Investigator |
Institution |
Award Amount |
Award Time Period |
Bernhard Palsson |
University of California-San Diego |
|
FY1998-FY2000 |
Co Prinicipal
Investigators/Related Projects:
George Church (Harvard University)
Bernhard Palsson (U.C San
Diego)[NSF-BES]
Bernhard Palsson (U.C San Diego)[NIH]
Sponsored by: National Science Foundation (KDI)
Project Number: 9873384
Brief
Description of Project:
DNA sequencing technology is revolutionizing biology. DNA
sequence data now needs to be translated, into functional
information. The first step is the prediction of the function
of individual gene products from their sequence through
comparison with other known genes and genomes. The second
step, and perhaps the more challenging one, involves the use
of this functional information on individual genes to predict
cellular functions resulting from their coordinated activity.
In order to accomplish the objectives of this second step,
model in silico organisms need to be built based on genomic
data, and systems analysis methods developed to describe them
so as to analyze, interpret, and predict the
genotype-phenotype relationship. At present there is no
computational infrastructure available to address these
challenges. This project addresses this overall question by
the following two methods: 1. Structural and steady state
analysis methods will be developed to formulate organism-scale
metabolic networks from annotated sequence data. These
analysis methods will be presented within the Biology
WorkBench (bioweb.ncsa.uiuc.edu). This capability will include
the development of a graphical user interface that shows the
metabolic map for a genomically defined metabolic genotype and
the display of flux balance solutions thereon. Flux balance
methods will be developed to study the characteristics and
capabilities of defined genotypes, including the variation in
the genotype, prediction of metabolic shifts, prediction of
genome scale expression, and formulation of defined media, 2.
To characterize the dynamic characteristics of multi-enzyme
systems, methods will be developed to catalog, interpolate,
and predict enzyme kinetic properties. These methods can be
used to assign enzyme kinetic properties to annotated genome
sequences. Methods will also be developed for temporal
decomposition of a large set of simultaneous metabolic
reactions. These methods will be able to determine the
independent modes of motion in complex systems, give a
physiological interpretation of these modes, and to predict
the pool transformation matrix that can subsequently be used
for model reduction for description on a selected time scale.
The results of this research will form the basis for the
needed computational infrastructure for the formulation and
testing of in silico metabolic representations of living
cells. Basically, the result is the important metabolic
genotype-phenotype relationship, and means to analyze,
interpret, and predict it. These relationships will form the
basis for metabolic engineering based on annotated genomic
data.
Project
Starting Date: 08/99
Project
Ending Date (Planned):
08/01
Research Area: |
Bioinformatics/Genetics |
Potential Field of Use: |
Basic Research |
Federal
Contact Name:
NSF
Contact:
Hector Flores Email: heflores@nsf.gov
Phone:(703)306-1441
Last
Revision: 02/18/2000
|