Post Doctoral Fellow

Systems Biology

Harvard Medical School (Boston, MA)

Galit Lahav

The central theme in my research is the integration of complex data sets and mathematical modeling to understand and engineer cellular signaling systems.

My doctoral work focused on understanding signal transduction in human cells – both normal and pathologic – from a systems perspective, using novel computational tools to model, characterize, and predict cellular behavior. This approach was applied to two biological systems: (i) signaling through the epidermal growth factor receptor (EGFR), a receptor tyrosine kinase that is commonly overexpressed or structurally altered in human cancers; and (ii) phosphoinositide and calcium signaling in human platelets. Platelets are small, anucleate cell fragments that respond to vessel injury to prevent blood loss or, under diseased conditions, to initiate thrombosis.
Our effort to model EGFR signaling was strongly motivated by the observation that many cancer patients who harbor certain EGFR mutations show a remarkable response to tyrosine kinase inhibitors. Thus, we studied the effects of molecular alterations in the receptor (i.e., mutant forms of the receptor) on its kinetic behavior and downstream signaling responses. By modeling signal flows through branching pathways of the receptor, we showed that EGFR mutants had increased inhibitor binding, enhanced phosphorylation of particular substrate tyrosine residues, and preferential activation of the Akt signaling pathway, a critical pathway for cell growth, survival, and motility. This last prediction is consistent with experimental findings4 and is being pursued further.
In a second project, we developed the first computational human platelet model—assembled from 24 peer-reviewed studies—that accurately predicted the full transient calcium and phosphoinositide dynamics in response to increasing levels of ADP. In a full stochastic simulation of single-platelet response to ADP, the model provided accurate prediction of the statistics of the asynchronous calcium spiking behavior observed in single platelets and provided a quantitative molecular explanation for this stochastic behavior. Interestingly, the asynchronous spiking was a result of the platelet’s small size, suggesting that large populations of platelets may be needed in vivo to achieve a robust signaling response. The analysis also yielded specific, testable predictions regarding the requirement for high SERCA/IP3R ratios in functional platelets, limits on the concentration of intracellular calcium stores, and the relative potency of platelet agonists.
More recently, we have developed a high-throughput protocol to test the platelet response to all pair-wise combinations of 6 agonists at 3 doses. These 15,000 data points representing 154 pair-wise combinations were used to train an artificial neural network (NN) that successfully predicted sequential agonist responses and all ternary combinations of 3 of the platelet agonists. The NN model also identified combinations of 4, 5, and 6 agonists predicted to display synergistic signaling. These predictions are currently being confirmed experimentally. We are also training models for 3 individual normal donors to discover unique patterns of agonist synergisms that will generate a functional fingerprint for each donor. If successful, this approach will ultimately allow us to approximate the conditions in vivo where platelets interact simultaneously with many molecular signals.