Now showing items 1-6 of 6

    • Analysis of growth factor signaling in genetically diverse breast cancer lines 

      Niepel, Mario; Hafner, Marc; Pace, Emily A; Chung, Mirra; Chai, Diana H; Zhou, Lili; Muhlich, Jeremy L; Schoeberl, Birgit; Sorger, Peter K (BioMed Central, 2014)
      Background: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent ...
    • ChemBank: A Small-Molecule Screening and Cheminformatics Resource Database 

      Seiler, Kathleen Petri; George, Gregory A.; Happ, Mary Pat; Bodycombe, Nicole E.; Carrinski, Hyman A.; Norton, Stephanie; Brudz, Steve; Serrano, Martin; Ferraiolo, Paul; Tolliday, Nicola J.; Clemons, Paul A.; Sullivan, John P.; Muhlich, Jeremy; Schreiber, Stuart L. (Oxford University Press, 2008)
      ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. ...
    • Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling 

      Aldridge, Bree Beardsley; Saez-Rodriguez, Julio; Muhlich, Jeremy; Sorger, Peter Karl; Lauffenburger, Douglas A. (Public Library of Science, 2009)
      When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing ...
    • LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures 

      Duan, Qiaonan; Flynn, Corey; Niepel, Mario; Hafner, Marc; Muhlich, Jeremy L.; Fernandez, Nicolas F.; Rouillard, Andrew D.; Tan, Christopher M.; Chen, Edward Y.; Golub, Todd R.; Sorger, Peter K.; Subramanian, Aravind; Ma'ayan, Avi (Oxford University Press, 2014)
      For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene ...
    • Programming biological models in Python using PySB 

      Lopez, Carlos Francisco; Muhlich, Jeremy; Bachman, John Ata; Sorger, Peter Karl (Nature Publishing Group, 2013)
      Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple ...
    • Properties of cell death models calibrated and compared using Bayesian approaches 

      Eydgahi, Hoda; Chen, William Wei-Lun; Muhlich, Jeremy; Vitkup, Dennis; Tsitsiklis, John N; Sorger, Peter Karl (Nature Publishing Group, 2013)
      Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions ...