General description

Course organized by TUT, see TUT study guide for up-to-date information.

Learning outcomes

After the course, the student can:
- Identify and define techniques used in modeling gene expression and gene regulatory networks. Demonstrate the accuracy of the models.
- Interpret data generated from the models, classify strengths and weaknesses of the modeling strategies, summarize results and explain the connection between models and native gene networks.
- Implement models, apply them to mimic experiments, and calculate statistical features associated to gene expression in cells. Apply the knowledge to construct models of engineered genetic circuits.
- Analyze results of simulations of models of gene networks. Compare different methodologies for verifying a hypothesis or measuring a variable using such models.
- Compare and appraise different computational models, and interpret conclusions using different models.
- Create and develop models of gene networks from experimental data, and use the models to address questions on the dynamics of gene networks and processes regulated by these networks, e.g., cell differentiation.

Contents

- The Stochastic Simulation Algorithm and The Delayed Stochastic Simulation Algorithm
- Modeling single gene expression with the delayed Stochastic Simulation Algorithm
- A stochastic delayed modeling strategy of Gene Regulatory Networks: models of noisy attractors as cell types, and ergodic sets
- Stochastic models of cell differentiation
- Examples and applications of the modeling strategies

Evaluation

Belongs to following study modules

2017–2018

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

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