Cell Cycle

Conserved cell cycle genes in four species

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

  • 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).

  • 04/2019

    Sabrina defends her PhD Sabrina Rashid successfully defended her PhD thesis titled Distributed Computing in Nature. Sabrina accepted a position as a research scientists in AI Therapeutics a biotechnology startup in Connecticut. Congratulations and good luck!

  • 09/2018

    We are leading the HuBMAP Computational Tools Center The Human BioMolecular Atlas Program (HuBMAP) is a large, national effort to develop a 3D map of the human body. As part of this program the NIH has funded 15 centers to collected new data, develop new technologies and develop computational methods for the analysis of the data. We are heading one of these centers, which is a part of the HuBMAP Integration, Visualization & Engagement (HIVE) effort. Our center will focus on the development of computational methods for the processing, analysis, modeling and retrieval of the HuBMAP data and on the interactions between users and the MAP. In addition to 4 investigators from CMU our center also includes co-PIs for UCSC and the UK and investigators from Google Brain. See CMU Press Release , Post-Gazette story , and KDKA radio story

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