Course Topics

The qbSS17 will involve instruction in four overlapping courses ranging from inference of stochastic models from single-cell measurements to analysis of complexity in synthetic biological systems and to the modeling of dynamics in cancer. 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
  • 8 career oriented discussion panels on topics ranging from forming interdisciplinary collaborations to finding postdoctoral opportunities
Stochastic Gene Regulation Cell Signaling Cancer Dynamics Computational Synthetic Biology: Cells, Communities and Living Matter

Stochastic Gene Regulation

This series of lectures and projects will be held at the new Fort Collins Colorado campus. We will explore stochasticity and cell-to-cell variability in the measurement and modeling of biochemical systems. In particular, we will concentrate on the effects that small numbers of important molecules (i.e. genes, RNA, and protein) have on the dynamics of living cells. 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 study how different cellular mechanisms control and/or exploit randomness in order 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 a number of instructor-suggested group projects, in which students will apply various numerical techniques to formulate, identify and solve stochastic models for gene regulatory systems. Students will then apply these tools to model experimental flow cytometry or other single-cell data. Access and knowledge of Matlab will be helpful, but is not strictly necessary.

This section of the summer school will be co-organized by Brian Munsky, Doug Shepherd, Sabrina Spencer. Please address all questions about this section of the summer school to an organizer.

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Cell Signaling

This series of lectures will be focused on modeling cell signaling. We will begin with a brief overview of the hallmark features of cell signaling systems. We will then discuss how these features complicate efforts to develop predictive mechanistic models of cell signaling systems. We then introduce the rule-based modeling approach. We will cover simulation methods, sensitivity analysis, and parameter identification. We will make use of software tools that are compatible with the BioNetGen language (BNGL). An example of such a tool is BioNetGen (http://bionetgen.org). For additional information, contact Bill Hlavacek.

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Cancer Dynamics

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 abstracts 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!

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Computational Synthetic Biology: Cells, Communities and Living Matter

Synthetic Biology deals with the engineering of biological processes to perform specific tasks. What makes engineering in biology different from engineering in the inanimate world is what’s increasingly referred to as ‘complexity’. Complexity means many things, but for us it will mean that biological organisms are characterized by hierarchies of self-assembled networks. These are networks of interacting molecules at the level of the cell, interacting molecules and cells at the level of tissues, interacting organs at the level of the organisms and finally interacting organisms at the level of eco-systems. These interactions are enormous in number, are organized in space and time and are typically non-linear, making the task of understanding them in full generality currently impossible. However what we can do is to understand fundamental properties of simpler interacting non-linear systems, and use that to help us figure out specific questions about cellular, organismal or eco-system responses. For example, bacterial and mammalian cells undergo chemotaxis. But chemotaxis involves some rudimentary information processing, and a cellular “decision”. How do biological networks process information and how does that information processing lead to a cellular decision is the kind of question that we ask.

In the first part of the course, we will look at small systems and try to understand the consequences of nonlinearity. What we’ll find is that nonlinear biochemical networks display fascinating and non-trivial behavior that has important biological consequences. We will explore some of these consequences, at different scales. We will also look at some exciting engineering applications in the field of synthetic biology.

In the second part of the course, we will ask how we can use biological data to determine the structure and properties of biological networks. We will explore the dynamics of genetic transcription and translation, emphasizing inference of the network of interacting genetic factors believed to be responsible for controlling timing of a wide-array of biological processes (such as cellular cycles and circadian rhythms) in a variety of organisms. We introduce tools designed to use time-course gene expression data to aid in the discovery of both the key participants in the network and the functional relationships between them.

The course organizers are Ashok Prasad, Patrick Shipman, and Lev Tsimring. We are really excited to be organizing this school. Please address all questions to an organizer.

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Course Projects

The 2017 qbSS will include a strong emphasis on course projects. A list of possible mentors and project themes will made be available at the time of application selection. Students are encouraged to request specific project mentor from the list of organizers in their application statement. Some mentors may be willing to accommodate visiting students prior to, or following, the summer school in order to engage in more detailed computational, theoretical or experimental work. Such arrangements are on a one-by-one basis and should be requested well in advance of the summer school. Please contact individual organizers, lecturers, or mentors to discuss these possibilities.