Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N. A2-025
P.O. Box 19024
Seattle, WA 98109-1024
Web page: http://www.smoldyn.org/andrews/index.html
My research is in the interdisciplinary field of systems biology. This field combines physics, chemistry, and biology methods to investigate organization within biological systems, on size scales that typically range from a few proteins to many cells. Results are yielding deep insights into how the highly structured macroscopic world of living organisms is built from the stochastic microscopic world of individual molecules. They are also providing an improved conceptual foundation for medical and biotechnology developments, with impacts on topics such as drug discovery, personalized medicine, biofuel generation, and bioremediation. My research projects include:
Computer simulations are are used in systems biology as a way to build intuition about the system dynamics, to test hypotheses, to make predictions, and to identify essential system components. I am developing modeling tools that can simulate biochemical systems with a relatively high level of detail, in which individual molecules are represented with nanometer-scale spatial resolution, but that are also fast enough to allow the simulation of tens of thousands of molecules over several minutes of real time. I developed algorithms for simulating reactions between freely diffusing molecules in solution and for interactions between molecules and surfaces. These algorithms are implemented in the Smoldyn computer program, which can be downloaded from the Software page. My current algorithm development addresses the detailed simulation of cytoskeletal filaments and membranes.
Biological cells are highly crowded spaces, with often 20-30% of the volume occupied by macromolecules such as proteins and nucleic acids. This fact has been both known and investigated for many years, but there remains no predictive theory for how much crowding slows diffusion, nor for the quantitative effects of crowding on biochemical reaction rates. Using a combination of analytical theory and computer simulation, I am developing semi-emipirical theories to address these questions. If successful, these theories will allow in vitro measurements of biochemical reaction rates to be converted to in vivo reaction rates, for the appropriate native biological systems. This will help address a major problem of cell biology modeling, applicable to both conceptual and computational models, which is that the quantitative data on intracellular biochemical reaction rates is generally very sparse.
The yeast mating pheromone response pathway is a classic model system for studying intracellular signaling because it is relatively easy to study, is similar to many mammalian signaling pathways, and is a rich system. In collaboration with other scientists at the Molecular Sciences Institute, I am using analytical and computational methods to investigate information transfer along the pathway. This study addresses topics such as the effects of biochemical feedback and feedforward on information transfer and the importance of having aligned dose-response curves at different points of the signaling network.
The bacterial cytoskeleton is highly dynamic. For example, the MinC, MinD, and MinE proteins of E. coli exhibit a remarkable oscillation between the cell poles: the MinD protein polymerizes in a helical coil that extends from one pole towards the cell center, is depolymerized by MinE, forms a new polymer from the opposite pole, and so on. In another example, the FtsZ protein forms a central ring around the cell center that constricts during cell division to yield two daughter cells. In recent work, I investigated how the micron-scale shapes of these cytoskeletal polymers might arise from mechanical forces between individual proteins, along with how these polymers are likely to apply forces to cell walls. While I am not continuing to focus on this research direction at the moment, I plan to return to it in a year or two, equipped with simulation methods that can simultaneously model chemical reactions, filaments, and membrane dynamics. This cell division system has been extensively modeled in the past, but it continues to reveal new insights and to be an ideal model system for exploring biochemical spatio-temporal dynamics.