Research Labs - Kansas State University

Kansas State University

An organism's genome is a functional control system that, inter alia, responds to inputs from the external environment, yielding a sequence of states whose observable features are phenotypes. Our research group (comprising faculty from molecular genetics, computer science, electrical engineering, and theoretical plant modeling) is interested in developing modeling technologies capable of elucidating genotype-to-phenotype relationships in plants. We are currently focused on flowering time control in Arabidopsis thaliana and rice (Oryza sativa) as test case systems in which to develop effective modeling methodologies. We are applying a variety of machine learning tools, including symbolic regression and support vector machines, to the analysis of various example networks. We have developed gene network models capable of predicting A. thaliana flowering time across a range of photothermal environments and mathematical analyses linking critical short day lengths in rice to photoperiod-dependent expression levels of Heading Date 1 (Hd1). Additionally, we have developed new multi-objective simplex-GA hybrid optimization methods for gene network parameter estimation based on a novel concept, Fuzzy Dominance. The method out-performs existing, gold-standard algorithms such as NSGA and SPEA at this particular task.

Dr. Stephen M. Welch
785-532-7236 or

Stephen M. Welch

Dr. William H. Hsu
Computing and Information Sciences
785-532-6350 or

William H. Hsu

Dr. Judy L. Roe
785-532-3174 or

Judy Roe


Dr. Sanjoy Das
Electrical and Computer Engineering
785-532-4642 or

Sanjoy Das

Mary Knapp
Weather Data Lab
785-532-7019 or


Mary Knapp

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