Regulation of T-cell signaling networks

Abstract

For a better understanding of how biology carries information within cells, it is not sufficient to look at individual protein or gene interactions, but to understand these networks of interactions as a whole. The goal of this thesis is to understand various aspects of how cells in general and T-cells in particular function, using models built from basic principles in chemical engineering, statistical physics and network theory, together with experiments performed by our collaborators. The ultimate objectives are to gain an insight into the mechanisms of certain key biological processes, understand the cause of certain diseases and to generate new ideas for methods of treating these diseases. First, we look at an example of a specific network built from previously published experiments and data collected by our collaborators, which governs the mechanism of activation of the T-cell receptor (TCR) by its kinase Lck and a negative regulator of Lck called Csk. We show that the mechanism by which the cell regulates TCR levels, together with the manner in which Lck activates the TCR produces interesting behavior, such as a "perfectly adaptive" system and a high-pass filter. Second, we look at heterogeneity in cancer cells at the level of protein signaling networks. Many common cancers are not treatable at the "source" or initial mutation, so one has to target downstream effector molecules. However, different cell lines bearing the same initial cancerous mutation exhibit varying signaling patterns due to differing secondary mutations which makes this difficult. The objective of this project is to try to characterize this heterogeneity and be able to identify molecules in the cell which would be the most effective drug targets. A general model for signaling in networks has been developed, analogous to models of neural networks, with mutations modeled as changes in the topology of this network. Keeping in mind that cancer cells are trying to maximize their growth, we are looking for patterns in secondary mutation during the directed evolution of these networks. A method for looking at free energy landscapes in topology space has also been developed. We find that lowest degree nodes along the shortest paths from the driver mutation to effector nodes tend to be the most conserved, and the frequency of multiple optima depends on the number of feedback loops. Finally, we look at the problem of constant activation thresholds for activation of various types of T-Cells. Despite having different TCRs, T-Cells of a certain type have a fixed activation threshold in terms of a peptide-MHC interaction strength (and a corresponding time, earlier than which they do not activate). We built a reaction-diffusion model for the network involved in the search process by which a pMHC-TCR finds a coreceptor-Lck, which enables us to understand how the threshold for activation is determined by the parameters of a particular cell type. We also developed an analytical solution for a simplified Markov Chain form of the model, which predicts how the activation rate scales with the parameters of interest in the system. We find that this rate is proportional to the fraction of coreceptors with Lck, increases (slowly) with diffusion and is independent of the number of coreceptors on the surface of the cell.