Regulatory Networks

Application of the Dynamic Regulatory Events Miner (DREM) to study amino acid starvation response in yeast.


Our group develops computational methods for understanding the dynamics, interactions and conservation of complex biological systems. As new high-throughput biological data sources become available, they hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, each type of data is noisy, contains many missing values and only measures a single aspect of cellular activity. Our computational focus is on methods for large scale data integration. We primarily rely on machine learning and statistical methods. Most of our work is carried out in close collaboration with experimentalists. Many of the computational tools we develop are available and widely used.

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Latest Publications

Full list of publications


  • 03/2021

    Working with Yale and Pitt on the PF connectome We are leading the computational analysis and modeling efforts for a new project focused on Pulmonary Fibrosis (PF). Together with Yale and Pitt we are working on developing computational methods to model disease progression at the single cell level and match drugs to different disease stages to improve personalized treatment of PF. See the news story for more details.

  • 10/2020

    Chieh's thesis awarded SCS Honorable Mention Chieh's Lin PhD thesis, 'Probabilistic Single Cell Lineage Tracing', is one of 5 from the School of Computer Science at CMU selected for a PhD thesis award. Chieh is the second student from our group to receive this honor. Last year, Sabrina Rashid's thesis was also selected for a honorable mention (see news item below from 11/19). Given the large number of PhD students at SCS and the amazing work they all do, this is a great honor! Congratulations to Chieh.

  • 06/2020 award to develop AI methods for predicting cocktail treatments for SARS-COV-2. Our proposed project was one of a handful of proposal selected for funding by the Digital Transformation Institute. Our project focuses on using machine learning methods to model the dynamic signaling and regulatory networks that are activated lung cells following infection with SARS-COV-2. Once such networks are reconstructed they will be analyzed to identify top proteins that can lead to reduction in viral loads. Next we will identify potential drugs to target combinations of these key proteins and test these on our recently constructed iPSC lung cell lines. This is joint work with our long time collaborator Darrell Kotton from Boston University and with Profs. Regina Barzilai and Tommi Jaakkola at MIT. See the press release, the CMU story and a story in Pittsburgh Post-Gazette.

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