Biographical Information

Research Interests

According to the 2002 edition of the Physicians’ desk reference, cancer therapies are simultaneously some of the most expensive and least effective. The reason for this lack of effectiveness is that cancer is a complex disease with many different causes, many of which require different treatments. The goal of the research in my group is to integrate experimental data together to create computational, systems level models of how cancer initiates and grows. In particular, we study cancers that arise from the misregulation of the sonic hedgehog signaling pathway—a master regulator of embryonic development.

To make these models, we employ a mixture of high throughput experiments and high performance computation. High throughput experiments such as gene chips, protein chips, and microfluidic assays allow us to gather large sets of quantitative biological data. To integrate this data, we use computational methods taken from engineering and machine learning, such as differential equation modeling, and probabilistic network modeling such as Bayesian networks. Using these tools, we can identify connections between our measurements, and make predictions based on these connections.

The impacts of this work are in both pharmacogenomics and drug discovery. Pharmacogenomics is the study of how our genetic sequence influences our response to drugs. By creating a systems level model of cancer growth, we can predict the effect of a drug given a specific genetic background. For drug discovery, our systems level models allow us to perform what if experiments to predict how well a particular cancer type will respond to a drug before the drug is even developed.

Recent Publications

Woolf, P. J. and Y. Wang, “A Fuzzy Logic Approach to Analyzing Gene Expression Data”, Physiol. Genomics, 3, 9-15 (2000).

Woolf, P. J., Kenakin, T. P., and J. J. Linderman, “Uncovering Biases in High Throughput Screens of G-Protein Coupled Receptors”, J. Theoretical Biol. 208(4), 413-418 (2001).

Woolf, P. J. and J. J. Linderman “Untangling ligand induced activation and desensitization of G-protein-coupled receptors”, Biophys. J. 84, 3-13 (2003).

Zhong H., Wade S.M., Woolf P.J., Linderman J.J., Traynor J.R., and Neubig R.R. “A spatial focusing model for G protein signals. Regulator of G protein signaling (RGS) protein-mediated kinetic scaffolding”, J. Biol. Chem., 278(9), 7278-84 (2003).

Woolf, P. J. and J. J. Linderman “Self organization of membrane proteins via dimerization”, Biophys. Chem. 104, 217-27 (2003).

Woolf, P. J. and J. J. Linderman “Dimerization Algebras: implications for G-protein coupled receptor signal transduction”, J. Theoretical Biol. 229(2), 157-68 (2004)

Brinkerhoff C.J., Woolf P.J., Linderman J.J. “Applications of Monte Carlo Modeling to Signal Transduction”, accepted by Journal of Molecular Histology.

Woolf P.J., Prudhomme W., Daheron D., Daley G.Q., and Lauffenburger D.A., “Bayesian analysis of signaling networks governing embryonic stem cell fate decisions”, Bioinformatics 6:741-53 (2005).

Eide EJ, Woolf MF, Kang H, Woolf P, Hurst W, Camacho F, Vielhaber EL, Giovanni A, Virshup DM., “Control of mammalian circadian rhythm by CKIepsilon-regulated proteasome-mediated PER2 degradation.” Mol. Cell Biol. 25(7): 2795-807 (2005).

Rosania GR, Crippen G, Woolf P, States D, Shedden K. “A Cheminformatic Toolkit for Mining Biomedical Knowledge.” Pharm Res. 24(10):1791-802 (2007).

Ninfa AJ, Selinsky S, Perry N, Atkins S, Xiu Song Q, Mayo A, Arps D, Woolf P, Atkinson MR.    “Using two-component systems and other bacterial regulatory factors for the fabrication of synthetic genetic devices.”  Methods Enzymol. 422:488-512. (2007)

Xiang, Z, Minter RM, Bi X, Woolf P., He, Y, “MiniTUBA: medical inference by network integration of temporal data using Bayesian analysis.” Bioinformatics, 23(18):2423-32 (2007)

Kainkaryam, RM, Woolf, PJ. “poolHITS: a shifted transversal design based pooling strategy for high-throughput drug screening.” BMC Bioinformatics 9:256 (2008).

Shah, A, Woolf PJ “Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data” J. Machine Learning Research 9 (2008)

Courses Taught at Michigan

ChE Undergraduate Courses

ChemE 230 Materials and Energy Balance
(as a graduate student instructor)

Other Courses Taught (at MIT)

Undergraduate/Graduate Course

Computational and Systems Biology, Winter 2004
(co-instructor)