1. Inferring genetic regulatory logic from expression data Prepared by Chunhui CAI

2. Logic gates  Working principles of cis-regulatory elements ‘OR’ logic ‘AND’ logic ‘NOR’ logic ‘OR-NOR’ logic

3. Bayesian network  A probabilistic model Use probability as a means to express uncertainty about modeling variables and their dependencies.

4. System and methods  The model of gene regulatory interactions Xi ~ regulator Y ~ regulatee Ii ~ intermediate variable θi ~ binding probability

5. System and methods

7.  ‘OR’ model

8. System and methods  ‘OR-NOR’ model

9. System and methods  Rules Gene expression values ~ two states  0 – not expressed  1 – expressed Type of regulation  Simultaneous  Time delay

10. Result  Regulatory interactions of 20 genes of S.cerevisiae. The full arcs represent activatory regulation, the dashed arcs represent inhibitory regulation. The relationship between genes regulating one common gene is described by ‘OR’- function.

11. Discussion  Probabilistic or deterministic  Either ~ Fitting of the expression data into state value (simply two states(0,1)?)  Or ~ Determining the activation/repression logic  (Later) How good is our clustering method (need to improve and more expression data should be involved)