Guest Lecture by Areejit Samal on Elucidating and leveraging the design principles towards realistic Boolean models of gene regulatory networks

Title: Elucidating and leveraging the design principles towards realistic Boolean models of gene regulatory networks

Speaker: Areejit Samal (The Institute of Mathematical Sciences (IMSc), Chennai)

Venue: MRDG Conference Room

Abstract:

Boolean networks is a widely-used mathematical framework to model gene regulatory networks. Such Boolean models consist of a set of nodes, directed edges between the nodes, and Boolean functions representing the regulatory logic at each node in the network.

In the first part of my talk, I will discuss several biologically-meaningful types of BFs, and present our systematic study of their preponderance in a compilation of 2687 regulatory rules extracted from published Boolean models of diverse biological systems [1]. A surprising observation in the empirical dataset is that most Boolean functions have an odd ‘bias’. Thereafter, 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 two complexity measures: Boolean complexity and average sensitivity. In particular, we show that the RoFs minimize Boolean complexity and the NCFs minimize the average sensitivity in addition to minimizing the Boolean complexity, and this highlights the importance of minimum complexity in the regulatory logic of biological networks [1].

In the second part of my talk, I will present our systematic framework leveraging biologically-meaningful regulatory logic and relative stability of cell fates or attractors for model selection of Boolean developmental gene regulatory networks [2]. In particular, I show the usefulness of mean first passage time (MFPT) as a measure to capture cell state transitions within the Boolean framework and construction of cellular lineage trees. Thereafter, I will show an application of these tools towards in-depth analysis and subsequent improvement of existing Boolean models of Arabidopsis root development [2].

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, bioRxiv 2023.01.08.523151.

About the Speaker:

Areejit Samal is a faculty member in the computational biology group of The Institute of Mathematical Sciences, Chennai since April 2014. He obtained his Bachelors, Masters and PhD from University of Delhi. Subsequently, he did Postdoctoral research at the Max Planck Institute for Mathematics in the Sciences Leipzig, CNRS/LPTMS Orsay, Institute for Systems Biology Seattle, and ICTP Trieste. He has been a recipient of FEBS Short term Research Fellowship, Ramanujan Fellowship, Simons Associateship of ICTP and a Max Planck Partner Group. His research interests span the broad area of complex systems / networks, in particular, its applications to biological systems.