CAREER: Modeling made easy: Extending systems biology modeling approaches to genetics and ecology

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

The University of Wisconsin-Madison is awarded a CAREER grant to support Laurence Lowe in his research to extend rigorous mathematical simulation methods and make them available in a user-friendly model description language designed to solve computationally challenging forward simulation problems in biology. Many user-friendly simulation tools have been developed in systems biology, but their underlying mathematical representation impairs their use for important problems in genetics and ecology. This project will transform quantitative modeling into a mainstream activity that will be an integral part of biological research in the future. The first task will be to implement a new language that makes it easy to read and write such models for humans and for computers. To harness the large-scale computing power needed for analyzing complicated models, a link will be built to the evolution@home public global computing system and to private Condor pools. To efficiently store the results produced, a new standard for storing and sharing simulation results will be pioneered. The second goal will be to address combinatorial explosions in genetics and ecology models with algorithms and representations that work efficiently with both systems biology and genetics simulations, and that also allow for distributions of event times other than the standard assumption (exponential). Finally, the project will apply these new tools to intensely studied biological systems, including growth of VSV viruses in cells and adaptive evolutionary ecology of copepods (important zooplankton food source for fish). Close collaborations with neighboring labs will help shape the development of tools to ensure the resulting cyber-infrastructure is useful for cutting edge research. Such research is pivotal for increasing the quantitative rigor, realism and accuracy represented in models that will become increasingly important for various decision support systems. The same language and simulation tools developed for research will be used to teach graduate and undergraduate students, and design innovative teaching materials that improve the understanding of quantitative modeling for diverse audiences. Using "the real thing" is fascinating and transformational for learning, as witnessed by the rise of "R" in statistics. Working with K12 teachers, materials will be developed that explain the importance of good models to a broad audience. To this end, a brief interactive course with comprehension tests will be implemented to impart basic modeling knowledge, encourage responsible use of models, and discourage abuse. Successful completion of the course will provide a "License for Using Models". This and other teaching tools will be developed and tested on K12 students and evolution@home participants. The latter contribute CPU power to simulations of evolution and have a natural interest in models they are simulating, an interest to be met by an engaging website. The overall vision is to raise awareness for the importance of good quantitative models. The PI's career is to build such models. Further information about this project will be available at the PI's lab page at http://evolutionary-research.net/people/loewe.