Theoretical Studies of Modulated Protein-DNA Interactions and Their Impacts on Gene Regulation

Abstract

Gene expression describes the process by which instructions encoded in an organ ism’s DNA directs the synthesis of mRNA or protein. The relationship between an input signal and the amount of final gene products in a gene regulatory system is de scribed by a response curve. However, the shape of these curves depends on the detailed interactions among the protein and DNA in gene regulatory networks. A comprehensive understanding of the shape of the response curve and its relationships with the under lying molecular mechanisms by which a gene is transcribed is still challenging from a theoretical and experimental point of view. Therefore, considerable attention has been paid to understanding the mechanisms that determine the shape of the response curve. With the advantage of available genomic data, one can develop predictive models that explore the relationship between the genotype and phenotype of an organism. Thus, theoretical models emerge as a suitable option that provides insight into the various routes of protein-DNA interactions with the response curve. This thesis investigates the relationship between the mechanisms of protein-DNA interactions and the shape of the response curve for gene regulatory networks. First, we develop a statistical thermodynamic framework for response curves by con sidering the binding of a transcription factor with the promoter region of genomic DNA. The transcription factors follow various mechanisms during binding, such as cooperative interactions and DNA looping. In cooperative interaction, the transcription factors can spread from a nonspecific binding site to an adjacent specific binding site. DNA looping is another crucial alternative by which two bound transcription factors at large distances come close through protein-protein interaction. These two physical factors promote the self-assembly of transcription factors or the formation of higher-order oligomers on DNA. However, one can control their population by adding suitable input signals that perturb the protein-protein interactions. These input signals may be a selective binding of a small molecule to transcription factors or post-translational modifications such as phos phorylation or acetylation of an amino acid. Both modes alter the binding property of the transcription factors, controlling the population of a selective configuration of a protein-DNA complex. We develop a thermodynamic model in a grand canonical en semble that corroborates the relationship between an input signal and the population of a selective protein-DNA complex at thermodynamic equilibrium. Precisely, this rela tionship is the response curve in our study. However, the calculations become difficult for a complex gene regulatory system. Therefore, we use grand canonical Monte Carlo simulation to calculate the response curves for those cases. The equilibrium thermodynamic analysis of gene regulatory systems is a good start ing point, but these systems often experience out-of-equilibrium events that result in alternative steady states. These alternative steady states of gene regulatory systems are critical factors for the functioning of an assembly network. We perform their stochastic dynamic analysis to explore their existence in a gene regulatory network. Here, the evolution of the system is described by a Markov process as realized by a set of elementary reactions whose joint distribution is governed by a master equation. The gene regula tory systems often have correlated noise that alters their dynamics significantly. In this thesis, we explore the role of correlated noise in detail for a few gene regulatory systems. Wealso show that our developed thermodynamic model can discern the fate of a cell. To explore this, we consider the p53 signaling network, where the binding of tetrameric phosphorylated p53 to the promoter regions of a few cell fate-determining genes. We use a minimum free energy model and the Ising-based network model to establish a connec tion between the network topology and cell fate. In particular, the minimum free energy model infers the existence of first-order phase transitions of a damaged cell upon binding of tetrameric phosphorylated p53. Further, we apply our network model to various can cer cell lines ranging from breast cancer (MCF-7), colon cancer (HCT116), and leukemia (K562) that exhibit different network topologies and determine the differential fate of a malignant cell. Together, this thesis investigates modulated protein-DNA interactions and their role in gene regulation in complex regulatory systems.