Erzsébet R. Regan, PhD
Instructor of Medicine
Beth Israel Deaconess Medical Center
Harvard Medical School
330 Brookline Avenue, RN 270H
Boston, MA 02215
Regan Lab >>
Erzsébet Ravazs Regan earned a PhD in Physics from the University of Notre Dame, Notre Dame, IN in 2004. She then started a two-year research fellowship at the Los Alamos National Laboratory as a Director Funded Postdoctoral Fellow. In late 2006 Dr. Ravasz Regan joined the Division of Molecular and Vascular Medicine at Beth Israel Deaconess Medical Center as a postdoctoral fellow, and became a member of CVBR as an Instructor in 2007.
Transcriptional Regulatory Networks
My group is interested in investigating biological systems from a complex networks perspective, with special emphasis on transcriptional regulatory networks and the logic of biological regulation.
Most of our current topics are motivated by endothelial cell biology, and consist of computational work in collaboration with experimental laboratories at CVBR.
Effects of VEGF Signaling on Endothelial Gene Transcription
We are developing methodology to identify a poorly mapped region of the VEGF regulatory network: that of the boundary between signaling events and transcriptional regulation. We use a network-based computational approach that mines large sets of microarray data from the literature to predict new transcription factors targeted by VEGF signaling. Human cells have been perturbed in many different ways to generate a large amount of micro-array data, thus a wealth of information about transcriptional interactions is hidden in these data. Microarray measurements, however, do not directly see signaling events. We are trying to capture the point(s) in the regulatory system where signaling information introduces a "perturbation" and initiates the first transcriptional events.
Logic of Combinatorial Regulation
Combinatorial complexity of signaling and transcription, although widely recognized in theory, remains poorly amenable to reverse engineering. Boolean logic gates governing digital computers offer a simple but powerful way of modeling functional interactions between transcription factors. Based on this formalism we are developing an experimental methodology, based on concurrent manipulation of two transcription factors, which can map the combinatorial possibilities of genetic control by the interplay between these two factors. Additionally, we use binding site information and large numbers of public microarray measurements to predict the type of genetic control expected from our experiments.
Modeling the Emergence of Biological Function
We have recently begun investigating the possibility that biological function can be defined and captured by minimal but whole-scale cellular models. We are using a Boolean modeling framework, simple and flexible enough to model regulatory networks at the scale of the whole cell, but one that also captures combinatorial biological regulation governing broad features of cellular dynamics. We are working towards creation of a full-scale cellular model capable of synthesizing known molecular pathways into dynamical system, where function is defined and captured at multiple levels of organization.
New and Noteworthy Publications
View all publications via PubMed >>
Ravasz Regan E. Networks: Structure and Dynamics. In: Meyers RA, editor in chief. Encyclopedia of Complexity and System Science. Springer; 2008 (in press).
This review chapter is a brief account of current advances in the field of complex networks.
Ravasz E, Gnanakaran S, Toroczkai Z. Network Structure of Protein Folding Pathways (submitted).
This study presents a model of conformation spaces of polypeptide chains as complex networks and uncovers structural characteristics of conformation networks responsible for dynamical features of folding pathways, as seen in molecular dynamics simulations.
Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL. Hierarchical organization of modularity in metabolic networks. Science 2002; 297:1551.
This study show that the metabolic networks of 43 distinct organisms are organized into many small, highly connected structural modules that combine in a hierarchical manner into larger, less cohesive units, with their number and degree of clustering following a power law. This hierarchical modularity closely overlaps with known metabolic functions in E. Coli.
Ravasz E, Barabási AL. Hierarchical organization in complex networks. Phys Rev E 2003; 67:026112.
This study show that most real networks have hierarchical modularity, where tight small clusters are embedded in larger and looser modules, without having a characteristic cluster scale.
Ravasz E, Vicsek T, Brechet Y, Barabási AL. The sound of many hands clapping, Nature 2000; 403:850.
This study describes the repeated emergence and disappearance of synchronized clapping following theatrical performances as competition between synchronization and noise intensity, conflicting means of expressing appreciation for a performance.