1. Ritesh Krishna Department Of Computer Science WPCCS July 1, 2008

2. Why should you listen to my talk ? System Biology is everybody’s playground in this room – Image processing, Algorithms, Parallel processing etc. Importance of System Biology in today’s context – Agriculture Energy sources (Bio Fuels) Gene Therapy Waste clean-up

3. Use of Computational Techniques Massive data generated by molecular biology experiments Need to analyse outputs files produced in various formats, facilitate storage of bulk data, quick and precise retrieval, and most importantly understanding the behaviour and pattern in the data

4. How are these experiments performed Major revolution in the world of molecular biology No limitation of one gene in one experiment Possible to monitor expression levels of thousands of genes simultaneously

5. An example - Arabidopsis Thaliana Popular in plant biology as a model plant One of the smallest plant genome First plant genome to be sequenced Present Study The present study is about understanding leaf senescence process in Arabidopsis. Senescence refers to the biological processes of a living organism approaching an advanced age, caused due to age and stress in plant It is a programmed event responding to a wide range of external and internal signals and is controlled in a tightly regulated manner by different genes and proteins..

6. Experimental Design

7. Issues with data Biological variations vs. Technical variations Technical variations – Sample bias, Dye bias, Slide bias, Experimental conditions variations, Scanning and Imaging errors, Human errors Massive dataset with ~31,000 genes Goal is to understand functioning of certain sets of genes (needle in the haystack)

8. Step one – Clean the raw data using Normalization To assess different sources of technical biases To remove the correlations between replicates to make them independent from each other Fitting a multivariate error model - Normal distribution with mean zero and constant variance for the residuals associated with genes Propose statistical tests for evaluating the effects of normalization

9. Step two - Clustering Reduce the data dimension Similar genes sit in the same cluster.

10. Step three – Causal Network inference

11. ELF4 TOC1 CCA1LFY Circadian Circuit

12. ERS2 ETR2ETR1 CTR1 ERF1 EIN3 EIL3 EIL4 EIL5 EIN2 EIL1 EIL2 EIN4 PDF 1.2 EIN6 ERS1

13. More information…. Affymetrix Inc. (http://www.affymetrix.com/index.affx)http://www.affymetrix.com/index.affx Agilent Technologies (http://www.chem.agilent.com)http://www.chem.agilent.com Microarray Analysis, Gibson G (2003) Microarray Analysis. PLoS Biol 1(1): e15