“Engineering Designs behind Biological Networks: Combining Biochemical and Biophysical Modeling with Phenotypical Information”
Abstract
Biological networks have a number of unique and
distinguishing properties, which encompass a wide
variety of structural, functional, and control features
that have been evolutionarily selected to robustly
support life under a broad spectrum of conditions. Yet,
underlying this overt phenotypical diversity are
elementary chemical reactions and physical interactions
that link together the assorted molecular species and
complexes with which biological systems are composed.
Remarkably, it can be shown that this basic
characterization leads to a number of powerful
biochemical and biophysical constraints that profoundly
restrict the range of possible biological network
designs compatible with corresponding regulatory,
dynamic and other empirically observed traits. The
resulting modeling framework provides novel
understanding of engineering principles behind many
natural biological systems and offers new tools for
identifying their organizational features from
phenotypical information. Applications of this approach
range from improving reverse-engineering of gene
regulatory pathways, to the analysis of signal
processing capabilities of ubiquitous biochemical
motifs, to investigation of molecular virulence
mechanisms in prominent pathogens. Examples include
biological networks/circuits from E. coli, B. subtilis,
S. cerevisiae, Drosophila and HIV, among others. Bio
Michael Samoilov is a Research Staff Member at the California Institute for Quantitative Biosciences (QB3) at UC Berkeley. Dr. Samoilov earned his Bachelor’s degree with Honor in Physics and Mathematics from Caltech (1991). He then went on to do graduate work at Stanford University, beginning with high-energy physics and astrophysics, for which he was awarded an M.S. in Physics (1994), and continuing at the biophysics program where he received a Ph.D. in Biophysics (1997). After spending several years developing stochastic trading strategies for leading finance companies and running a Webby Award-winning multimedia start-up, Michael was drawn back to science by the emergence of novel biological systems engineering and analysis paradigms driven by the advances in single-molecule, single-cell and bulk high-throughput experimental methods. His most recent work includes investigating the role of discrete and stochastic dynamics in biological circuits, developing biochemically - and biophysically-driven methods for structural identification and functional analysis of biological networks, as well as studying information and signal processing characteristics of biomolecular reaction systems. |