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I work at the interface between mathematical modeling and
biological experimentation, as an experimentalist who is learning
modeling. I use computational simulations of models to
help design, prioritize, and interpret experiments so that
each experiment is more likely to yield new insights. I
am using modeling to identify experiments that will best distinguish
between alternative mechanisms in regions of the pathway where
existing molecular details are vague. Also, I am using
modeling to explore how negative feedback can control the properties
of signal transduction.
Over the years, we have arrived at a new principled process
for constructing quantitative models of biological systems. The
three pillars of principled biological modeling are (1) measuring
parameter values whenever feasible, (2) embedding reaction
rules and parameters in documentation of underlying choices,
and (3) generating mathematical models automatically from reaction
rules specified within the documentation. See yeastpheromonemodel.org.
Experimentally, to ground the models in reality, I have measured
the abundances (molecules per cell) of the key protein components
of the mating pathway. I am making targeted perturbations
to the pathway by changing the protein abundances and eliminating
specific feedbacks. Careful observation of pathway behavior
under these perturbed conditions can reveal molecular mechanisms,
uncover design principles, and identify robust properties of
the pathway.
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