This Core's agenda is to build a Cancer Biology Systems Science (CBSS) curriculum, with implications for the nature and scope of biology related to cancer and the application of a systems research approach from engineering and mathematics. Based on groundbreaking efforts at UCB and UCSF, we propose to design a sequence of undergraduate and graduate level courses in CBSS. These courses will be introduced through UCB's Bioengineering program (see Letter of Support from Dr. Tirrell), the graduate program of which is jointly administered by UCB and UCSF. The courses will be open to students from both campuses and will be made widely available to encourage critical discussion and facilitate adoption. In addition, new undergraduate students (usually juniors) from minority institutions will be recruited through the SUPERB program at UCB. Undergraduate curriculum development. UCB has begun a drastic revision of its undergraduate systems curriculum in Engineering, with new courses that focus on methods to model and analyze complex systems (combining differential equation modeling with that of discrete event systems), and new courses that emphasize a computational view of systems. Also, in conjunction with UCB's campus-wide Designated Emphasis in Computational and Genomic Biology, undergraduates in any discipline can take a wide array of courses covering computational methods, algorithm design, and statistics in molecular biology and genomics. A relatively new course in UCB's Bioengineering department, Frontiers in Microbial Systems Biology (which is offered at both the undergraduate and graduate levels), introduces students to the basic modeling and analysis methods for network discovery and dynamic model design, focusing on two model systems, the chemotaxis network and Lambda bacteriophage infection. To complement these offerings, and to provide an important example of a concrete system for our undergraduates studying systems theory, we will develop a """"""""mezzaninelevel"""""""" (upper year undergraduate, first year graduate) project course called """"""""Modeling cancer pathways"""""""". Each semester, a different signaling network of a pathway related to cancer will be chosen as a focus for the whole class. Students would work in groups on projects related to the development of dynamic models, analysis results, and identification of new parts of the network from data, and developing an understanding of the cancer pathway. We will use this course as an opportunity to bring in new research results from our HER, AKT, MEK and ERK projects that the students may use. We propose to introduce the course initially as a UCB/UCSF Bioengineering course, crosslisted in Electrical Engineering and Computer Sciences, Mechanical Engineering, and Civil and Environmental Engineering, and open to any student on campus. The course will be self-contained: the tools that will be used in the class, such as Bayesian analysis and modeling, statistical association models, and differential equation models, and the assumptions that one makes in using these tools, will be presented in the first several weeks of the class, followed by lectures focusing on explanations of what is already known or hypothesized about the system under study. The course will be developed and initially taught by Tomlin and Spellman, in conjunction with project personnel, and we will work with other faculty who have expressed interest in such a course, such as Professors Adam Arkin and Jan Liphardt, who has proposed a complementary course entitled Cancer Biology for the Physical Scientist. Initially, enrollment will be limited to 40 students, though based on undergraduate systems course offerings and initial feedback, demand is expected to be higher than this. The course materials and lectures will be available on the web.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
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Oregon Health and Science University
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Risom, Tyler; Langer, Ellen M; Chapman, Margaret P et al. (2018) Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun 9:3815
Gast, Charles E; Silk, Alain D; Zarour, Luai et al. (2018) Cell fusion potentiates tumor heterogeneity and reveals circulating hybrid cells that correlate with stage and survival. Sci Adv 4:eaat7828
Xu, Xiaowei; De Angelis, Carmine; Burke, Kathleen A et al. (2017) HER2 Reactivation through Acquisition of the HER2 L755S Mutation as a Mechanism of Acquired Resistance to HER2-targeted Therapy in HER2+ Breast Cancer. Clin Cancer Res 23:5123-5134
Hill, Steven M; Nesser, Nicole K; Johnson-Camacho, Katie et al. (2017) Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling. Cell Syst 4:73-83.e10
Seviour, E G; Sehgal, V; Mishra, D et al. (2017) Targeting KRas-dependent tumour growth, circulating tumour cells and metastasis in vivo by clinically significant miR-193a-3p. Oncogene 36:1339-1350
Riesco, Adrián; Santos-Buitrago, Beatriz; De Las Rivas, Javier et al. (2017) Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search. Biomed Res Int 2017:1809513
Hassan, Saima; Esch, Amanda; Liby, Tiera et al. (2017) Pathway-Enriched Gene Signature Associated with 53BP1 Response to PARP Inhibition in Triple-Negative Breast Cancer. Mol Cancer Ther 16:2892-2901
Sears, Rosalie; Gray, Joe W (2017) Epigenomic Inactivation of RasGAPs Activates RAS Signaling in a Subset of Luminal B Breast Cancers. Cancer Discov 7:131-133
Gendelman, Rina; Xing, Heming; Mirzoeva, Olga K et al. (2017) Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells. Cancer Res 77:1575-1585
Hafner, Marc; Heiser, Laura M; Williams, Elizabeth H et al. (2017) Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics. Sci Data 4:170166

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