Evolution is fueled by variation among individual organisms in characters such as body size, metabolic rate and resistance to infection. These characters typically emerge from an interaction between genes and environment through a complex process of development. Developmental mechanisms are widely regarded as being robust against many kinds of genetic and environmental perturbations, but the causes and consequences of this robustness are still unknown. The goal of this project is to investigate the evolutionary causes and consequences of the robustness of developmental systems. A family of models of developmental processes based on networks of interacting genes will be constructed. These computational models will range from the very simple and abstract to the increasingly detailed and data-driven, and will represent several different levels of biological organization from gene function and interactions between molecules, through cell proliferation, differentiation, and interactions between cells, to the morphology, complexity and fitness of individual organisms, and culminating in evolving populations of organisms. The results of this project will contribute to the construction of a quantitative theory of evolutionary developmental biology.
This project will generate new software tools that can be used to study diverse problems in evolutionary developmental biology and related disciplines. These tools will be made freely available on the internet for academic and educational use. This project will train graduate and undergraduate students in research. Approximately half of the University of Houston (UH) students have minority status, and many are the first in their families to attend college. Students from these typically underrepresented groups will be recruited to participate in the proposed research. In addition, the interdisciplinary nature of the project will promote collaborations between biologists, computer scientists, mathematicians and physicists, and these interactions will strengthen an interdisciplinary research cluster on Networks at UH along with interdisciplinary training for students. Finally, results from this study have long term relevance for understanding human diseases such as cancer.
