
Research Overview
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 3dimensional arrangements of atoms
in proteins to the 3dimensional 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) Gprotein coupled signal transduction
in cardiomyocytes and BCells, 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, coopting yeast twohybrid technology,
a reusable, "pluggable", flipflop 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). 

