Physical modeling of cellular processes: stochastic gene regulation in embryonic stem cells
Year: 2011
Type: dissertation
Abstract: In the last few decades, with the advent of single-cell measurement techniques in experimental biology, a growing interest in the role of noise in cellular processes is apparent. In particular, cellular decision processes, based on (intrinsically noisy) regulated gene expression, are of paramount importance, as they can be found across all life, allowing cells to react to the internal and external media. The tools of statistical physics have proved ideal for the development of a theoretical understanding of the underlying mechanics in noisy cellular processes. In this thesis, we make use of these tools to build a bottom-up model for single-gene auto-regulation, and present an application of this model to a concrete biological system, namely the regulation of the expression of the Nanog protein in mouse embryonic stem (ES) cells. Nanog has been identified as one of the core factors associated with the pluripotent state of ES cells: the concentration of Nanog protein present in a cell is known to be related to differentiation decisions. The structured population distributions of Nanog protein observed in ES cell populations call for mathematical modeling as an important tool to unravel the mechanisms underlying Nanog heterogeneity. In Chapter 1, we begin with a brief overview of the main biological concepts involved in the following Chapters. After discussing the origin and role of stochasticity in cellular processes, we then present some of the basic modeling procedures and assumptions that have been adopted in the context of these processes. In Chapter 2, we consider (transcriptional) single-gene auto-regulation specifically. This is the simplest motif in transcriptional gene regulation, and is thus of paramount importance. We adopt a Master Equation approach to describe stochastic effects in an intrinsic, fully dynamic fashion. We discuss the general formulation of the model, and obtain analytical descriptions of protein and mRNA equilibrium distributions using approximations that hold in regimes of strong biological relevance. In Chapter 3, we discuss the application of our model for gene auto regulation to the particular case of Nanog in mouse ES cells. To this end, we analyze experimental measurements of Nanog mRNA and protein distributions in populations of ES cells cultured in two different media. We finish in Chapter 4 with some final remarks regarding the work laid out in this thesis.
Author: Tomás Aquino
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2285600413