ARL/ICB Crash Course in Systems Biology, August 2010
This course is geared toward biologists who want to become familiar with current computational biology software and capabilities, emphasizing quantitative applications for understanding and modeling complex biological systems. The course is taught by researchers from the Army Institute for Collaborative Technology and the Army Research Laboratory.
Schedule
The course will consist of four sessions, each lasting approximately 3.5 hours (including a break in the middle of the session).
Monday, 9 Aug
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Tuesday, 10 Aug
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Lecture Outline
Session 1: Modeling and Analysis using Differential Equations
Elisa and Maria: Please put together a one paragraph summary of your session (abstract-like) that describes the basic areas that you will cover, in words. A more specific listing of topics that you will cover can go below, under the individual lectures. You should update the topics that I listed based on what you will actually cover (probably a more focused list, given the time constraints) and also add any references that you think participants might find useful to read.
Lecture 1: Core Processes in Cells (Elisa Franco, Caltech) This lecture will provide an introduction to modeling of core processes in biology using differential equations. Specific topics to be covered include:
- The Cell as a Dynamical System
- Modeling Techniques: reactions, master equation, differential equations
- Transcription and Translation
- Transcriptional Regulation
- Post-Transcriptional and Post-Translational Regulation
- Cellular Subsystems (?)
Reading list:
Lecture 2: Analysis Methods (Maria Rodriguez Fernandez, UCSB)
- sensitivity analysis
- robustness analysis
- identifiability and design of experiment
- parameter identification
Reading list:
Session 2: Stochastic Modeling and Simulation
Linda and Min: Can you put together a one paragraph summary of your session (abstract-like) that describes the basic areas that you will cover, in words. A more specific listing of topics that you will cover can go below, under the individual lectures (as done above), as well as any references that you think participants might find useful to read. Also, if you would like to give some hands-on material using computers, can you let Richard know what the requirements are for software that the students would use?
Lecture 3: Discrete stochastic simulation algorithms (Linda Petzold, UCSB)
Lecture 4: Stochkit (Min Roh, UCSB)
Reading list: Stochastic modelling of gene regulatory networks StochKit Manual
Available software:
Session 3: Data Acquisition and Analysis
Bernie and Rasha: Can you put together a one paragraph summary of your session (abstract-like) that describes the basic areas that you will cover, in words. Then pick a couple of lecture titles and add a more specific listing of topics below that, as done in the first session, as well as any references that you think participants might find useful to read. Also, if there is software that can be used by participants (either during the talks or afterwards), it would be great to include a list of programs, source link, and requirements for the software.
Lecture 5: Title (Bernie Daigle, UCSB)
Reading list:
Lecture 6: Title (Rasha Hammamieh, WRAIR)
Reading list:
Session 4: Applications
Mike, Camilla and Adam: can you send put in a title and abstract for your talk (or send to Richard).
Lecture 7: Polarization in Yeast Mating (Mike Lawson, UCSB)
We have developed a spatial stochastic model of polarisome formation in mating yeast, focusing on the tight localization of proteins on the membrane. This new model is built on simple mechanistic components, but is able to achieve a highly polarized phenotype even in relatively shallow input gradients. Preliminary results highlight the need for spatial stochastic modeling because deterministic simulation fails to achieve a sharp break in symmetry.
Lecture 8: Insulin Signaling and Stem Cells (Camilla Luni, UCSB)
Lecture 9: Biofuels (Adam Arkin, LBNL)
