Shamim Mollah, PhD

Assistant Professor

Computational and Systems Biology Program
Molecular Genetics and Genomics Program
Cancer Biology Program
Biomedical Informatics and Data Science Program

  • 314-273-8438

  • Room 6315, 4444 Forest Park Blvd



  • AI-based network models, cancer systems biology, chromatin remodeling, post-translational modifications of histone, multi-omics integration, gene regulatory networks

  • Interpret and distill the complexity of cancer and other rare diseases through integration of large scale multi-omics data, special emphasis on phospho signaling pathways and post translational modification of histones

Research Abstract:

Our research goal is to interpret and distill the complexity of cancer and other rare diseases through integration of large scale multi-omics data using dynamic modeling, graph theory, and machine learning methods. We aim 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, we hope to extract relevant information necessary to make precise biological and clinical predictions and computationally direct experiments. The primary focus of our 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.

Research Projects:

Cancer systems biology
-----Chromatin remodeling in cancer
-----Tumor microenvironment
-----Cancer subtyping
-----Targeting cancer stemness pathway in breast cancer
-----Single-cell approaches to address tumor heterogeneity
-----Pharmacodynamics & pharmacokinetics of anti-cancer drugs
-----Biomarker discovery in cancer
-----Cancer immunotherapy
Modeling gene regulatory networks
Genotype-phenotype correlation
Natural language processing

Mentorship and Commitment to Diversity Statement:

Mission statement: Spread awareness of and inspire involvement in STEM through mentorship and outreach efforts both on and off-campus.

I am committed to helping students in their careers and become the best version of themselves by achieving their goals, introducing them to critical thinking, challenging their limiting assumptions, teaching them life lessons, and much more. My lab strives to cross-pollinate students from various STEM disciplines toward utilizing computational biology and systems approaches to address the underlying disease mechanisms. I am also committed to empowering and inspiring next-generation women in STEM field.

Selected Publications:

Xiang J, Lu M, Shi M, Cheng X, Kwakwa K, Davis J, Su X, Bakewell S, Zhang Y, Fontana F, Xu Y, Veis D, DiPersio J, Ratner L, Sanderson R, Noseda A, Mollah S, Li J, Weilbaecher, K. “Heparanase Blockade as a Novel Dual-Targeting Therapy for COVID-19”, 2022 Journal of Virology (in press)

Xiang J, Shi M, Fiala MA, Gao F, Rettig MP, Uy GL, Schroeder MA, Weilbaecher KN, Stockerl-Goldstein K, Mollah S, DiPersio JF. “Machine Learning-Based Scoring Models to Predict Peripheral Blood Hematopoietic Stem Cell Mobilization in Allogeneic Donors”, 2021 Blood Advances. 2021005149. (doi:/10.1182/bloodadvances. 2021005149)

Shi M., Mollah, S. “NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting”, 2021. (

Klie A, Tsui B, Mollah S, Skola DDow MHsu CCarter H. “Increasing metadata coverage of SRA BioSample entries using deep learning-based Named Entity Recognition”. Database, Volume 2021, 2021, baab021, (

Min Shi, Rintsen Sherpa, Liubou Klindziuk, Stefanie Kriel, Shamim Mollah. “A Non-Negative Tensor Factorization Approach to Deconvolute Epigenetic Microenvironment in Breast Cancer”. 2020. PLOS Computational Biology(in review) (

S. A. Mollah and S. Subramaniam, “Histone Signatures Predict Therapeutic Efficacy in Breast Cancer”. IEEE Open Journal of Engineering in Medicine and Biology, 2020, vol. 1, pp. 74 -82. (

Mollah SA, Subramaniam S. Global chromatin profiling fingerprints reveal therapeutic efficacy in breast cancer. 2019, CELL-REPORTS. (  

Mollah S, Dobrin J, Feder R, Tse S, Matos I, Cheong C, Steinman R, Anandasabapathy N; Flt3L dependence helps define an uncharacterized subset of murine cutaneous dendritic cells, 2014, Journal of Investigative Dermatology, 134(5):1265-75. (

Anandasabapathy N, Feder R, Mollah S, Tse S, Longhi M, Mehandru S, Matos I, Cheong C, Ruane D, Brane L, Teixeira A, Dobrin J, Mizenina O, Park C, Meredith M, Clausen B, Nussenzweig M, Steinman R. Classical Flt3L-dependent dendritic cells control immunity to protein vaccine. 2014 Journal of Experimental Medicine, 25,211(9):1875-91. (

Elbatarny M, Mollah S, Grabell J, Bae S, Deforest M, Tuttle A, Hopman W, Clark DS, Mauer AC, Bowman M, Riddel J, Christopherson PA, Montgomery RR, Zimmerman Program Investigators, Rand ML, Coller B, James PD, Normal Range of Bleeding Scores for the ISTH-BAT: Adult and Pediatric Data from The Merging Project, 2014, Haemophilia. 20 (6), 831-835. (

Last Updated: 3/19/2022 12:14:54 AM

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