Busca avançada
Ano de início
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

MOOMIN - Mathematical explOration of `Omics data on a MetabolIc Network

Texto completo
Pusa, Taneli [1, 2, 3] ; Ferrarini, Mariana Galvao [2, 4] ; Andrade, Ricardo [1, 2] ; Mary, Arnaud [1, 2] ; Marchetti-Spaccamela, Alberto [3] ; Stougie, Leen [5] ; Sagot, Marie-France [1, 2]
Número total de Autores: 7
Afiliação do(s) autor(es):
[1] INRIA Grenoble Rhone Alpes, F-38334 Montbonnot St Martin - France
[2] Univ Lyon 1, Lab Biometrie & Biol Evolut, CNRS, UMR 5558, Univ Lyon, F-69622 Villeurbanne - France
[3] Sapienza Univ Rome, Dept Comp Automat & Management Engn, I-00185 Rome - Italy
[4] Univ Lyon, UMR0203, BF2i, INRA, INSA Lyon, F-69621 Villeurbanne - France
[5] CWI, NL-1098 XG Amsterdam - Netherlands
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: Bioinformatics; v. 36, n. 2, p. 514-523, JAN 15 2020.
Citações Web of Science: 0

Motivation: Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. Results: In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. (AU)

Processo FAPESP: 15/13430-9 - Enumerando histórias metabólicas multidimensionais: modelos e métodos
Beneficiário:Ricardo Luiz de Andrade Abrantes
Linha de fomento: Bolsas no Brasil - Pós-Doutorado
Processo FAPESP: 17/05986-2 - Histórias metabólicas a partir de dados de transcriptômica
Beneficiário:Ricardo Luiz de Andrade Abrantes
Linha de fomento: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado