Affiliations: The Wallace H. Coulter Department of Biomedical
Engineering, Georgia Institute of Technology and Emory University, 313 Ferst
Drive, Atlanta, GA 30332, USA. E-mail: [email protected] | Department of Biostatistics, Bioinformatics and
Epidemiology, Medical University of South Carolina, 303K Cannon Place, 135
Cannon Street, Charleston, SC 29425, USA | Department of Microbiology and Immunology, 6730
Medical Science Bldg. II, The University of Michigan, Ann Arbor, MI 48109-0620,
USA
Note: [] Corresponding author
Abstract: Modern methods of high-throughput molecular biology render it
possible to generate time series of metabolite concentrations and the
expression of genes and proteins in vivo. These time profiles contain valuable
information about the structure and dynamics of the underlying biological
system. This information is implicit and its extraction is a challenging but
ultimately very rewarding task for the mathematical modeler. Using a
well-suited modeling framework, such as Biochemical Systems Theory (BST), it is
possible to formulate the extraction of information as an inverse problem that
in principle may be solved with a genetic algorithm or nonlinear regression.
However, two types of issues associated with this inverse problem make the
extraction task difficult. One type pertains to the algorithmic difficulties
encountered in nonlinear regressions with moderate and large systems. The other
type is of an entirely different nature. It is a consequence of assumptions
that are often taken for granted in the design and analysis of mathematical
models of biological systems and that need to be revisited in the context of
inverse analyses. The article describes the extraction process and some of its
challenges and proposes partial solutions.
Keywords: Biochemical Systems Theory, metabolic profile, network identification, pathway analysis, proteomics, S-system, time series
