Mathematics and Synthesis of a Contact-Mediated Multicellular Patterning System

University Of California Berkeley, Berkeley CA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): We propose to develop a new mathematical approach to the analysis of pattern formation by contact-mediated signaling and to leverage the results to engineer a programmable multicellular patterning system. The mathematical approach blends graph-theoretic ideas with dynamical systems techniques to develop systematic tools for predicting and designing patterns, applicable to large networks of cells and broad classes of contact-mediated systems. The approach is to describe the configuration of the cells with a contact graph, and to exploit graph symmetries to partition the vertices into classes of cells with equal fates. We propose dynamical systems procedures to determine whether steady-state patterns structured according to candidate partitions exist, and to reveal the stability properties of such patterns. To engineer a synthetic bacterial contact signaling system, we propose to modify the newly discovered E. coli contact-dependent inhibition (CDI) system so that we can transfer proteins that control gene expression to adjacent cells. Contact-mediated signaling would allow higher information content than the previous quorum signaling systems, since whole proteins with different functionality may be transferred. By reestablishing patterning in a simple bacterial medium, this system will provide a testbed for theories of control and robustness in biological development. This patterning system may also lead to applications in programmable materials, tissue engineering, and compartmented biosynthesis. The project will produce both genetic parts for contact signaling systems and an analysis toolkit for researchers employing such systems. The team is composed of Murat Arcak (dynamical systems and control theory), Adam Arkin (synthetic biology), and Michel Maharbiz (biocompatible devices), who are current collaborators and provide the necessary mix of expertise in mathematical analysis and design, complex genetic components, and controlled cellular environments.