lumpGEM algorithms to reduce the curated iJN1411 model systematically, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism ofP. putida. Using the medium complexity core model as a scaffold, we employed the ORACLE framework to generate populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain ofP. putidaKT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Overall, we demonstrated the potential and predictive capabilities of developed kinetic models that allow for rational design and optimization of recombinantP. putidastrains for improved production of biofuels and biochemicals.

A structural property for reduction of biochemical networks

10.1101/2021.03.17.435785 ◽

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.

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

Metabolic Engineering ◽

10.1016/j.ymben.2016.01.009 ◽

2016 ◽

Vol 35 ◽

pp. 148-159 ◽

Cited By ~ 47

Author(s):

Stefano Andreozzi ◽

Anirikh Chakrabarti ◽

Keng Cher Soh ◽

Anthony Burgard ◽

Tae Hoon Yang ◽

...

Keyword(s):

Metabolic Engineering ◽

Kinetic Models ◽

Large Scale ◽

E Coli

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

Genetics ◽

10.1093/genetics/159.4.1765 ◽

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.

Allometric Equations for Estimating Biomass and Carbon Stocks in Afforested Open Woodlands with Black Spruce and Jack Pine, in the Eastern Canadian Boreal Forest

Forests ◽

10.3390/f12010059 ◽

2021 ◽

Vol 12 (1) ◽

pp. 59

Author(s):

Olivier Fradette ◽

Charles Marty ◽

Pascal Tremblay ◽

Daniel Lord ◽

Jean-François Boucher

Keyword(s):

Large Scale ◽

Ad Hoc ◽

Black Spruce ◽

Tree Height ◽

Jack Pine ◽

Carbon Stocks ◽

Allometric Equations ◽

C Stocks ◽

The Difference ◽

Canadian Boreal Forest

Allometric equations use easily measurable biometric variables to determine the aboveground and belowground biomasses of trees. Equations produced for estimating the biomass within Canadian forests at a large scale have not yet been validated for eastern Canadian boreal open woodlands (OWs), where trees experience particular environmental conditions. In this study, we harvested 167 trees from seven boreal OWs in Quebec, Canada for biomass and allometric measurements. These data show that Canadian national equations accurately predict the whole aboveground biomass for both black spruce and jack pine trees, but underestimated branches biomass, possibly owing to a particular tree morphology in OWs relative to closed-canopy stands. We therefore developed ad hoc allometric equations based on three power models including diameter at breast height (DBH) alone or in combination with tree height (H) as allometric variables. Our results show that although the inclusion of H in the model yields better fits for most tree compartments in both species, the difference is minor and does not markedly affect biomass C stocks at the stand level. Using these newly developed equations, we found that carbon stocks in afforested OWs varied markedly among sites owing to differences in tree growth and species. Nine years after afforestation, jack pine plantations had accumulated about five times more carbon than black spruce plantations (0.14 vs. 0.80 t C·ha−1), highlighting the much larger potential of jack pine for OW afforestation projects in this environment.

Sensor System: A Survey of Sensor Type, Ad Hoc Network Topology and Energy Harvesting Techniques

Electronics ◽

10.3390/electronics10020219 ◽

2021 ◽

Vol 10 (2) ◽

pp. 219

Author(s):

Phuoc Duc Nguyen ◽

Lok-won Kim

Keyword(s):

Energy Harvesting ◽

Large Scale ◽

Ad Hoc ◽

Sensor Nodes ◽

Sensor System ◽

Wireless Sensor ◽

The Internet ◽

Original Research ◽

Energy Scavenging ◽

Term Operation

People nowadays are entering an era of rapid evolution due to the generation of massive amounts of data. Such information is produced with an enormous contribution from the use of billions of sensing devices equipped with in situ signal processing and communication capabilities which form wireless sensor networks (WSNs). As the number of small devices connected to the Internet is higher than 50 billion, the Internet of Things (IoT) devices focus on sensing accuracy, communication efficiency, and low power consumption because IoT device deployment is mainly for correct information acquisition, remote node accessing, and longer-term operation with lower battery changing requirements. Thus, recently, there have been rich activities for original research in these domains. Various sensors used by processing devices can be heterogeneous or homogeneous. Since the devices are primarily expected to operate independently in an autonomous manner, the abilities of connection, communication, and ambient energy scavenging play significant roles, especially in a large-scale deployment. This paper classifies wireless sensor nodes into two major categories based the types of the sensor array (heterogeneous/homogeneous). It also emphasizes on the utilization of ad hoc networking and energy harvesting mechanisms as a fundamental cornerstone to building a self-governing, sustainable, and perpetually-operated sensor system. We review systems representative of each category and depict trends in system development.

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

Microbial Cell Factories ◽

10.1186/s12934-021-01614-2 ◽

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.