Artikel ; Online: Evolutionary Stability of Small Molecular Regulatory Networks That Exhibit Near-Perfect Adaptation
Biology, Vol 12, Iss 841, p
2023 Band 841
Abstract: Large-scale protein regulatory networks, such as signal transduction systems, contain small-scale modules (‘motifs’) that carry out specific dynamical functions. Systematic characterization of the properties of small network motifs is therefore of great ... ...
Abstract | Large-scale protein regulatory networks, such as signal transduction systems, contain small-scale modules (‘motifs’) that carry out specific dynamical functions. Systematic characterization of the properties of small network motifs is therefore of great interest to molecular systems biologists. We simulate a generic model of three-node motifs in search of near-perfect adaptation, the property that a system responds transiently to a change in an environmental signal and then returns near-perfectly to its pre-signal state (even in the continued presence of the signal). Using an evolutionary algorithm, we search the parameter space of these generic motifs for network topologies that score well on a pre-defined measure of near-perfect adaptation. We find many high-scoring parameter sets across a variety of three-node topologies. Of all possibilities, the highest scoring topologies contain incoherent feed-forward loops (IFFLs), and these topologies are evolutionarily stable in the sense that, under ‘macro-mutations’ that alter the topology of a network, the IFFL motif is consistently maintained. Topologies that rely on negative feedback loops with buffering (NFLBs) are also high-scoring; however, they are not evolutionarily stable in the sense that, under macro-mutations, they tend to evolve an IFFL motif and may—or may not—lose the NFLB motif. |
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Schlagwörter | perfect adaptation ; molecular regulatory networks ; evolutionary algorithm ; evolutionary stability ; incoherent feedforward loops ; Biology (General) ; QH301-705.5 |
Thema/Rubrik (Code) | 006 |
Sprache | Englisch |
Erscheinungsdatum | 2023-06-01T00:00:00Z |
Verlag | MDPI AG |
Dokumenttyp | Artikel ; Online |
Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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