Difference between revisions of "ARL/ICB Crash Course in Systems Biology, August 2010"

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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.   
 
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)'''
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''' Lecture 8: Biological variability and model uncertainty: issues for stem cell expansion and therapy development (Camilla Luni, UCSB)'''
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• Dissecting cell population heterogeneity
 +
• Case study: input dynamics affects population heterogeneity during stem cell expansion
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• Developing multi-drug therapies from ODE models in presence of uncertainty (patient-patient variability, dosage uncertainty, measurement uncertainty ...)
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• Case study: adipocyte cell response to insulin
  
 
''' Lecture 9: Biofuels (Adam Arkin, LBNL)'''
 
''' Lecture 9: Biofuels (Adam Arkin, LBNL)'''

Revision as of 23:15, 19 July 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

8:00 am   Registration open
8:45 am   Welcome and introductions (Ed Perkins)
9:00 am   Session 1: Modeling and Analysis using Differential Equations
12:30 pm   Lunch
2:00 pm   Session 2: Stochastic Modeling and Simulation
5:30 pm   Adjourn

Tuesday, 10 Aug

9:00 am   Session 3: Data Acquisition and Analysis
12:30 pm   Lunch
2:00 pm   Session 4: Applications
5:30 pm   Adjourn

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: In this session, we will discuss various stochastic simulation methods in details. The latter half of the session focuses on StochKit, a software package for simulating stochastic models. We will give a comprehensive review of the available algorithms and illustrate how to use Matlab functions in StochKit to process output files.

Lecture 3: Discrete stochastic simulation algorithms (Linda Petzold, UCSB)

Reading list:

Lecture 4: Stochkit (Min Roh, UCSB)

  • Presentation on StochKit
    • available stochastic solvers
    • creating a model
    • SBML conversion
    • output processing
    • examples

Reading list:

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: Biological variability and model uncertainty: issues for stem cell expansion and therapy development (Camilla Luni, UCSB)

• Dissecting cell population heterogeneity • Case study: input dynamics affects population heterogeneity during stem cell expansion • Developing multi-drug therapies from ODE models in presence of uncertainty (patient-patient variability, dosage uncertainty, measurement uncertainty ...) • Case study: adipocyte cell response to insulin

Lecture 9: Biofuels (Adam Arkin, LBNL)