Elucidating and leveraging design principles towards realistic Boolean models of gene regulatory networks (Record no. 60550)

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fixed length control field 03805nam a22002057a 4500
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fixed length control field 240719b |||||||| |||| 00| 0 eng d
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
080 ## - UNIVERSAL DECIMAL CLASSIFICATION NUMBER
Universal Decimal Classification number HBNI
Item number Th246
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Ajay, Subbaroyan
Relator term author
245 ## - TITLE STATEMENT
Title Elucidating and leveraging design principles towards realistic Boolean models of gene regulatory networks
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Chennai
Name of publisher The Institute of Mathematical Sciences
Year of publication 2024
300 ## - PHYSICAL DESCRIPTION
Number of Pages xiii, 258p.
502 ## - DISSERTATION NOTE
Degree Type Ph.D
Name of granting institution HBNI
Year degree granted 2024
520 ## - SUMMARY, ETC.
Summary, etc Cells make decisions based on underlying gene regulatory networks (GRNs). GRNs may be modeled as a Boolean network (BN) in which nodes and directed edges represent genes or proteins and their interactions respectively. In BNs, a gene assumes a binary state and its temporal dynamics is governed by the state of its regulators via a regulatory logic rule (or logical update rule or Boolean function (BF)). The dynamics of BNs under synchronous update (in which all nodes are updated simultaneously) lead to fixed point attractors (which correspond to cellular phenotypes) or cyclic attractors. Stuart Kauffman conceived BNs in 1969, and modeled GRNs as random Bns due to paucity of experimental data. Advances in experimental techniques including omics approaches have fostered the reconstruction of real Boolean GRNs for several cellular processes in a wide range of species. It is now imperative to understand whether the regulatory logic rules in such models, which have remained largely unexplored, are just random or possess distinct features. In this thesis, we systematically investigate the nature of real regulatory logic rules by first compiling a dataset of 2687 logic rules from 88 reconstructed discrete models, and then examining in that dataset, the preponderance of various known types of biologically meaningful BFs. Two types that are particularly preponderant in our dataset are read-once functions (RoFs) and nested canalyzing functions (NCFs). We explain this by showing that RoFs and NCFs have the minimum complexity at a given number of inputs (k) and given bias (P ) in terms the Boolean complexity and the average sensitivity respectively. Furthermore, we also explore the abundance and biological plausibility of more recently published types of logic rules, namely, link operator functions (LOFs) and composition structures respectively. The voluminous biological data generated over the past three decades has driven both manual reconstruction of Boolean GRNs and advancement of automated methods to reconstruct Boolean GRNs. Even if the network structure of a GRN is kept fixed, there is generally a very large number of combinations of BFs (across all nodes) that can recover the same set of biological fixed points (or cellular phenotypes), and it is usually unclear how a certain model or subset of models are chosen as the biologically relevant ones during reconstruction. In this thesis, we leverage the relative stability of cell states derived from its developmental landscape to develop a framework that performs model selection on an ensemble of models that are otherwise equally plausible in the cell states they recover and in the type of logic rules (based on the minimum complexity criteria) they employ. We demonstrate our model selection framework on the latest root development Boolean GRN of Arabidopis thaliana and provide several improved models over the original one. In sum, we elucidate design principles of regulatory logic in GRNs and leverage those principles to develop methods for realistic reconstruction of Boolean models of biological systems in this thesis
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Computational Biology
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element Computational Biology
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Thesis Advisor Areejit Samal
Relator term Thesis Advisor [ths]
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://dspace.imsc.res.in/xmlui/handle/123456789/882
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type THESIS & DISSERTATION
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Current library Full call number Accession Number Uniform Resource Identifier Koha item type
        IMSc Thesis IMSc Library HBNI Th246 78121 https://dspace.imsc.res.in/xmlui/handle/123456789/882 THESIS & DISSERTATION
The Institute of Mathematical Sciences, Chennai, India

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