{"id":1990,"date":"2026-03-31T22:15:08","date_gmt":"2026-03-31T22:15:08","guid":{"rendered":"https:\/\/www.hmc.edu\/eht\/?page_id=1990"},"modified":"2026-03-31T22:21:41","modified_gmt":"2026-03-31T22:21:41","slug":"february-13th-sarah-spencer","status":"publish","type":"page","link":"https:\/\/www.hmc.edu\/eht\/february-13th-sarah-spencer\/","title":{"rendered":"February 13th: Sarah Spencer"},"content":{"rendered":"\n

Single Cells, Many Paths: The Role of Data Science in Drug Discovery<\/h2>\n\n\n\n
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Dr. Sarah Spencer gave a talk on her career journey and provided insight on the target selection process for drug discovery. She graduated with a B.S. in Biology from Washington University. During her time in undergrad, she took a genomics education partnership course, which exposed her to computational biology. After spending two years as a research technician in a yeast RNA lab, she attended MIT for a PhD in Computational and Systems Biology. She currently works within discovery science at Novartis, a company that focuses on the following disease areas: oncology, immunology, cardiovascular, and neuroscience. <\/p>\n\n\n\n

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Dr. Spencer works within the early stages of drug discovery, target selection: discovering a novel target (gene) that can be modulated to impact a disease. This process aims to answer whether there are genes, RNA, or proteins that are differentially expressed under disease conditions. Many companies ultimately pursue the same targets. One target selection method is single cell RNA sequencing (scRNA seq), identifying and counting the number of RNA expressed in a single cell. <\/p>\n\n\n\n

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“What really clicked for me is how much drug discovery now starts on computers rather than in the lab. You can take single-cell genomic data, run it through neural networks, and build these maps that show which cells are tied to disease and how they might respond to gene tweaks. It made me see the whole process less as finding molecules and more as solving a systems-level puzzle.<\/strong>“<\/p>\n\n\n\n

This process generates large data and provides a window of what is occurring in one cell type at a particular moment. This data is analyzed with cell atlases, which combines data from many scRNA seq to form a large dataset. AI foundation models can be created by treating genes as words and cells as sentences to predict how a cell would change by a small gene edit and rank the order of genes within a cell. Dr. Spencer ended her talk by discussing career opportunities in biotechnology; many people enter as an analyst and rise to positions that design research experiments. Student questions were focused on the types of educational backgrounds best suited for the field and whether drug discovery is very interdisciplinary.\u00a0<\/p>\n\n\n\n

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Single Cells, Many Paths: The Role of Data Science in Drug Discovery Dr. Sarah Spencer gave a talk on her […]<\/p>\n","protected":false},"author":405,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-1990","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/pages\/1990","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/users\/405"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/comments?post=1990"}],"version-history":[{"count":4,"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/pages\/1990\/revisions"}],"predecessor-version":[{"id":2005,"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/pages\/1990\/revisions\/2005"}],"wp:attachment":[{"href":"https:\/\/www.hmc.edu\/eht\/wp-json\/wp\/v2\/media?parent=1990"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}