Our lab has recently moved to Altos laboratories. However, these pages are here for community referenced until they are relocated.
Exploration of MOMP at the membrane level using molecular, coarse-grain, and continuum descriptions of the signaling network.
The Bcl-2 family of proteins exhibit a rich interplay between activators, inhibitors, and sensitizers that leads to eventual mitochondrial outer membrane permeabilization (MOMP). Despite great advances in our understanding of the mechanisms that lead to MOMP, we still are faced with gaps in our knowledge of the protein-protein interactions at the molecular level that lead to MOMP. In this work we combine molecular simulation at the atomic and coarse-grain levels of description as well as continuum mass-action mechanistic modeling to better understand the MOMP regulation network.
Studies of multi-pathway representations of cell signaling to understand cellular commitment to fate in healthy and diseased cells.
Multiple pathways have been described for cellular functions such as proliferation, growth, and programmed cell death. These pathways have been studied in isolation, both experimentally and theoretically. Recent advances in experiments, however, have started to yield information about multiple pathways and how they compete for different outcomes. We are particularly interested in the mechanistic interpretation of ‘omics’ data to identify the strategies that cells use to maintain a life and death balance in cells. This work involves experimental collaborations to understand how treatments with single or combinations of drugs affect the outcome of healthy as well as cancer phenotypes.
Toward a better description of cellular environments.
The common practice in systems biology modeling is to use mass-action approximations, suitable for well-mixed systems, to simulate biochemical reactions in cellular environments. However, due to confined spaces, low number of molecules, compartmentalization, etc the cell is far from a well-mixed system. In addition most studies employ simulations of “average cells” to represent a multitude of cells within a tissue. We are currently investigating addressing two aspects of this problem, at the interface of particle-continuum description and at the interface of single-multiple cell descriptions. For this goal we develop tools (PySB, MAGINE, HypBuilder) which enable us to express biological concepts in a programming environment. This project involves the use of techniques such as statistical mechanics, molecular simulation, mass-action kinetics, and fluid dynamics as applied to cellular systems.
From protein interactions to chemical reaction kinetics.
A central challenge to understand how cells process cues and elicit responses involves the fundamental problem to link atomic and molecular interactions with signaling networks and cellular phenotypes. We are addressing this challenge with a combination of modeling and experiments with our collaborators to understand how allostery and inhibition at the molecular level lead to information flow in biological networks. This project involves statistics, high-performance computing, machine learning, and data analysis to understand how information flows in a network.