Systems Biology
Systems Biology... is the quantitative analysis of networks of dynamically interacting biological components, with the goal of reverse engineering these networks to understand how they robustly achieve biological function.
What lies behind the complexity of biological networks? We attempt to answer this question by using a control theory approach to explore the exquisite architecture of a well known cellular stress system: the bacterial heat-shock response. The heat shock response is a mechanism for dealing with cellular stress resulting from the denaturation of cellular proteins. The response is characterized by the up-regulation of the heat shock proteins (HSPs) and is regulated directly by alterations in the level, activity, and stability of the sigma factor sigma-32. The logic of the heat shock response is implemented through a hierarchy of feedback and feedforward control architectures that regulate both the amount of sigma-32 and its functionality. We have developed a dynamic model that captures known aspects of the heat shock system and are using it to exploring the logic of the heat shock response from a control theory perspective, drawing comparisons to control systems in engineering.
Apoptosis, Inflammation, and Stress in Ischemia
The goal of this project is to develop a dynamic model that provides a holistic understanding of the mechanisms of ischemic stroke. In collaboration with clinicians and medical researchers from Stanford Medical School and UCSF we are developing quantitative dynamic computer models that: a) capture the complex biochemical networks involved in ischemic strokes; b) provide a systems level understanding of the process of programmed cell death seen in stroke victims; and c) will help facilitate the creation and testing of alternative stroke treatments, e.g. through the overespression of HSP70. This model incorporates apoptotic, inflammation, and stress response pathways and the complex way in which they interact.
Noise Analysis of Gene Networks
The cellular environment is abuzz with noise. A key source of this "intrinsic'' noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise results in random fluctuations (over time) within individual cells and is also a source of phenotypic variability among clonal cellular populations. In some instances fluctuations are suppressed through an intricate dynamical networks that acts as noise filters. Yet in other instances, fluctuations are exploited to the cell's advantage. We use several analytic tools, some developed in our own group, to analyze and characterize the impact of noise in gene networks. Our studies have focused on the sensitivity of noise properties to parameter variation and network architectures, the impact of feedback through regulated degradation, coherence resonance, the role of noise in competence, and genetic switches.
Calcium Homeostasis
Calcium is tightly regulated in mammals because of the critical role of calcium ion concentrations in many physiological functions. In this work, we develop a model for calcium homeostasis and identify integral feedback control as a functional module that maintains this homeostasis. We argue that maintaining calcium concentrations in a narrow range and perfect adaptation seen when the calcium homeostatic mechanism is subjected to extreme disturbances are the result of a feedback control system implementing integral control through specific interactions of the regulating hormones. Based on the constraints imposed by the suggested integral control, we arrive at a simple dynamical model for calcium homeostasis. We show that the model is biologically plausible and is consistent with known physiology. Furthermore, the utility of the integral-feedback model is revealed by examining an extreme calcium perturbation, parturient paresis in dairy cows.
The Pap Pili Epigenetic Switch
We have been developing a new stochastic model for the study of the Pap pili epigenetic switch in Escherichia Coli. The model focuses on the period immediately following DNA replication during which the cell’s fate is decided as a result of a few critical stochastic chemical reactions. Gene methylation and protein-gene binding events are modeled as Markovian state transitions through which the Pap gene can reach any of 64 distinct epigenetic configurations; some of which allow for the production of the feedback regulator PapI. These patterns are composed with an infinitely variable PapI population level for an infinite number of possible system states. The resulting Chemical Master Equation is analytically approximated using the Finite State Projection method and compared with experimental data involving variations in the concentration of DNA adenine methylase. The model successfully captures all experimentally observed traits, and makes strong predictions that currently being tested in David Low’s lab here at UCSB.