Sutton Place Hotel
Newport Beach, CA
in conjunction with the
The Computable Plant project is a systematic effort to advance the understanding of the shoot apical meristem (SAM) of Arabidopsis thaliana through imaging and computational modeling of developmental processes. Interesting and generic problems arise within this computational approach. For example, to quantify the growth of the SAM and its cell lineages requires detecting and tracking multiple features in 3D image sequences, and finding a smoothed global velocity field due to growth; we approach this problem through nonlinear optimization. Also, fitting the resulting data to dynamical models requires a flexible modeling framework for coupled mechanical and regulatory networks. For these problems we develop a mathematical foundation based on the use of a ?dynamical grammar? capable of representing discrete-time events such as cell division that change the number of objects and their relationships, as well as continuous-time processes arising from regulatory networks and mechanical interactions. The resulting algorithms are being used to assist experimental research on mechanisms of meristem maintenance and phyllotaxis.
Joint work with Tigran Bacarian, Pierre Baldi, Ashish Bhan, Victoria Gor, Marcus Heisler, Henrik Jönsson, Elliot Meyerowitz, Venu Reddy, Alex Sadovsky, Bruce Shapiro. Further information available at http://www.computableplant.org.
Eric Mjolsness received an undergraduate degree from Washington University in St. Louis in 1980 and a PhD in physics from the California Institute of Technology in 1986. He has served on the faculties of Yale University, the University of California San Diego, the California Institute of Technology, and the University of California Irvine where he is currently a member of the Institute for Genomics and Bioinformatics and the Computer Science Department. He has also served as a leading member of the Machine Learning Systems Group at the Caltech Jet Propulsion Laboratory. His research interests are largely connected with the construction of scientific inference systems, using techniques from machine learning, pattern recognition, nonlinear optimization, statistical physics and other mathematical disciplines to further research into computational biology and other sciences.