Regulatory Networks

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

Research

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

News

  • 09/2019

    Hamim Zafar accepts Assistant Prof. Position Hamim Zafar, who joined us in 2018 as a Lane Fellow, has accepted a faculty position as an Assistant Professor In one of Indias top schools: IIT Kanpur. Hamim is joining the computer science department and will continue his work on computational biology. Congratulations and good luck!

  • 08/2019

    Best Poster Award at ISMB 2019 Congratulations to Hamim Zafar (Lane Fellow) and Chie (Jessica) Lin for winning the F1000 best poster award at the most recent ISMB meeting in . The poster presented their new method, LinTIMaT, which was developed for integrating CRISPR-Cas9 mutation data with scRNA-Seq data for constructing linage trees. ISMB is the largest computational biology conference and included hundreds of posters of which only 5 were selected for an award.

  • 05/2019

    Bacteria tumbling paper published in PNAS Our most recent Algorithms in Nature work, which focuses on how bacteria navigates obstacle courses by adjusting its tumbling rate has been published in PNAS. In that paper we show both simulations and experimentally that by adaptively changing their tumbling rates as they encounter obstacles bacteria greatly improves their food search time. Application of the same idea to robot swarms indicates that it can be used to improve efficient search in constrained environments. More information can be found in CMUs press release, in a report by the NSF in a news and views piece in Nature Reviews Microbiology and in a great podcast interview by the Naked Scientist with Sabrina Rashid (the lead author).

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