Deciphering Heterogeneities in Cellular Decision Making

Abstract

This thesis presents a multifaceted exploration into the complexities of cellular decision making, integrating experimental, computational, and engineering approaches to unravel the intricacies of heterogeneous cellular responses to noisy and conflicting extracellular cues. At the core of this research lies the investigation of how cells resolve conflicts in signaling, particularly when exposed to competing stimuli, and how this understanding can be harnessed to engineer novel therapeutic strategies.In the first segment of our study, we explore the intricate dynamics of cellular signaling and conflict resolution using rat pheochromocytoma (PC12) cells as a model. Challenging the traditional understanding, our research uncovers that both low and high concentrations of epidermal growth factor (EGF) can induce sustained extracellular signal-regulated kinase (ERK) activation, propelling cells towards differentiation, a role previously attributed exclusively to nerve growth factor (NGF). This is a significant deviation from the established paradigm where EGF is known to drive proliferation through transient ERK activation, and NGF is associated with sustained ERK activation leading to differentiation. Our findings are bolstered by both population-level and single-cell measurements. To further elucidate this phenomenon, we employed an enhanced mechanistic model. This model reveals a critical aspect: attenuated EGF receptor degradation due to reduced receptor phosphorylation is a key factor driving the sustained ERK activity at both low and high EGF concentrations. Moving beyond the qualitative labels of 'transient' and 'sustained', the model introduces a quantitative metric for ERK activation – its time integral. This metric, governed by receptor-ligand binding and degradation rates, is predictive of cell fate in cultures co-stimulated with EGF and NGF. It facilitates an ultrasensitive switch between proliferation and differentiation, leading to distinct and predictable cell fate outcomes. Building on these findings, the thesis then explores the design of self-assembling multivalent protein constructs. These constructs demonstrate the ability to create synthetic signaling hubs that precisely control kinase activities with spatial accuracy, offering a novel approach to manipulate cellular decision making processes. This advancement opens up possibilities for designing novel signaling paradigms, moving beyond conventional approaches in cell signaling. The third aspect of the research applies machine learning to optimize T cell expansion in immunotherapy. By analyzing kinase activities and signaling pathways, we identify key determinants for enhancing the proliferation and stemness during T cell expansion in vitro, crucial for effective T cell therapy. This approach integrates systems biology, which focuses on the complex interactions within biological systems, with computational analytics, a field that applies algorithmic and statistical techniques to biological data. Together, this synergy enhances our ability to predict and optimize therapeutic outcomes. Collectively, this thesis underscores the importance of a systems biology approach in understanding cellular heterogeneity and decision making. The integration of cellular biology, molecular biology, computational modeling, synthetic biology, and machine learning illustrates the potential for innovative research in controlling and guiding cellular behavior, particularly in the realm of targeted cancer therapies. The findings and methodologies presented here not only advance our knowledge of cellular signaling dynamics but also provide foundational insights for the development of more effective and precise cellular therapies.