Computational biology
Modern breakthroughs in assays and computing have enabled the massive collection of data on complex biological systems. I develop computational biology methods to improve our understanding of gene regulation and better inform in vitro scientific experiments. I have worked on projects including combinatorial biomarker identification, drug repurposing, experimental design, cellular aging, and nuclear mechanogenomics.
Articles
Cammarata, L., Uhler, C. and Shivashankar, G.V., 2023. Adhesome Receptor Clustering is Accompanied by the Co-localization of the Associated Genes in the Cell Nucleus. bioRxiv.
In this project, we characterized the map between protein assemblies on the cell membrane and 3D organization of the genome in nucleus, specifically for adhesome in IMR-90 fibroblasts using multiple genomic/epigenomic data modalities (RNA-seq, ChIP-seq, Hi-C, FISH).
Braunger*, J.M., Cammarata*, L.V., Sornapudi, T., Uhler, C. and Shivashankar, G.V., 2023. Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging. Aging Cell.
In this project, we developed a novel time-course prize-collecting Steiner tree algorithm to identify age-associated transcription factors and investigated the coupling between gene transcription and spatial gene clustering in aging (using RNA-seq, ChIP-seq, Hi-C). Our work got media coverage in the Eric and Wendy Schmidt Center.
Zhang, J., Cammarata*, L.V., Squires*, C., Sapsis, T.P. and Uhler, C., 2023. Active learning for optimal intervention design in causal models. Nature Machine Intelligence.
In this work, we developed a consistent causal active learning strategy to identify optimal interventions in linear causal models with known causal graph for the discrepancy between the post-interventional mean of the distribution and a desired target mean.
Belyaeva*, A., Cammarata*, L., Radhakrishnan*, A., Squires, C., Yang, K.D., Shivashankar, G.V. and Uhler, C., 2021. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing. Nature Communications.
In this project, we developed a drug repurposing pipeline using overparametrized representation learning, network analysis and causal structure learning and applied it to identify in silico candidate drug targets for the treatment of COVID-19. Our work got media coverage in the MIT News Office and other specialized online media.
Delaney*, C., Schnell*, A., Cammarata*, L., Yao-Smith, A., Regev, A., Kuchroo, V.K. and Singer, M., 2019. COMET: a tool for marker-panel selection from single-cell transcriptomic data. Molecular systems biology.
In this project, we developed COMET, a computational tool for combinatorial marker detection from single-cell transcriptomic data available as an online platform and a Python package and leveraged COMET to identify B cell subpopulations.
Article Python package GitHub Repo