qbSS19: Course Topics
Gulf Coast Campus (Rice University - Houston, Texas)
The Gulf Coast Campus of qbSS 2019 will involve instruction in two overlapping tracks [Predictive Modeling of Cell Regulatory Systems and Cancer Dynamics and Evolution]. Information about specific course topics can be found below:
Each course will include:
- 10 shared 1-hour general lectures from invited speakers
- 10 shared 1.5-hour chalk talks from invited speakers
- 30 hours of in-depth instruction during breakout discussions including expert panel discussions, chalk talks, computer/experimental lab demonstrations;
- 20+ hours of mentored project work and project presentations;
- 2 catered poster sessions
- 20-24 student talks
- 4 career oriented discussion panels on topics ranging from forming interdisciplinary collaborations to finding postdoctoral opportunities
Predictive Modeling of Cell Regulatory Systems
This series of lectures and projects will explore stochasticity and cell-to-cell variability in the measurement and modeling of biochemical systems. We will discuss regulatory role of feedbacks and other non-linear elements on signal processing and then concentrate on the effects that small numbers of important molecules (i.e. genes, RNA, and proteins) have on the dynamics. We will review experimental manifestations of stochastic effects in molecular biology, as can be measured using single-cell and single-molecule techniques. We will discuss the most recent analytical and numerical methods that are used to model these systems and show how these methods can improve interpretation of experimental data. We will also discuss rule-based modeling techniques, which are needed to capture single-molecule patterns of phosphorylation and other aspects of biomolecular site dynamics. We will use such models to study how different cellular mechanisms control and/or exploit randomness to survive in uncertain environments. Similarly, we will explore how single-cell measurements of cell-to-cell variability can reveal more information about underlying cellular mechanisms.
This section of the summer school will include several instructor-suggested group projects, in which students will apply various numerical techniques to formulate, identify and solve stochastic models for cell regulatory systems. Students will then apply these tools to model experimental flow cytometry or other single-cell data. Access and knowledge of scientific computing in Matlab/Python/C/C++ will be helpful, but is not strictly necessary.
This section of the summer school will be co-organized by William Hlavacek (Los Alamos National Laboratory) and Brian Munsky (Colorado State University). Please address all questions about this section of the summer school to an organizer.
Cancer Dynamics and Evolution
In this theme we will address a number of biological and mathematical issues related to modeling of evolution of cancer. Lectures will cover topics spanning many time- and length-scales, from the fundamental issues of cell proliferation and mutation dynamics, to molecular events affecting specific pathways in cells, to population genetics effects (see the preliminary list further on). This section of the summer school will include a number of instructor-suggested group projects, in which students will apply various numerical techniques to formulate, identify and solve stochastic models of cancer evolution. Students will then apply these tools to model experimental and clinical data. This section of the summer school is organized by Marek Kimmel and Rosemary Braun -- please contact us if you have any questions!
Preliminary list of thematic threads
- Rosemary Braun (Northwestern): Seeing the Forest and the Trees: Multi-scale Approaches for Analyzing Omic Data
- Alexandra Jilkin (Notre Dame): TBA
- Marek Kimmel (Rice): Deterministic and Stochastic Dynamics of Cancer Growth
- Katherine King (Baylor College of Medicine): Inflammation and Cancer in Hematopoiesis
- Herbert Levine (Rice): Phenotypic Plasticity and Tumor Progression with a View on Understanding of the Tumor Microenvironment
- Thomas Oliver McDonald (Harvard): Mathematical Models of Molecular Evolution in Cancer
- Simon Tavaré (Cambridge, UK): Population Genetics Models in Cancer