Quantitative dynamic modeling of transcriptional networks of embryonic stem cells using integrated framework of Pareto optimality and energy balance

Abstract

Embryonic Stem Cells (ESCs) are pluripotent and thus are considered the "cell type of choice". ESCs exhibit several phenotypic traits (e.g., proliferation, differentiation, apoptosis, necrosis, etc.) and when differentiated into a particular lineage they can perform an array of functions (e.g., protein secretion, detoxification, energy production). Typically, these cellular objectives compete against each other because of thermodynamic, stoichiometric and mass balance constraints. Analysis of transcriptional regulatory networks and metabolic networks in ESCs thus requires both a nonequilibrium thermodynamic and mass balance framework for designing and understanding complex ESC network approach as well as an optimality approach which can take cellular objectives into account simultaneously. The primary goal of this thesis was to develop an integrated energy and mass balance-based multi objective framework for a transcriptional regulatory network model for ESCs. The secondary goal was to utilize the developed framework for large-scale metabolic flux profiling of hepatic and ESC metabolic networks. Towards the first aim we first developed a complete dynamic pluripotent network model for ESCs which integrates several different master regulators of pluripotency such as transcription factors Oct4, Sox2, Nanog, Klf4, Nacl, Rexl, Daxl, cMyc, and Zfp281, and obtained the dynamic connectivity matrix between various pluripotency related gene promoters and transcription factors. The developed model fully describes the self-renewal state of embryonic stem cells.

(cont.) Next, we developed a transcriptional network model framework for ESCs that incorporates multiobjective optimality-based energy balance analysis. This framework predicts cofactor occupancy, network architecture and feedback memory of ESCs based on energetic cost. The integrated nonequilibrium thermodynamics and multiobjective-optimality network analysis-based approach was further utilized to explain the significance of transcriptional motifs defined as small regulatory interaction patterns that regulate biological functions in highly interacting cellular networks. Our results yield evidence that dissipative energetics is the underlying criteria used during evolution for motif selection and that biological systems during transcription tend towards evolutionary selection of subgraphs which produces minimum specific heat dissipation, thereby explaining the frequency of some motifs. Significantly, the proposed energetic hypothesis uncovers a mechanism for environmental selection of motifs, provides explanation for topological generalization of subgraphs into complex networks and enables identification of new functionalities for rarely occurring motifs. Towards the secondary goal, we have developed a multiobjecive optimization-based approach that couples the normalized constraint with both energy and flux balance-based metabolic flux analysis to explain certain features of metabolic control of hepatocytes, which is relevant to the response of hepatocytes and liver to various physiological stimuli and disease states. We also utilized this approach to obtain an optimal regimen for ESC differentiation into hepatocytes.

(cont.) The presented framework may establish multiobjective optimality-based thermodynamic analysis as a backbone in designing and understanding complex network systems, such as transcriptional, metabolic and protein interaction networks.