The Center for Cancer Systems Pharmacology

We are reinventing the fundamental science underlying the development of new medicines and their use in individual patients.

The Laboratory of Systems Pharmacology (LSP) is a research and education program in the Harvard Program in Therapeutic Science (HiTS) that advances fundamental science and develops the technology needed for accelerating the invention of new medicines and personalization of patient care.

The lab’s students, faculty and staff study the molecular causes of disease, the ways drugs exert their therapeutic and adverse effects, and the design and interpretation of clinical trials. They also use machine learning and AI to develop the molecular diagnostics needed for precision medicine. LSP research will make the development of life-saving drugs more rapid and predictable, reduce the burden of therapy, lower costs, and improve outcomes for untreatable diseases. LSP training will empower students and fellows for leadership in academic research, industry, and government service.

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Explore LSP Research

thumbnail for publication: High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers
High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers
Lin et al. 2023. Nature Cancer.
Publication
thumbnail for publication: Sequential apoptotic and multiplexed proteomic evaluation of single cancer cells
Sequential apoptotic and multiplexed proteomic evaluation of single cancer cells
Lecky et al. 2023. Science Advances.
Publication
thumbnail for publication: Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer
Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer
Lin, Wang, Coy et al. 2023. Cell
Publication
thumbnail for publication: Automated assembly of molecular mechanisms at scale from text mining and curated databases
Automated assembly of molecular mechanisms at scale from text mining and curated databases
Bachman et al. 2023. Mol Syst Biol.
Publication
thumbnail for publication: Single-sequence protein structure prediction using a language model and deep learning
Single-sequence protein structure prediction using a language model and deep learning
Chowdhury et al. 2022. Nature Biotechnology.
Publication
Image of SARS-COV-2 from Adobe Stock #398699960
Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques
Baaijens, Zulli, Ott, et al. 2022. Genome Biology
Publication
thumbnail for publication: Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia
Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia
Charpignon et al. 2022. Nature Communications.
Publication
thumbnail for publication: Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma
Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma
Gaglia et al. 2023. Cancer Cell.
Publication

News, Events, & Updates

Seminar Series

“Managing the image-data life cycle for the real world: connecting the dots from sample preparation to image acquisition, analysis, and publication”

Caterina Strambio De Castillia, PhD

September 20, 1-2pm | Goldenson 122 

Watch Previous Data to Discovery Seminars

Social Lunch

“Trivia and Lunch”

September 13, 2-1:30pm | ARM108

LSP in the News

“A New Tool for Diagnosing Cancer”

Catherine Caruso | June 22, 2023

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