NCXT Collaborator Sam Isaacson gets NSF CAREER awards

06 May 2014
Posted by admin

Associate Professor of Mathematics and Statistics Sam Isaacson has been awarded a CAREER Award in Mathematics by the National Science Foundation. It is a prestigious five-year grant, given annually to the top 20-30 tenure-track mathematicians nationally.

This award will fund the development of new numerical methods for simulating how proteins and mRNAs move about and interact within cells. These new methods are designed to allow the study of cellular processes, such as gene expression and signal transduction, in realistic three-dimensional models of the cellular space. An important feature of these models is the incorporation of explicit representations of cellular organelles and membrane surfaces, reconstructed from high resolution soft x-ray tomography data (courtesy C. Larabell at UCSF). The algorithms and computer programs made available by this research will enable more in depth studies of many pathways involved in cell signaling, growth, division, tissue development, and cancer proliferation.

The grant will also support the development of a more comprehensive mathematical biology program within the Department of Mathematics and Statistics. A new “Mathematical Biology on Clusters” course will be created to teach the process of computational model development for biological problems requiring the use of large datasets and large-scale computing resources. The new simulation tools we develop will be integrated into this course, enabling course projects in which students study models of specific cellular processes within their own cell (reconstructed from soft x-ray tomography data).

The award shows that there is substantial interest in developing new methods with which to model the spatial movement and interaction of proteins within cells. I’ve been interested in these questions for many years, but it is only recently that the needed computational resources, experimental data, and appropriate numerical methods to facilitate such modeling are becoming available. The planned research is one mathematical step in the long journey toward creating accurate, dynamic, three-dimensional in silico models of single cells from high-throughput and high resolution imaging data.

Link to original Boston University News article.