Aviv Madar
Phone: 212-998-3976
Email: madaraviv AT nyu.edu
Room: Center for Genomics and Systems biology - 8th floor Brown Building
Address: 100 Washington Square E, New York, NY 10003

A bit about me:
I am a Biology PhD. Candidate with the Bonneau research group , at the Center for Genomics and Systems Biology (CGSB) at New York University.  I moved to NYC ~3 years ago from Israel.  I would like to consider myself as intelligent, spicy, someone who loves to travel and exploring new things.  I love cooking (everything), movies, a good exercise, good conversations and interesting people. 

Research: (Mentor Richard Bonneau, main collabarator Eric Vanden-Eijnden)
Organisms must continually adapt to changing cellular and environmental factors (e.g. oxygen levels) by altering their gene expression patterns.  At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation.  Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology.  We have recently described a network inference algorithm, the Inferelator (original paper pdf, systems-biology application for halobacter pdf), which infers regulatory influences for genes and gene-clusters.  The typical input is: 1) a microarray compendium composed of time-series and equilibrium measurements; and 2) prior information such as a set of considered predictors (e.g. transcription factors).  The output is a dynamical model for each gene, i.e. a differential equation describing the rate of change in mRNA concentration as a function of relevant predictors.  At the core of the algorithm is a model shrinkage step (L1-shrinkage) that allows the Inferelator to learn sparse models.  We have shown that the Inferelator is descriptive and predictive (up to the next time point in a time series) over a large test-set (with different conditions then train-set).  We are currently developing the  next version of the Inferelator.  To this end we are developing a Markov-Chain-Monte-Carlo (MCMC) optimization algorithm, which learns models that agree with the uncoupled as well as coupled dynamics of the system, and is constrained by the sparsity expected from biological systems.  This should allow us to model the dynamics of a cell’s mRNA expression levels, over longer time scale, such as the cell cycle.