* Refreshments will be served at 3:30 PM in the Lower Level 1 Gossage Atrium
* Lecture commences at 4:00 PM in L1255 in the Ford ES&T Building

Seminar Abstract
In a developing organism, what begins as a single cell must give rise to diverse tissues and body
structures. The pattern formation that underlies these crucial developmental processes must be
regulated at a variety of levels, from gene sequence to anatomy. With the growing body of
experimental data, and the realization of the high degree of complexity in development,
mechanistic models of development have become essential for integrating data, guiding future
experiments, and predicting the effects of genetic and physical experiments. However, the
formulation and analysis of quantitative models of development are limited by high levels of
uncertainty in experimental measurements, along with a large number of both known and
unknown system components. At the same time, an expanding arsenal of experimental tools can
constrain models and directly test their predictions, making the modeling efforts not only
necessary, but feasible [1].

As an example, we have formulated a mechanistic model to describe the pattern formation
controlled by the epidermal growth factor receptor (EGFR) network in the fruit fly, Drosophila
melanogaster. This receptor network, which is used dozens of times throughout the
development of all animal species, has also been found to be active in multiple types of tumors,
and thus, pharmacological studies of EGFR inhibition are common [2]. In our system, the EGFR
ligand, Spitz, is controlled by a negative feedback loop through a diffusible inhibitor, Argos,
which acts as a ligand sink [3]. We have shown that the model is consistent with previous
experimental data, and that the negative feedback loop serves to impart robustness to the pattern
[4]. We have discovered that neither correct patterning, nor robustness, requires a finely-tuned
dynamic length scale of Argos action [5]. Therefore, we are currently conducting experiments,
to be used in conjunction with the model, to estimate values of biophysical parameters, such as
the dynamic length scale of Argos. We expect further analysis of this