Systematic analysis of noise reduction properties of coupled and isolated feedforward loops

Chakravarty Suchana; Csikász-Nagy Attila: Systematic analysis of noise reduction properties of coupled and isolated feedforward loops.
PLOS COMPUTATIONAL BIOLOGY, 17 (12). ISSN 1553-734X (2021)

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Szerző azonosítók:

Név	ORCID	MTMT szerző azonosító
Chakravarty Suchana		10065228
Csikász-Nagy Attila	0000-0002-2919-5601	10012379

Absztrakt (kivonat):

Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures. Copyright: © 2021 Chakravarty, Csikász-Nagy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Folyóirat/kiadvány címe:

PLOS COMPUTATIONAL BIOLOGY

Évszám:

2021

Kötet:

Szám:

ISSN:

1553-734X

Intézmény:

Pázmány Péter Katolikus Egyetem

Kar:

Információs Technológiai és Bionikai Kar (2013.07.-)