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.

scholarly journals ADAPTIVE HARMONY SEARCH FOR OPTIMIZING CONSTRAINED RESOURCE ALLOCATION PROBLEM

2018 ◽  
pp. 260-269
Author(s):  
Amol C. Adamuthe ◽  
Tushar R. Nitave
Keyword(s):  
Resource Allocation ◽  
Optimization Problems ◽  
Harmony Search ◽  
Population Diversity ◽  
Allocation Problem ◽  
Resource Allocation Problem ◽  
Multi Objective ◽  
Wide Range ◽  
Allocation Problems ◽  
Population Based Algorithm

Resource Allocation problem is finding the optimal assignment of finite available resources to tasks or users. Resource allocation problems refer to a wide range of applications such as production, supply chain management, transportation, ICT technologies, etc. Resource allocation problems are NP-hard in nature where the objective is to find the optimal allocations satisfying given constraints. Harmony search (HS) algorithm is a meta-heuristic population based algorithm found good for solving different optimization problems. This paper presents adaptive harmony search (AHS) for solving one-dimensional bin packing problem (BPP) and multi-objective virtual machine placement problem (VMP). The proposed real coded solution representation supports partial constraint satisfaction. Adaptive pitch adjustment rate (PAR) based on population diversity improves the performance of harmony search algorithm. Results show that proposed HS gives optimal solution for 50 BPP instances with 100 % success rate. The performance reduced for large instances of BPP. The proposed weighted AHS for multi objective VMP problem gives better results than genetic algorithm.

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 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.

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