Using gene network models in evolutionary ecology: Gene duplication roles in flowering time control under natural environments
Gene duplications are a rich source for evolutionary novelty and are thought to promote both speciation and adaptation. However, understanding why particular duplications are preserved by evolution is difficult because the functions of related genes tend to overlap. The Arabidopsis flowering time pathway is an ideal system to investigate the role of ecological factors in maintaining genetic redundancy due to a long history of detailed molecular genetic, modeling and evolutionary ecological work on floral control in this species. This fellowships supports research that will combine mathematical modeling to explore the role of redundancy within gene networks and gene expression profiling in field trials to identify functional differences among duplicate genes. These data will be used to test the hypothesis that seemingly redundant genes in the Arabidopsis flowering time pathway have different roles in regulating the timing of flowering under real-world environments.
Training objectives include becoming proficient in fitting practical and realistic models of biological systems, and carrying out effective field and lab experiments to test predictions of these models. The broader impacts include incorporating evolutionary simulations into an ongoing summer training programs on genetics and evolution for high school teachers in Wyoming. Also, the Arabidopsis flowering time gene network has analogs in many important crop species. Models that can predict flowering under complex, natural environments in this model species may translate into improvements in crop models useful for agriculture.
Plants must accurately coordinate life history transitions, such as flowering, in seasonal environments to maximize their chance of reproducing while conditions are favorable. The optimal time to flowering differs among species and across geographic locations, depending on the length of the growing season, temperature, water, and many other factors. Therefore, plants have evolved complex signaling networks to assess both internal and external factors and regulate their development. The genetic pathways that control flowering in plants are best understood in the model species Arabidopsis thaliana. The central goal of this project was to synthesize this information in a quantitative model to explain how specific genetic changes alter flowering time in natural seasonal environments. I discovered that by combining a model of the gene action in three key genetic pathways (temperature, day length and winter chilling) with a model of plant physiology and growth in response to those same factors, I could accurately predict the timing of flowering across a number of field locations. Furthermore, by fitting the model to carefully selected genotypes with known differences in four genes controlling those pathways, I could use the model to accurately predict the flowering times of novel genotypes formed by re-combining these genes in all possible combinations. This result is important because it demonstrates a way to use quantitative models to predict the effect of genetic changes in many different environments. To support this modeling, I also ran three main experiments in controlled environmental conditions. I am currently using gene expression analysis to validate key predictions of this model and to identify new mechanisms that could be incorporated into future models. I have developed a web-application interface to the model that allows users (other researchers or instructors) to explore how different processes represented by the model alter flowering times in different environments with a simple graphical interface. This post-doctoral fellowship from the National Science Foundation allowed me to acquire in depth training in mathematical modeling of plant development, in plant physiology, and in model system genetics. As part of this fellowship, I have mentored both undergraduate and graduate students in research, and developed numerous collaborations both at UC Davis and at other institutions. This fellowship has given me the training and experience I need to succeed in my new position as an Assistant Professor of Plant Science at the University of California Davis, starting Summer, 2015.
