Course Structure

The key elements of the 3-week, intensive Cancer Systems Biology Course:

  1. Initial preparatory, 1-week long, workshops in Mathematical & Computational Modeling and in Foundational Paradigms in Oncology.  While these workshops are optional students will be encouraged to attend to receive individual attention in learning valuable background material.
  2. A two-week core course involving both lecture and laboratory modules.

Throughout the class, there will be activities  focused on teaching collaborative skills, including practical exercises and assessment

The Prep Week

About 2 weeks before the start of the course, participants will receive a pre-course survey (to assess baseline knowledge and student expectations for the course and their careers); a reading list; licensed copies of Mathematica and MatLab software, along with installation instructions for additional open-source tools; and access to advanced training sets available via the course website. Upon arrival at UCI, they begin “preparatory” workshops, the goal of which is to bring participants “up to speed” in mathematics, statistics and computation—through lectures and hands-on tutorials that introduce concepts and important software tools—or fundaments in cancer biology, where lectures and hands-on laboratory exercises emphasize key concepts. While the prep week is optional, we encourage all participants to attend. Participants will be assigned to one of the two prep-week courses according to their backgrounds.

The Mathematical & Computational Modeling Workshop is suited for cancer biologists and biomedical researchers who need foundational knowledge in mathematics and computation needed for effective multidisciplinary collaboration. The workshop will cover reviews of essential mathematics (calculus, linear algebra), basic mathematical and computational modeling, biostatistics and informatics and on developing facility with commonly used software (Matlab, Mathematica, R) that will be exploited in the rest of the course. Morning lectures will be supplemented by afternoon tutorials. Afternoon sessions will consist of hands-on theoretical and computational exercises. Tutorials will be run by a faculty lecturer with the aid of graduate student assistants.

The Foundational Paradigms in Oncology Preparatory Workshop is aimed at non-biologists (e.g., mathematicians, statisticians, computer scientists, bioengineers etc.) who are eager to participate in rigorous cancer research but whose training has been deficient in essential foundational knowledge required for effective collaborative interactions. Participants will review foundational concepts in the etiology and pathology of cancers, core mechanisms underlying oncogenesis at the molecular, cellular and tissue level, human tumor histopathology, epidemiology and clinical interventions. Morning lectures will be supplemented by afternoon laboratory modules. Afternoon sessions will consist of hands-on laboratory exercises and demonstrations covering basic quantitative molecular biology protocols and tools, e.g QRTPCR quantitation of p21/p53 expression in fibroblast cultures after UV exposure, tissue culture systems (including growth factor dependent and 3D systems) and surrogate in vivo tumor model systems, e.g. perturbations of chick growth and angiogenesis by angiostatins/VEGF. Participants will have the opportunity to visit campus cancer research labs and medical school clinics to increase appreciation and connection to the lecture material.

Mathematical/Computational Modeling Workshop
  AM PM
Mon

Systems Biology: From Data to Models and Back

Calculus and Linear Algebra Review

Tutorials (Mathematica-based)
Tues Calculus Review II Tutorials (Mathematica-based)
Wed Modeling with Mathematica and Matlab Tutorials (Matlab-based)
Thurs Partial Differential Equations Tutorials (Matlab based)
Fri Biomedical statistics & fundamentals of informatics Tutorials (Matlab and R-based)
Sat Big data analysis & machine learning
Tutorials (Matlab and R-based)
Fundamentals in Cancer Biology and Biomedicine Preparatory Workshop
  AM PM
Mon Intro to Cancer Biology— cancer etiology and pathology Practical introduction to Quantitative Molecular Biology/Omics. Part 1.
Tues Oncogenesis: molecular mechanisms Practical introduction to Quantitative Molecular Biology/Omics. Part 2.
Wed Oncogenesis: cellular/tissue processes In vitro/vivo experimental models of angiogenesis
Thurs Tumor Histopathology and Imaging Histopathology /Imaging
Fri Therapeutic interventions Site visit to clinical facilities
Sat Cancer Epidemiology Lab/tutorial

The Core Course

The content of the next two weeks, the “core course”, is organized around selected topics organized around three major themes in Cancer Biology — oncogenesis and regulation of growth control, genetic and non-genetic heterogeneity, and the spatiotemporal dynamics of signaling between tumor cells and cells in the microenvironment—and will introduce a variety of cutting edge approaches, including genomic and imaging-based big data methods. Most lectures will take place in the morning and be immediately followed by lunchtime discussions with the lecturers. Participants will spend most afternoons performing a series of lab/tutorial modules, which will emphasize both how models are derived from (and tested by) data, and how the analysis of models guides the generation of hypotheses and the acquisition of data.

Student learning in labs and tutorials will be facilitated by tutors—typically project scientists or graduate students from the Mathematical, Computational and Systems Biology Interdisciplinary Program at UC Irvine—who will provide on-the-spot personal assistance and answers to questions in addition to the course faculty/instructors. Evenings are generally unscheduled to leave time for participants to continue lab projects or for lectures or tutorials that may be added ad hoc, in response to participant requests or needs.

A sample two-week curriculum is summarized below. Superscripts following the lecture titles indicate which of the central themes of the course each lecture relates to: 1Systems analyses of growth control/oncogenesis, 2Cancer cell heterogeneity, 3Spatiotemporal dynamics of tumors and their microenvironments. Five laboratory modules will be chosen among six lab modules (one module will be rotated out of the course from year-to-year) and are described below, after the sample curriculum. Labs will be supported by graduate student assistants at a high ratio (typically 6:1). The actual curriculum may differ due to scheduling issues, and will be updated on this website as changes are made.  If you have questions about whether specific lecture or laboratory topics will be offered, feel free to contact us.

Week 2 AM PM
Mon

Systems Biology: A Revolution in Progress (Lander)1

Current Progress in Cancer Biology (Waterman) 1

*Lab Module A

 

Tues

Cancer Signaling Modeling 1

Principles of Growth Control 1

*Lab Module A, cont’d.
Wed

Cancer Cell Lineages 1, 3

Modeling Lineages 1

*Lab Module A, cont’d.
Thurs

Genomics and Networks 1

Statistical Methods for Cancer Omics 2

*Lab Module B 
Fri

Single-Cell & Network Analysis 2

Cancer Cell Heterogeneity 2

*Lab Module B, cont’d.
Sat

Mutation Accumulation Analyses (Nie) 1,2,3

Advanced Imaging Techniques 3

 

*Lab Module C 
Sun  Free day

 

Week 3 AM PM
Mon

Cancer Metabolism 3

Reaction-diffusion Systems 1,3

*Lab Module D
Tues

Mathematical/computational models of Tumor Spatial Dynamics 1-3

Metastasis 2

*Lab Module D, cont’d.
Wed

Mathematical/computational models of Metastasis 2,3

Principles of Evolution 1,2,3

*Lab Module E
Thurs

Evolution and Cancer 1,2,3

Cancer Therapeutics 1,3

*Lab Module E, cont’d.
Fri

Tumor Resistance 2,3

Future challenges 1,2,3

 

Final Review
Sat Southern California Regional Systems Biology Conference (February 9th 2019)

*Laboratory modules (tentative titles and instructors):

A. Cancer Signaling Networks (Razorenova and Bardwell)

A.1: Modeling and analysis of cancer signaling networks

A.2: Synthetic lethality in cancer therapeutics (A.2 may not necessarily be given every year)

B. Role   of  environmental   mutagenesis   in              driving   cancer:   characterization   using   duplex      DNA sequencing (Ganesan, Cinquin)

C. Imaging & Modeling of Tumor Metabolism (Gratton, Digman, Waterman, Lowengrub)

D. Single-cell analysis of intratumor heterogeneity and its role in drug resistance (Kessenbrock, Lawson, Van Etten, Nie)

E. Tumor-On-A-Chip: Laboratory experiments and mathematical modeling of In Vitro Microphysiological Tumors (Hughes, Lowengrub, Waterman)

 

Continuing post-course education support and online resource sharing

The course structure (small class size, guided group tutorials, small group/team projects, prolonged instructor interactions) and mix of participants with diverse training backgrounds and skills is a conducive environment for building meaningful connections among participants and faculty trainers. We aim to encourage these interactions to grow after the course.

To address this goal, we will support continuing alumni educational interaction through social networking opportunities. Our course website will be maintained with regular content updates: course materials, tutorials and videos, as well as interactive community features. However, because few individuals are willing to devote time to new online forums, we will establish a targeted LinkedIn user group, focused on Cancer Systems Biology.

We intend to actively engage our course alumni by opening a monthly discussion of a significant research article (chosen by members of the UCI Cancer Research Institute or teaching faculty or through member recommendation or polling). Members will be encouraged to contribute information they have found useful or of interest. To achieve critical mass in user group membership we will include UCI graduate students who served as course assistants and invite students, postdocs or researchers from related programs and institutions. We believe this will keep alumni actively engaged in the growing cancer systems biology community and provide them with continuing educational support that in time will become self-sustaining.