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16
JUL
Uncovering and leveraging design principles in Boolean models of gene regulatory networks
Par Ajay Subbaroyan, The Institute of Mathematical Sciences, Tamil Nadu, India
Le 16 Juillet 2024 à 11h00 - Laboratoire Jean Perrin - Campus Jussieu - T 32-33 - 5e et. - P533

Résumé

Boolean networks (BNs) are a well-established framework for modeling the dynamics of gene regulatory networks (GRNs). In Boolean GRNs, genes can be either "on" or "off", with their dynamics dictated by regulatory logic rules or Boolean functions (BFs). The complete dynamics of BNs are encapsulated in its state transition graph (STG). In this presentation, I will aim to address three research lacunae. The first is whether regulatory logic rules in reconstructed BNs that model cellular decision making, are random. We show that certain biologically meaningful BFs, namely, the read-once functions (RoFs) and the nested canalyzing functions (NCFs) are highly preponderant in such reconstructed BNs. This observation is substantiated by proving that RoFs and NCFs possess minimal complexity based on two metrics, Boolean complexity and average sensitivity, respectively [1]. The second is the proposal of a model selection framework for developmental Boolean GRNs. Our framework leverages relative stability constraints that are predicated on the hierarchy of cell states on the developmental landscape. The application of this framework to the latest reconstructed Boolean GRN of the Arabidopsis thaliana root stem cell niche yields several improved models [2]. The third is how various types of regulatory logic rules influence the structure of the STG of a BN. Certain biologically meaningful BFs typically engender more "bushy" and convergent STGs, indicative of robust dynamics, as opposed to STGs obtained using random BFs. This investigation is made possible by our adaptation of measures from the theory of cellular automata to the domain of BNs [3].

References

[1] A. Subbaroyan, O.C. Martin* & A. Samal*, Minimum complexity drives regulatory logic in Boolean models of living systems, PNAS Nexus, 1(1): pgac017 (2022).

[2] A. Subbaroyan, P. Sil, O.C. Martin* & A. Samal*, Leveraging developmental landscapes for model selection in Boolean gene regulatory networks, Briefings in Bioinformatics, 24(3): bbad160 (2023).

[3] P. Sil#, A. Subbaroyan#, S. Kulkarni, O.C. Martin* & A. Samal*, Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph, Briefings in Bioinformatics, 25(3): bbae150 (2024).