Background

Shamim A Mollah, PhD, is an Assistant Professor of Genetics at Washington University School of Medicine in St. Louis. Dr. Mollah specializes in developing integrative network-based models using multi-omics data to study cellular processes. Her research goal is to interpret and distill the complexity of cancer and other rare diseases through genetics and epigenetic approaches using dynamic modeling, graph theory, and machine learning methods. She aims to apply these methods to study challenging cancer biology problems, particularly how chromatin alterations influence cellular phenotypes in response to genetics, environments, and pharmacological perturbations. By integrating large datasets, she hopes to extract relevant information necessary to make precise biological and clinical predictions and computationally direct experiments. The primary focus of her lab is to produce high-resolution computational models to study the effects of genetic and epigenetic perturbations on chromatin alterations that affect cellular states, elucidating the molecular mechanisms of cancer and other diseases. Previously, Dr. Mollah served as the bioinformatics scientist at the Rockefeller University, where she managed bioinformatics data analysis core for the Center of Clinical and Translational Science (CCTS). During her tenure at the Rockefeller University, her proposed bioinformatics research ideas led to a 2008 Obama challenge grant award and its renewal in 2011.

Dr. Mollah received her PhD in Bioinformatics and Systems Biology from UCSD. Her research was focused on applying network analysis-based models on multi-omics data using dynamic modeling, graph-theory, and machine-learning techniques to characterize drug responses in cancer cells. She studied the responses of drug individual/combinations on tumor cells and their effects on key proteins involved in cell signaling pathways. Dr. Mollah received her Master’s degree in Biomedical Informatics from Columbia University, where her research was focused on developing AI-based medical language parser using Natural Language Processing. She received her undergraduate degrees in Computer Science (B.S.) and Mathematics (B.A.) from Indiana University.

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