Bioengineering models of cell signaling

Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions

Unraveling the intricacies of spatial organization of the ErbB receptors and downstream signaling pathways

Faced with the complexity of diseases such as cancer which has 1012 mutations, altering gene expression, and disrupting regulatory networks, there has been a paradigm shift in the biological sciences and what has emerged is a much more quantitative field of biology. Mathematical modeling can aid in biological discovery with the development of predictive models that provide future direction for experimentalist. In this work, I have contributed to the development of novel computational approaches which explore mechanisms of receptor aggregation and predict the effects of downstream signaling. The coupled spatial non-spatial simulation algorithm, CSNSA is a tool that I took part in developing, which implements a spatial kinetic Monte Carlo for capturing receptor interactions on the cell membrane with Gillespies stochastic simulation algorithm, SSA, for temporal cytosolic interactions. Using this framework we determine that receptor clustering significantly enhances downstream signaling. In the next study the goal was to understand mechanisms of clustering. Cytoskeletal interactions with mobile proteins are known to hinder diffusion. Using a Monte Carlo approach we simulate these interactions, determining at what cytoskeletal distribution and receptor concentration optimal clustering occurs and when it is inhibited. We investigate oligomerization induced trapping to determine mechanisms of clustering, and our results show that the cytoskeletal interactions lead to receptor clustering. After exploring the mechanisms of clustering we determine how receptor aggregation effects downstream signaling. We further proceed by implementing the adaptively coarse grained Monte Carlo, ACGMC to determine if \u27receptor-sharing\u27 occurs when receptors are clustered. In our proposed \u27receptor-sharing\u27 mechanism a cytosolic species binds with a receptor then disassociates and rebinds a neighboring receptor. We tested our hypothesis using a novel computational approach, the ACGMC, an algorithm which enables the spatial temporal evolution of the system in three dimensions by using a coarse graining approach. In this framework we are modeling EGFR reaction-diffusion events on the plasma membrane while capturing the spatial-temporal dynamics of proteins in the cytosol. From this framework we observe \u27receptor-sharing\u27 which may be an important mechanism in the regulation and overall efficiency of signal transduction. In summary, I have helped to develop predictive computational tools that take systems biology in a new direction.\u2

Quantitative analysis of the receptor-induced apoptotic decision network

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2008.Includes bibliographical references (p. 157-169).Cells use a complex web of protein signaling pathways to interpret extracellular cues and decide and execute cell fates such as survival, apoptosis, differentiation, and proliferation. Cell decisions can be triggered by subtle, transient signals that are context specific, making them hard to study by conventional experimental methods. In this thesis, we use a systems approach combining quantitative experiments with computational modeling and analysis to understand the regulation of the survival-vs-death decision. A second goal of this thesis was to develop modeling and analysis methods that enable study of signals that are transient or at intermediate activation levels. We addressed the challenge of balancing mechanistic detail and ease of interpretation in modeling by adapting fuzzy logic to analyze a previously published experimental dataset characterizing the dynamic behavior of kinase pathways governing apoptosis in human colon carcinoma cells. Simulations of our fuzzy logic model recapitulated most features of the data and generated several predictions involving pathway crosstalk and regulation. Fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to generate not only quantitative predictions but also biological insights concerning operation of signaling networks. To study transient signals in differential-equation based models, we employed direct Lyapunov exponents (DLEs) to identify phase-space domains of high sensitivity to initial conditions. These domains delineate regions exhibiting qualitatively different transient activities that would be indistinguishable using steady-state analysis but which correspond to different outcomes.(cont.) We combine DLE analysis of a physicochemical model of receptor-mediated apoptosis with single cell data obtained by flow cytometry and FRET-based reporters in live-cell microscopy to classify conditions that alter the usage of two apoptosis pathways (Type I/II apoptosis). While it is generally thought that the control point for Type I/II occurs at the level of initiator caspase activation, we find that Type II cells can be converted to Type I by removal of XIAP, a regulator of effector caspases. Our study suggests that the classification of cells as Type I or II obscures a third variable category of cells that are highly sensitive to changes in the concentrations of key apoptotic network proteins.by Bree Beardsley Aldridge.Ph.D