Computational Systems Biology

Computational Systems Biology - BME 211

The course covers:

  • Algorithms to integrate high-throughput genomics datasets.
  • Probablistic graphical models to infer gene activity in normal and diseased cell states.
  • Incorporating gene regulatory networks into machine-learning approaches.
  • Review and participation in DREAM challenges to learn about (and contribute!) winning solutions to open molecular biology problems.

Students are graded on:

  • 2 written assignments
  • 1 oral team presentation covering a winning method in one of the DREAM competitions
  • 1 oral team presentation covering the design and application of their own method to perform in a DREAM competition.

Testing equation writing in this wiki:

  • Message from a variable v to a factor a is the product of all messages from other factors b:
    • $$\mu_{v \rightarrow a}(x_v)=\prod_{b \in N(v) \setminus a}{\mu_{b \rightarrow v}(x_v)}$$
  • Message from factor a to variable v:
    • $$\mu_{a \rightarrow v}=\sum_{u \in N(a) \setminus v}{ P(x_v,x_u)*\mu_{u \rightarrow a} } $$

 

Instructors and Assistants

Class Web Page