Ravi Iyengar 

Title: Dynamic Topology of Biological Networks: Functional Consequences  

Dorothy H and Lewis Rosenstiel Department of Pharmacology and Biological Chemistry

Mount Sinai School of Medicine, New York NY 10029 

The mammalian cell can be represented as a large modular network that is made up of a central signal network that interacts with and regulates multiple cellular machines that are responsible for phenotypic behavior.  We have used graph-theory approaches to analyze signal flow through a network representing the hippocampal neuron and find that signal-induced connectivity results in the formation of many regulatory motifs. Information flow through the central signaling network is initiated by extra-cellular signals such as hormones binding to their receptors. The flow of information through the signaling network results in the appearance of regulatory motifs such as feedback loops, feedforward and bifan motifs. Within the large cellular networks, these regulatory motifs are juxtaposed next to each other in several configurations such as stacked and nested. We have studied the dynamics of regulatory motifs by biochemical computation using ordinary differential equation models. Positive feedback loops can function as bistable switches. Nested feed-forward motifs can give rise to two emergent properties: coincidence detection and prolonged outputs for short inputs.  Bifan motifs can control response times, with some configurations working as delays and others promoting rapid responses. Bifan motifs can also act as filters. The functional consequences of organization of motifs within networks as well as the properties of feedback, feedforward and bifans motifs will be discussed.