LAMOS: A linear algorithm to identify the origin of multiple optimal flux distributions in metabolic networks

•In this work, a four-phase new algorithm (LAMOS) is proposed to identify the origin of optimal solutions.•A current non-basic variable with zero reduced cost is iteratively substituted with a basic variable satisfying the feasibility condition to find the optimal vertices enclosing the optimal solu...

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Vydané v:Computers & chemical engineering Ročník 117; s. 372 - 377
Hlavní autori: Motamedian, Ehsan, Naeimpoor, Fereshteh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 02.09.2018
Predmet:
Flux balance analysis
Genome- scale metabolic network
Internal and futile cycles, Key reactions
Linear algorithm
Multiple optimal solution
Genome- scale metabolic network
Multiple optimal solution
Flux balance analysis
Linear algorithm
Internal and futile cycles, Key reactions
ISSN:0098-1354, 1873-4375
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Popis
Shrnutí:•In this work, a four-phase new algorithm (LAMOS) is proposed to identify the origin of optimal solutions.•A current non-basic variable with zero reduced cost is iteratively substituted with a basic variable satisfying the feasibility condition to find the optimal vertices enclosing the optimal solution region.•These basic and non-basic variables are key reaction pairs that their successive activity or inactivity causes alternate optimal solutions.•Key reactions were 1–3% of all reactions for the large scale models and identification of these reactions using only 1% of optimal solutions was possible. In flux balance analysis, where flux distribution within a cell metabolic network is estimated by optimizing an objective function, there commonly exist multiple optimal flux distributions. Although finding all optimal solutions is possible, their interpretation is a challenge. A new four-phase algorithm (LAMOS) is therefore proposed in this work to efficiently enumerate all of these solutions based on iterative substitution of a current non-basic variable with a basic variable. These basic and non-basic variables are called key reaction pairs that their successive activity or inactivity causes alternate optimal solutions. LAMOS was implemented on E. coli metabolic models and the results proved it as a simple and fast method capable of finding the key reactions as well as reactions participating in the futile cycles. Key reactions were 1–3% of all reactions for the large-scale models and these reactions were identified using only 1% of optimal solutions.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2018.06.014