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


  • 08/2017

    CPCB directorship. Ziv is replacing Russell Schwartz as the director of the joint CMU-Pitt Computational Biology Ph.D Program .

  • 08/2017

    CMLH fellowship. Congratulations to Sabrina Rashid for receiving a Fellowships in Digital Health from the Center for Machine Learning and Health (CMLH) at CMU. Sabrina spent the summer as an intern in Microsoft Research and would start the fellowship on 9/2017.

  • 11/2016

    New paper in Science. Collaborating with the Ecker lab at the Salk Institute we have used computational methods to model Arabidopsis response to stress. The Science paper presents models that allowed our team to identify molecular conductors, top regulators of this response and have also led to the identification of new transcription factors that play a role in plant response to hormone and salt. See the CMU press release for more details.

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