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Research Overview

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The research philosophy in the laboratory is that we learn by doing. Since we are trying to understand cellular function at multiple levels and with different sorts of data, we find that we come with scientific problems ranging from the pure mathematical to the very wet biological. The project area briefly described below are explained more fully on each of the area pages accessible from the left toolbar.
Applied Math: Biological systems are mutiscale, hybrid dynamical systems; they have both deterministic and stochastic aspects and they contain discrete and continuous processes. They are most often only partially observable and controllable. Further, different parts of the system have different levels of knowledge and description. These sorts of systems are difficult to simulate and statistically analyze.The applied mathematical research in the laboratory in keyed to creating general mathematical tools and algorithms for the analysis of data and models for such systems.
	Theory: Theory, in this context, may be thought of as the specialization of the applied math research to understanding the particular structures that turn up in cell biological and biological network modeling. These range from physical chemical theories for intracellular transport, to cell mechanical modeling, to stoichiometric network analysis. This is where the general engineering design principles for cellular regulation are developed.
Computation: When the system dynamics to be analyzed are too complicated it proves necessary to use computer simulation, numerical continuation, optimization and estimatation to get results. Implementing algorithms from the applied math efforts and following the developed theories, programs and tools may be be created for the analysis of biological data and static/dynamic analysis of network models.
	Data Analysis: This is where the abstract work above begins to be applied to real biological systems. The first step in the understanding of any system is observation. Measurements of biological systems are almost as diverse as life itself. It ranges from microscopically quantitative to macroscopically qualitative. It concerns the 3-dimensional arrangements of atoms in proteins to the 3-dimensional arrangements of cells in tissue. There is mutant data, molecular profiling data, imaging data, kinetic data and a host of other physiological and developmental measurement types. The goal of this research is to: 1) create integrated databases for this information to serve as a basis for data mining, 2) create functional queries and visualization for data linked to pathway information, 3) develop statistical data models for each of the biological data sets to aid in determining "significant" changes under different conditions, 4) deduce and parameterize regulatory networks. It is also in this area that more tradition sequence analysis techniques are developed.
Biosystems: The ultimate goal of much of the above research is to understand particular biological system function. We have a number of focussing biological problems chosen for their dynamical interest, industrial/medical importance, and because they implement an engineering functionality (such as switching) that is of general interest and may be compared within and across organisms (comparative functional genomics?). These system currently include: 1) Bacillus subtilis sporulation, germination, secretion and chemotaxis,2) E. coli metabolism, pili phase variation, transcriptional regulation, and phage infection, 3) Myxococcus xanthus sporulation and chemotaxis, 4) Saccharomyces cerivisiae response to pharmaceuticals, 5) G-protein coupled signal transduction in cardiomyocytes and B-Cells, and 6) Cholesterol metabolism in different mammalian tissues. We partner with a number of different experimental laboratories to create the data necessary to model these systems in molecular detail.
	Biomolecular Engineering: This effort is aimed at designing and implementing custom biochemical and genetic circuitry for pure and applied purposes. In one effort, for example, we aim to produce, co-opting yeast two-hybrid technology, a reusable, "pluggable", flip-flop in Saccharomyces cerivisiae that can remember which of two signal was last received. In other effort, we are designing and building (through rational and directed evolutionary methods), specialized gene expression cassettes that have different responses to external signals. The goal of these is both to learn how to program in the language of the cell and produce scientifically useful circuits.
Biosensing: Here we are trying to develop biosensors of two types: one capable of measuring protein and small molecule concentrations with high sensitivity and in parallel, and the other capable of tracking the statistical heterogeneity in cell fate and physiology (especially for the sporulating bacilli).