In yeast, pheromone signaling can produce either a graded or binary transcriptional response depending on the dose of pheromone, the time of treatment, and the intracellular activation event being measured. A binary response may be appropriate in some physiological situations but not in others. For instance in yeast, pheromones initiate a process leading to mating, an inherently irreversible process where an all-or-none decision is appropriate. Binary outputs are also appropriate during cell division, cell differentiation, and cellular apoptosis. Thus, establishing the mechanisms by which the graded-to-binary conversion is accomplished is a fundamental problem in cell biology. Here we seek to identify components of the pheromone response pathway that mediate the graded-to-binary conversion and to uncover the mechanism by which this conversion is accomplished.
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Kinetic insulation of signaling pathways
We present a mechanism for signal specificity termed “kinetic insulation” that prevents cross-talk by virtue of the distinct chemical kinetics and network architectures of pathways that share common components. We reasoned that similar to modern communication devices that transmit multiple signals through a single channel, a cell might use biochemical networks to encode external cues into temporal patterns that can be received only by the intended target. Extending the analogy further, we designed simple architectures that can function as “filters” and combined them into a system capable of maintaining specificity under a wide range of conditions. Our computational experiments demonstrate how signal specificity can be achieved without the need for scaffold proteins or cross-inhibition.
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Research
Our research uses mathematical modeling to understand the design principles that underlie complex biological systems. While biologically distinct, the mathematical approaches used to describe these systems are similar.
With the increasing interest in formulating accurate models of large biochemical networks, there is a need for reliable software packages that correctly incorporate stochastic effects, yet are fast enough to simulate large interconnected sets of reacting species (as found, for example, in signaling cascades or genetic regulatory networks). We have developed the BIOchemical NETwork Stochastic Simulator, "BioNetS," to meet this need. BioNetS is capable of performing full discrete simulations using an efficient implementation of the Gillespie algorithm. It is also able to set up and solve the chemical Langevin equations, which are a good approximation to the discrete dynamics in the limit of large abundances. Finally, BioNetS can handle hybrid models in which chemical species that are present in low abundances are treated discretely, whereas those present at high abundances are handled continuously. Thus, the user can pick the simulation method that is best suited to their needs. All aspects of the software are highly optimized for efficiency.
In the airways, adenine nucleotides support a complex signaling network mediating host defenses. Released by the epithelium into the airway surface liquid (ASL) layer, they regulate mucus clearance. Mucociliary clearance (MCC) constitutes the first line of defense against airway infection. Inhaled pathogens are trapped by a mucus layer, positioned above ciliated epithelia by a periciliary (PCL) layer and transported outside of the organism. This process is severely compromised in Cystic Fibrosis patients, making them vulnerable to airway infection. 
