Minimum complexity drives regulatory logic in Boolean models of living systems

The properties of random Boolean networks have been investigated extensively as models of regulation in biological systems. However, the Boolean functions (BFs) specifying the associated logical update rules should not be expected to be random. In this contribution, we focus on biologically meaningf...

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Vydáno v:PNAS nexus Ročník 1; číslo 1; s. pgac017
Hlavní autoři: Subbaroyan, Ajay, Martin, Olivier C, Samal, Areejit
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 01.03.2022
Témata:
Algebra, Boolean
Bias
Biological complexity
Boolean
Boolean functions
Complexity
Logic
Mathematical logic
Physical Sciences and Engineering
Sensitivity
India
New York
Boolean complexity
gene regulatory networks
update rules
average sensitivity
Boolean networks
ISSN:2752-6542, 2752-6542
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Shrnutí:The properties of random Boolean networks have been investigated extensively as models of regulation in biological systems. However, the Boolean functions (BFs) specifying the associated logical update rules should not be expected to be random. In this contribution, we focus on biologically meaningful types of BFs, and perform a systematic study of their preponderance in a compilation of 2,687 functions extracted from published models. A surprising feature is that most of these BFs have odd “bias”, that is they produce “on” outputs for a total number of input combinations that is odd. Upon further analysis, we are able to explain this observation, along with the enrichment of read-once functions (RoFs) and its nested canalyzing functions (NCFs) subset, in terms of 2 complexity measures: Boolean complexity based on string lengths in formal logic, which is yet unexplored in biological contexts, and the so-called average sensitivity. RoFs minimize Boolean complexity and all such functions have odd bias. Furthermore, NCFs minimize not only the Boolean complexity but also the average sensitivity. These results reveal the importance of minimum complexity in the regulatory logic of biological networks.
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ISSN:2752-6542
2752-6542
DOI:10.1093/pnasnexus/pgac017