scholarly journals NIHBA: A Network Interdiction Approach with Hybrid Benders Algorithm for Strain Design

2019 ◽  
Author(s):  
Shouyong Jiang ◽  
Yong Wang ◽  
Marcus Kaiser ◽  
Natalio Krasnogor
Keyword(s):  
Metabolic Networks ◽  
High Efficiency ◽  
Network Interdiction ◽  
Great Success ◽  
Design Strategies ◽  
Flux Balance ◽  
Model Free ◽  
Strain Design ◽  
Balance Analysis ◽  
Bilevel Optimisation

AbstractFlux balance analysis (FBA) based bilevel optimisation has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimisation problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues, or low quantity of design solutions in a single run. In this work, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimisation, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users’ production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 hour).

scholarly journals NIHBA: a network interdiction approach for metabolic engineering design

2020 ◽  
Vol 36 (11) ◽  
pp. 3482-3492 ◽  
Author(s):  
Shouyong Jiang ◽  
Yong Wang ◽  
Marcus Kaiser ◽  
Natalio Krasnogor
Keyword(s):  
Metabolic Networks ◽  
High Efficiency ◽  
Optimization Problems ◽  
Bilevel Optimization ◽  
Network Interdiction ◽  
Supplementary Information ◽  
Great Success ◽  
Design Strategies ◽  
Model Free ◽  
Strain Design

Abstract Motivation Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. Results Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users’ production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ∼1 h, e.g. studied in this article). Availability and implementation Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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 Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarah R. Smith ◽  
Chris L. Dupont ◽  
James K. McCarthy ◽  
Jared T. Broddrick ◽  
Miroslav Oborník ◽  
...  
Keyword(s):  
Nitrogen Metabolism ◽  
Phaeodactylum Tricornutum ◽  
Metabolic Networks ◽  
Nutrient Utilization ◽  
Marine Diatom ◽  
Carbon And Nitrogen ◽  
Flux Balance ◽  
Balance Analysis ◽  
Carbon And Nitrogen Metabolism

Abstract Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa.

DYNAMIC FLUX BALANCE ANALYSIS FOR PREDICTING GENE OVEREXPRESSION EFFECTS IN BATCH CULTURES

2014 ◽  
Vol 22 (03) ◽  
pp. 327-338 ◽  
Author(s):  
CAROL MILENA BARRETO-RODRIGUEZ ◽  
JESSICA PAOLA RAMIREZ-ANGULO ◽  
JORGE MARIO GOMEZ RAMIREZ ◽  
LUKE ACHENIE ◽  
HAROLD MOLINA-BULLA ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Metabolic Networks ◽  
Ethanol Concentration ◽  
Batch Cultures ◽  
Dynamic Flux Balance Analysis ◽  
Optimization Framework ◽  
Flux Balance ◽  
Balance Analysis ◽  
Engineering Strategy ◽  
Technological Platforms

The advent of numerous technological platforms for genome sequencing has led to increasing understanding and construction of metabolic networks. A popular system engineering strategy is used to analyze microbial metabolic networks is flux balance analysis (FBA). In recent times, there has been a lot of interest in the study of the metabolic network dynamics when genes are overexpressed in the system. Herein, an optimization framework, which employs dynamic flux balance analysis (DFBA) is proposed for predicting ethanol concentration profiles in glycerol fermentations using Escherichia coli. In silico results were experimentally validated by overexpressing alcohol/acetaldehyde dehydrogenase adhE, pyruvate kinase pykF, pyruvate formate-lyase pflB and isoleucine-valine enzymes ilvC and llvL.

scholarly journals Phenotypic innovation through recombination in genome-scale metabolic networks

2016 ◽  
Vol 283 (1839) ◽  
pp. 20161536 ◽  
Author(s):  
Sayed-Rzgar Hosseini ◽  
Olivier C. Martin ◽  
Andreas Wagner
Keyword(s):  
Metabolic Networks ◽  
Chemical Elements ◽  
Carbon Sources ◽  
Computational Method ◽  
Metabolic Reaction ◽  
Flux Balance ◽  
Metabolic Systems ◽  
Balance Analysis ◽  
Genome Scale ◽  
Different Sources

Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype–phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

scholarly journals Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: GridProd

2017 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Growth Rate ◽  
Metabolic Network ◽  
Flux Balance Analysis ◽  
Efficient Method ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Flux Distribution ◽  
Production Rates ◽  
Flux Balance ◽  
Balance Analysis

AbstractConstraint-based metabolic flux analysis of knockout strategies is an efficient method to simulate the production of useful metabolites in microbes. Owing to the recent development of technologies for artificial DNA synthesis, it may become important in the near future to mathematically design minimum metabolic networks to simulate metabolite production. Accordingly, we have developed a computational method where parsimonious metabolic flux distribution is computed for designated constraints on growth and production rates which are represented by grids. When the growth rate of this obtained parsimonious metabolic network is maximized, higher production rates compared to those noted using existing methods are observed for many target metabolites. The set of reactions used in this parsimonious flux distribution consists of reactions included in the original genome scale model iAF1260. The computational experiments show that the grid size affects the obtained production rates. Under the conditions that the growth rate is maximized and the minimum cases of flux variability analysis are considered, the developed method produced more than 90% of metabolites, while the existing methods produced less than 50%. Mathematical explanations using examples are provided to demonstrate potential reasons for the ability of the proposed algorithm to identify design strategies that the existing methods could not identify. The source code is freely available, and is implemented in MATLAB and COBRA toolbox.Author summaryMetabolic networks represent the relationships between biochemical reactions and compounds in living cells. By computationally modifying a given metabolic network of microbes, we can simulate the effect of knockouts and estimate the production of valuable metabolites. A common mathematical model of metabolic networks is the constraint-based flux model. In constraint-based flux balance analysis, a pseudo-steady state is assumed to predict the metabolic profile where the sum of all incoming fluxes is equal to the sum of all outgoing fluxes for each internal metabolite. Based on these constraints, the biomass objective function, written as a linear combination of fluxes, is maximized. In this study, we developed an efficient method for computing the design of minimum metabolic networks by using constraint-based flux balance analysis to simulate the production of useful metabolites.

scholarly journals k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

2014 ◽  
Vol 10 (2) ◽  
pp. e1003487 ◽  
Author(s):  
Anupam Chowdhury ◽  
Ali R. Zomorrodi ◽  
Costas D. Maranas
Keyword(s):  
Flux Balance Analysis ◽  
Flux Balance ◽  
Strain Design ◽  
Balance Analysis

Resource allocation in metabolic networks: kinetic optimization and approximations by FBA

2015 ◽  
Vol 43 (6) ◽  
pp. 1195-1200 ◽  
Author(s):  
Stefan Müller ◽  
Georg Regensburger ◽  
Ralf Steuer
Keyword(s):  
Resource Allocation ◽  
Flux Balance Analysis ◽  
Qualitative Agreement ◽  
Metabolic Networks ◽  
Optimization Problems ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Flux Balance ◽  
Balance Analysis ◽  
Theoretical Results

Based on recent theoretical results on optimal flux distributions in kinetic metabolic networks, we explore the congruences and differences between solutions of kinetic optimization problems and results obtained by constraint-based methods. We demonstrate that, for a certain resource allocation problem, kinetic optimization and standard flux balance analysis (FBA) give rise to qualitatively different results. Furthermore, we introduce a variant of FBA, called satFBA, whose predictions are in qualitative agreement with kinetic optimization.

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