Trainee Nir Yungster Wins Poster Award

nir_0Trainee Nir Yungster (Project 4), a graduate student working in the Research on Complex Systems group, received an award for his poster “Optimizing Rate Constants in Epigenetic Markov Models” at the 2013 PS-OC Annual Investigators’ Meeting.  Yungster was one of six recipients of the award out of 129 presenters.  Here is Nir’s research in his own words:

Describe your research in general terms.

I presented work on a mathematical model that describes the dynamics of histone modifications in cancer cells.  Histones are proteins that act as spools, allowing DNA to coil into a more compact form.  Through a number of mechanisms, these proteins can significantly impact gene expression in a manner dependent on chemical modifications made to them.  Cancers such as B-cell lymphoma and multiple myeloma have both been linked to proteins that modify these histones, and thus understanding the dynamics of this system can provide important insight into possible treatments for patients.  Our model uses experimental data to quantify the rates at which modifications occur in cancer cells, and allows for dependence on the current modified state of the histone.

How did the PS-OC help shape or develop this research?

My advisor and Project-4 leader Bill Kath and I have been fortunate to find a terrific set of collaborators in Neil Kelleher and Yupeng Zheng, without whom this work would not be possible. The model I presented was a natural outgrowth of their groundbreaking ‘M4K’ approach for tracking the kinetics of histone modification in human multiple myeloma cells as part of a PSOC Pilot Project. Bringing together their top-down proteomics expertise with our mathematical modeling background has made for a fruitful partnership, and our work together continues to grow as both the experimental and modeling sides of the project mature.

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What is the next step?

While thus far we have only modeled a small subset of possible histone modifications, our model can be robustly extended to include a larger number of modifications in order to provide a fuller picture of histone modification kinetics. Additionally, by comparing the histone kinetics in cancer cells to the kinetics in normal ones, we hope to use our model to predict treatments that could promote transitions of cancer cells to their non-cancerous forms.

Full Abstract: Recently, a method was developed for conducting M4K – mass spectrometry-based measurement and modeling of histone methylation kinetics (Zheng 2012). We have made improvements to this method by incorporating previously unused experimental statistics into our model-optimization procedure that results in significantly improved fits to experimental data. Accurately modeling methylation changes to histone proteins is essential to understanding the activities of methyltransferases such as EZH2, which has been linked to human B-cell lymphoma, or MMSET, which has been linked to multiple myeloma (MM). Among MM patients, 15-20% show a t(4:14) chromosomal translocation which leads to the overexpression of MMSET. Zheng et. al. used MM cells with high MMSET expression to demonstrate the potency of their M4K approach. By comparing the results from a targeted knock-out of MMSET to a non-targeted knock-out, they obtain a measured decrease in methylation rates. Our model, with its improved modeling of such rates, can be incorporated in testing the effectiveness of drug therapies that might target the activities of methyltransferases. Among the adjustments we made to improve the calculation of these rates was an alteration to the optimization cost function to weigh deviations between our model and individual data points based on the precision of those experimental values. Additionally, we imposed new error terms to the optimization cost function to ensure that for large time, our dynamical model agrees with the steady-state behavior observed in experiment.