Busca avançada
Ano de início
Entree
(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.)

Homeostasis in a feed forward loop gene regulatory motif

Texto completo
Autor(es):
Antoneli, Fernando [1] ; Golubitsky, Martin [2] ; Stewart, Ian [3]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Fed Sao Paulo, Escola Paulista Med, BR-05508090 Sao Paulo, SP - Brazil
[2] Ohio State Univ, Dept Math, 231 W 18th Ave, Columbus, OH 43210 - USA
[3] Univ Warwick, Math Inst, Coventry CV4 7AL, W Midlands - England
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: Journal of Theoretical Biology; v. 445, p. 103-109, MAY 14 2018.
Citações Web of Science: 4
Resumo

The internal state of a cell is affected by inputs from the extra-cellular environment such as external temperature. If some output, such as the concentration of a target protein, remains approximately constant as inputs vary, the system exhibits homeostasis. Special sub-networks called motifs are unusually common in gene regulatory networks (GRNs), suggesting that they may have a significant biological function. Potentially, one such function is homeostasis. In support of this hypothesis, we show that the feed-forward loop GRN produces homeostasis. Here the inputs are subsumed into a single parameter that affects only the first node in the motif, and the output is the concentration of a target protein. The analysis uses the notion of infinitesimal homeostasis, which occurs when the input-output map has a critical point (zero derivative). In model equations such points can be located using implicit differentiation. If the second derivative of the input-output map also vanishes, the critical point is a chair: the output rises roughly linearly, then flattens out (the homeostasis region or plateau), and then starts to rise again. Chair points are a common cause of homeostasis. In more complicated equations or networks, numerical exploration would have to augment analysis. Thus, in terms of finding chairs, this paper presents a proof of concept. We apply this method to a standard family of differential equations modeling the feed-forward loop GRN, and deduce that chair points occur. This function determines the production of a particular mRNA and the resulting chair points are found analytically. The same method can potentially be used to find homeostasis regions in other GRNs. In the discussion and conclusion section, we also discuss why homeostasis in the motif may persist even when the rest of the network is taken into account. (C) 2018 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 15/50315-3 - Geometry and dynamics between Ohio and São Paulo
Beneficiário:Paolo Piccione
Linha de fomento: Auxílio à Pesquisa - Regular