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Workshop 1: Metabolic Engineering (September 24-28, 2007)

Organizers: Michael Savageau, David Gang, and John Doyle

Broadly defined, metabolic engineering seeks to change the metabolism and physiology of an organism to suit the needs or desires of the farmer, the breeder, the genetic engineer, and the scientist. Targeted selection for more flavorful wines , for higher milk production in cattle, for larger chicken breasts, for sweeter corn, and for larger and more flavorful apples are all examples of metabolic engineering products that have been largely successful. In all of these instances, the metabolism of the organism was altered in such a way as to allow that organism to display the desired traits. However, such breeding-program driven projects are very slow to produce results and often end in failure. The exact changes in the organism that result in the altered phenotype are often unknown, making reproduction of the same changes in these or similar organisms almost impossible.

Although metabolic engineering of plants and microbes is a major scientific activity today, there are numerous biological and, increasingly, mathematical challenges. One can organize the challenges of metabolic engineering roughly into four areas: measurement technologies (sensing and quantification) for generating data and monitoring system performance; mathematical modeling (formulation, verification, and analysis) for systematic representation and characterization of the system; molecular tools (actuators and regulators) for altering the system in a controlled fashion; and system integration (system [re]design, prediction, and control) for discovery of system design principles and rational optimization. Advances in one area are obviously dependent on those in the others. New developments in each of these areas will form the interrelated themes of this workshop. Examples from microbes and plants will be emphasized.

The workshop will be organized along the following outline:

1. Overview of Organisms, Biological Tools, and Strategies: Microbes, plants and animals, mutagenesis, knockout and transfer of genes, rational design and directed evolution.
2. Measurement Technologies and Data Analysis Tools: Metabolites and fluxes, mRNA, protein, miRNA.
3. Mathematical Modeling: Metabolic pathways, protein interactions, gene circuitry.
4. Molecular Tools: Enzyme design, rewired circuitry, de novo circuitry, biological computing.
5. Systems Organization and Integration: Enzymatic networks, gene circuits, miRNA speculations, robust design and control.