Physical Chemistry of the Cellular Interior
The interior of both prokaryotic and eukaryotic cells are a
very non-classical environment as far as the assumptions of most
physical chemistry is concerned. The solutions are not really
aqueous; there is a great deal of organization and inhomogeneity
in the chemical concentrations; macomolecules are densely packed
together, there are mechanical coupling to the chemistry; and
many processes are driven by numbers of molecules and at rates
slow enough that the thermal fluctuations in reaction rates become
significant. We re-examine the thermodynamics, statistical mechanics
and kinetics of gene expression, biochemistry and morphogenesis
under these novel conditions to try and understand the physics
of the cellular environment.
Nonlinear and Stochastic Dynamics
The physical chemical theories give rise to equations that describe
biological processes such as gene expression, protein transport
and catalysis, and cell membrane potentential. These equations
are nonlinear and often contain stochastic components. We are
interested in application and extension of bifurcation theory,
stochastic process theory and stoichiometric network analysis
to the dissection and reduction of these equations to better elucidate
the fundamental system dynamics and points of control in cellular
networks.
Analysis and Modeling of Cellular Processes
Cellular behavior and develop is governed by a complex network
of interacting chemicals that are spatially dispersed throughout
the cell. We are developing models of these processes (for particular
functions of particular cells) at many different levels of abstraction
ranging from detailed physical chemical models to qualitative
logical models. We are developing theories for how large networks
of chemical reactions can be decomposed into sub-networks that
can be analyzed without direct reference to the rest of the cellular
environment. We are also developing control theories that predict
pathways critical to the qualitative behavior of the network and
indicate entry points for the external control of this behavior.
Analysis of Biological Data
In order to deduce and parameterize the network models for the
particular organism understudy, typically a large amount of experimental
data is necessary. The types of data we have at hand are genomic
sequence, gene, protein and metabolite expression data (often
from microarrays, 2D protein gel, or capillary electrophoresis
measurements), nomarski and fluorescence microscopy images, in
vitro enzymological studies, and gross phenotypic data such
as growth rates, population heterogeneities, etc. We are developing
experimental quality control protocols, statistical data analyses
and network deduction algorithms for deriving and validating functional
models of signal transduction, developmental and metabolic pathways
mostly in prokaryotes. We are also interested in nucleotide and
protein sequence and structure analysis in so far as it helps
us predict network structure and function and in the design of
novel genetic circuitry.
Bioinformatics
Here we define Bioinformatics narrowly to be the theory and application
of the storage and retrieval of complex biological data. We are
engaged in constructing and curating databases whose information
spans the hierarchy of biological phenomena, from genes to tissues,
that are necessary for the analysis of regulation. Integrated
knowledge and databases of pathway data, kinetic and mechanistic
information, molecular profiling and interaction data, and image
data linked to external databases of protein and gene sequence,
structure and function are under development.
Biosensors
We are starting to develop biosensors to rapidly and sensitively
detect metabolites and proteins in a massively parallel fashion
analogous to DNA microarrays.
Genetics and Cell Biology
We have two different types of experimental efforts in this regard.
The first is in using the theories of genetic and biochemical
control we have developed to design and build custom genetic circuitry
that behaves in a specified way (toggle switches, pulse generators,
etc) in the cell in response to various signals. These are both
to serve the role of cell-based biosensors and as a means to probe
the cellular environment. The second are experiments driven by
the analysis of particular biological systems that answer particuar
questions or address missing data needed by that analysis.
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