1. A Biology Primer Part IV: Gene networks and systems biology Vasileios Hatzivassiloglou University of Texas at Dallas

2. Protein-protein interactions Responsible for much of what happens in the cell Activation / inhibition Cleavage Phosphorylation, acetylation, methylation Identifying such interactions a major topic for bioinformatics

3. Cell memory The levels of proteins that are responsible for transcription and regulation specify the current state of the cell Levels change with time (as the cell and organism develop, and in response to the environment)

4. Gene regulatory networks A representation of the multiple interactions between genes and proteins Input: Levels of proteins that activate/repress expression Output: Gene expression Nodes: Individual genes that can be turned “on” or “off”

5. Boolean GRN models Basic model is a directed graph A node is either on or off; for genes, “on” means being expressed; for inputs/outputs, “on” means substance is present A link indicates that something can cause (or stop) an activation Time is modeled in discrete steps A boolean function is associated with each node A biological function is associated with each link

6. A simple network: Enzyme catalysis

7. Enzyme Commission numbers Specify an enzyme-catalyzed reaction Hierarchical classification of reactions –3: Hydrolase (breaks up other molecules with water) –3.4: Hydrolase acting on peptide bonds –3.4.11: Peptide bond hydrolases that act on the amino-terminal –3.4.11.4: As above but only on tripeptide bonds

8. Metabolic pathway

9. Methionine biosynthesis in E. Coli

10. Signal transduction

11. Lipid metabolism in humans

12. Other GRN models Petri nets –model the non-deterministic flow of tokens Bayesian networks –introduce probabilities for each transition Systems of differential equations –quantitative models of transitions rather than just on/off

13. Functional analysis At the forefront of modern molecular biology (systems biology) Goal: Induce network topology and actions from experimental data Massive data from –DNA microarrays –Text

14. Text Mining for Systems Biology Lots of data available in text form, written in journal articles for humans GeneWays system built over 8 years at Columbia, UTD, Berkeley, Yale, NIH Data collected from 46 online journals –~150,000 articles –~3,000,000 instances of molecular interactions –~1,400,000 unique interactions

15. System Tasks Recognize entities (genes, proteins, RNA, small molecules) –classify them –identify synonyms and abbreviations Learn possible interactions Apply learned patterns to recognize interactions Maintain and update database Construct and visualize the network

16. Extraction example Mediation of sonic hedgehog-induced expression of Coup-Tfii by a phosphatase

17. Extraction example Mediation of sonic hedgehog-induced expression of Coup-Tfii by a phosphatase Mediation of - induced expression of by a [action, promote,, [action, activate,, [action, express, ]]]

18. Search through the database: COLLAGEN

19. Filter: statements repeated at least 15 times 9 substances, 12 interactions

20. Filter: 10 25 interactions

21. Filter: 5 74

22. Filter: 1 1335

24. Details: clicking on interaction