PSB 2004 Tutorial
Historically, biological experiments have been designed using simple
changes in one variable at a time with relatively few measurements. The
benefit of such an approach is that it improves the ability of the
experimenter to directly understand the experimental results. However,
the approach has several drawbacks. First, single variable manipulations
do not allow for accurate characterization of multivariate interactions.
Second, the simple changes that are introduced likely bias the data
towards steady-state behavior, missing many of the dynamic interactions
and regulatory effects. Third, the number of data points generated is
often insufficient to capture the true, underlying behavior. As a
result, the data sets generated are limited to analysis by such
techniques as clustering (rather than identification of interactions and
model parameters). With the advent of high throughput experiments for
mRNA, protein, and metabolite concentrations, it is possible to design
experiments that allow for better insights and improved understanding of
interaction and regulation than clustering is able to provide.
This short course demonstrates each of the key issues in experimental design for the construction and identification of dynamic models for genetic and regulatory networks. Specifically, issues such as input selection, perturbation frequency, perturbation sequence design, and sampling frequency are addressed. The impacts of these issues on data quality are demonstrated through sample biochemical networks.
Kenneth Kauffman earned his doctorate in Summer, 2003 from Jeremy Edwards (University of Delaware). His thesis explored the design of biological experiments for improved understanding of regulatory and biochemical networks. He has explored the use of statistical analyses of complex datasets for inferring dynamic regulation and connectivity and for enhanced metabolic engineering. Prior to his thesis work, he carried out experiments and developed detailed, mechanistic models relating to the unfolded protein response in S. cerevisiae. Based on experimental limitations, he has also proposed a framework for producing reduced complexity models from full mechanistic models. He is currently serving as a post-doctoral researcher under Jay Keasling (University of California, Berkeley). Dr. Kauffman will be an assistant professor in chemical engineering (University of California, Riverside) in 2004. Dr. Kauffman has served as a teaching assistant for four undergraduate courses and one graduate elective, a co-instructor for Engineering Pedagogy, and as a teacher for several semesters in the public school system teaching grades 5-12 science. He holds a license to teach in the state of Iowa, has published three peer reviewed engineering education articles, and was one of two recipients of the 1998 American Society for Engineering Education's Apprentice Faculty Grant Award, recognizing his potential to impact the future of engineering education.
Babatunde A. ("Tunde'') Ogunnaike received the B.Sc. degree (with First Class Honors) in Chemical Engineering from the University of Lagos, Nigeria, in 1976; the M.S. degree in Statistics from the University of Wisconsin-Madison in 1981; and the Ph.D. degree in Chemical Engineering also from the University of Wisconsin-Madison in 1981. From 1981 to 1982, he was a Research Engineer with the Process Control group of the Shell Development Corporation in Houston, Texas; and from 1982 to 1988, he was a professor at the University of Lagos with joint appointments in the Chemical Engineering and the Statistics Departments. He joined the Advanced Control and Optimization group of DuPont Central Science and Engineering in 1989. From 1995 until September 2002, he was a Research Fellow in DuPont Chemical Sciences and Engineering. An Adjunct Professor in the Chemical Engineering Department of the University of Delaware since 1989, he joined the faculty as a full Professor in September 2002. He is the co-author of a widely used textbook, Process Dynamics, Modeling and Control, published in 1994 by Oxford University Press; he is also an Associate Editor of the journal Industrial and Engineering Chemistry Research. Dr. Ogunnaike is widely recognized as an outstanding instructor for his chemical engineering courses (including Process Control, Statistics for Engineers, and Design of Experiments) and for his short courses (including Statistical Design of Experiments, Statistical Process Control, and Process Control). His courses typically receive outstanding reviews from students and outside observers. Dr. Ogunnaike has published over 35 peer reviewed journal articles on topics including modeling and control of polymer reactors, extruders, distillation columns, and particulate processes; identification and control of nonlinear systems; the interaction of process design and process operability; applied statistics; reverse engineering biological control systems for process applications; and systems biology. He is the recipient of the American Institute of Chemical Engineers 1998 CAST Computing Practice Award.
Jeremy S. Edwards is an Assistant Professor in Chemical Engineering, the 2003 Outstanding Junior Professor of Engineering (University of Delaware) and an Adjunct Professor in Biochemical and Molecular Pharmacology (Thomas Jefferson University Medical School). He has published 30 papers in computational and experimental biology. He was the first researcher to extend flux balance analysis to entire organisms, to apply constraints to reduce the number and complexity of experiments required for accurate phenotype prediction, and to be able to predict the evolution of a species grown under constraints. His laboratory produces both experimental and computational research, studying such systems as human cancer, S. cerevisiae, E. coli, and D. radiodurans. Dr. Edwards has taught several graduate and undergraduate courses at the University of Delaware (Metabolic Engineering, Thermodynamics, and Junior Lab), short courses at Thomas Jefferson University, and tutorials to various groups around the United States. His short courses and the discussions that entail are typically well received. Dr. Edwards is an enthusiastic instructor who involves his students, incorporates timely examples and real-world problems to demonstrate interesting points, and fosters open discussions about the issues at hand.
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