Achieving reliable control over cellular decisions to improve human health
Reliable control of cellular decisions (such as differentiation vs. self-renewal, fate map determination in development, or cell death vs. survival) requires the ability to (1) monitor the current cell state, (2) manipulate it when things go wrong, and (3) predict how the manipulation will affect the final cell state (see Figure). Unfortunately, we are a long way from such a predictive understanding. Therefore, to lay a foundation for understanding cellular decisions, we study the fruit fly, Drosophila melanogaster, which is the ideal balance between complexity and simplicity:
Complexity
Drosophila, as a model system, is complex enough to form the foundation of our understanding of animal biology and to impact many aspects of human medicine. This is because almost all of the signaling pathways — and even structures, such as stem cell niches — are highly conserved and very similar between flies and higher organisms.
Simplicity
Drosophila lends itself to rapid and facile discovery, due to easier handling, short life cycle, and fewer biological redundancies than vertebrate systems. Moreover, in Drosophila, quantitative, live-tissue imaging can be rapidly and reproducibly performed at large sample size. Perhaps most importantly, Drosophila provides these opportunities in an in vivo system in which cells interpret signals and make decisions within their native context, surrounded by other cells in the tissue. This fact not only simplifies experiments, but also is advantageous, because many medically relevant phenomena require native context, such as stem cell niches, tumor microenvironments, and tissue patterning.
A gradient of nuclear concentration of the transcription factor Dorsal induces gene expression fates in the early Drosophila embryo.
Can we predict and/or control cellular decisions reliably?
Current Research Projects
Systems Biology and Nautral Variation
Every person (and every fly) is different. Can we use these “personal” differences to decode the information in regulatory DNA?
Synthetic Biology and Network Motifs
“What I cannot create, I do not understand (Feynman).” Here we are building synthetic signaling networks to understand pieces of native networks.
Measuring Dynamics of Gene Regulation
In this set of projects, we are developing the tools to study rapid dynamics in live tissues. Our testbed system is feed forward regulation, which often acts as a filter on rapidly changing signals.
Regulation of Long Range Signals
Long range signals are noisy and, just like gene expression, require regulation. Negative feedback loops, robustness through diffusion-based carriers, and noise filtering mechanisms are several ways in which signaling can be regulated.