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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Bibliographic Details
Published in:	Computers & chemical engineering Vol. 117; pp. 372 - 377
Main Authors:	Motamedian, Ehsan, Naeimpoor, Fereshteh
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 02.09.2018
Subjects:	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
Online Access:	Get full text
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Summary:	•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