Scholar Profile

Stanislav Y. Shvartsman

Associate Professor
Department of Chemical Engineering
Princeton University
A421 E-Quad
Princeton, NJ 08544
Voice: 609-258-4694
Fax: 609-258-0211
Email: stas@princeton.edu
Personal Homepage
2003 Searle Scholar

Research Interests

Computational Analysis and Directed Manipulation of Cell Communication Networks in Development

Networks of intracellular proteins define the repertoire of cellular responses to environmental inputs. Normal functioning of these signal transduction systems is essential for the well being of both a single cell and an entire organism; defects in signaling networks may lead to a range of diseases. Elucidating the regulatory structure of signaling transduction networks presents a major challenge to biomedical research and requires a multidisciplinary approach. In our work, we combine reaction engineering, transport theory, applied mathematics, and computation to develop mechanistic models of receptor tyrosine kinase (RTK) signaling networks. Receptor tyrosine kinases are transmembrane receptors that can specifically bind extracellular ligands. Ligand binding activates the enzymatic activity of the receptor, and starts a sequence of biochemical and biophysical events that can specify a range of cellular responses. Signaling via RTKs is highly conserved across species: organisms as diverse as fruit flies and humans rely on homologous molecular components and network architectures for similar biological functions. Our long-term goal is the development of predictive models that can be used to assign the functionality of signaling networks in different organisms.

Transport Processes in Signaling through Receptor Tyrosine Kinases

In vivo, receptor tyrosine kinases are activated by locally produced ligands. Secreted ligands stimulate either the producing cell or its neighbors, which constitutes, respectively, autocrine and paracrine modes of cell communication. Although these mechanisms are ubiquitous in cell biology, the distances over which they operate are still a matter of guesswork. We use Brownian motion theory and stochastic simulations as the computational assays of transport in autocrine and paracrine signaling through receptor tyrosine kinases. Our models relate local concentrations and distances traveled by secreted ligands to the measurable properties of ligand/receptor systems. Transport of secreted ligands through tissues is quite complicated: any given molecule can undergo several rounds of binding to surface receptors, endocytosis, and extracellular diffusion. We are interested in deriving effective transport parameters, such as diffusion coefficients and kinetic rate constants that characterize such processes. This work is motivated by the recent findings demonstrating that a combination of localized production and restricted transport of growth factors can define morphogen gradients in development.

Modeling and Computational Analysis of EGFR Autocrine Loops in Drosophila Oogenesis

Spatially distributed cell signaling networks establish chemical blueprints guiding tissue- and organogenesis in a developing organism. The Epidermal Growth Factor Receptor (EGFR), a receptor tyrosine kinase, has been implicated in a large number of developmental events across species. In the development of the Drosophila egg (oogenesis), a network feedback loops acting through the EGFR has been hypothesized to spatially modulate a simple single-peaked input into a more complex two-peaked signaling pattern that specifies the formation of a pair of dorsal appendages on the eggshell. To test this hypothesis, we integrate the genetic and biochemical information about the Drosophila EGFR signaling into a mathematical model. We use large-scale dynamical systems analysis to evaluate the alternative mechanisms hypothesized in this system. The model allows for a number of predictions about the transitions between different eggshell phenotypes. Together with Trudi Schupbach at the Department of Molecular Biology, we have started an experiment towards testing both the original hypothesis and the model predictions. Genetic techniques are applied to manipulate the signaling network, and biochemical assays are used to visualize the spatial distribution of the network’s components.