1. Doug Raiford Lesson 13 5/10/20151Gene networks and pathways

2.  Regulatory network  collection of genes that interact with one another in a way that governs the rates at which genes in the network expressed  Implication Networks  collection of genes whose expression levels are all affected by the same condition (drug, toxin, disease, etc.)  Pathways  Series of genes that produce enzymes that catalyze a sequential set of reactions 5/10/20152Gene networks and pathways Promoter RegionCoding regionTerminator Region RNA polymerase Start Codon ‘ATG’ = Methionine Stop Codon: non coding ‘TAA’, ‘TAG’, or ‘TGA’

3.  What do we know already?  Promoter regions  Motifs  But, only a limited number of sigma factors  Need other environmental factors to affect 5/10/20153Gene networks and pathways Promoter RegionCoding regionTerminator Region RNA polymerase Start Codon ‘ATG’ = Methionine Stop Codon: non coding ‘TAA’, ‘TAG’, or ‘TGA’

4.  Genes that catabolize the processing of lactose  Want to express these genes when…  Lactose is present  Glucose not present 5/10/20154Gene networks and pathways Operator sequence LacZLacYLacA Lactose Operon Promoter region (-10 -35)

5.  Lactose repressor protein: pLacI  pLacI can bind to operator sequence  When bound, inhibits RNA polymerase  Can also bind to lactose  When bound to lactose can’t bind to operator 5/10/20155Gene networks and pathways Operator sequence LacZLacYLacA Lactose Operon Promoter region (-10 -35) Lactose abundant  don’t repress

6.  Alone, promoter region not a good match for any sigma factor  When Cyclic-AMP receptor protein (CRP) promotes RNA polymerase binding  Can also bind to glucose  When binds to glucose doesn’t bind to promoter region 5/10/20156Gene networks and pathways Operator sequence LacZLacYLacA Lactose Operon Promoter region (-10 -35) Glucose abundant  don’t promote

7.  Infer from gene expression experiments  Change something (like reduce glucose and increase lactose)  Measure at time intervals 5/10/2015Gene networks and pathways7

8.  In previous example would detect increase in three genes  Could then target research on these genes to determine how (function) used in lactose metabolism  Implication networks also used to study diseases  Usually just a starting point 5/10/20158Gene networks and pathways Operator sequence LacZLacYLacA Lactose Operon Promoter region (-10 -35)

9.  A common next step is determining causality  For instance  If one gene is upregulated  Followed by another  Did the one cause the other? 5/10/20159Gene networks and pathways Increase in gene 1 protein product Increase in gene 2 protein product

10.  Start with a hypothesis  Do a literature review  If know what the gene is (and its function) have others noted the relationship? 5/10/201510Gene networks and pathways

11.  How determine function?  BLAST 5/10/201511Gene networks and pathways

12.  Hypothesis  There is a causal link between gene A and B  Expression of A causes expression of B  How verify? 5/10/201512Gene networks and pathways

13.  Disable gene A (Knockout)  See if B is still upregulated  Insert a sequence into gene that disables  Recombination  Recently have designed RNA that will attach to a specific gene and disable it 5/10/2015Gene networks and pathways13 Increase in gene 1 protein product gene 2 still upregulated

14. 5/10/201514Gene networks and pathways  Once causal relationship determined:  Not done  Now must figure out the why  Example: determine that pLacI can bind to both operator sequence and lactose

15.  Each step is an intermediate  Each arrow is a reaction  Proteins act as enzymes to catalyze these reactions  Gene/protein combo for each arrow  Represents a genetic “pathway” 5/10/201515Gene networks and pathways α-D-glucose-1P α-D-glucose β-D-glucose β-D-glucose-6P β-D-fructose-6P β-D-fructose-1,6P2 Glycerone-P

16.  E2.7.1.41  Phosphodismutase  Catalyze the transfer of a phosphate residue from one d-glucose 1-phosphate to another 5/10/201516Gene networks and pathways

17.  Can be quite complex  Highly conserved 5/10/201517Gene networks and pathways

18.  Directed graphs (though many reactions are bi-directional)  Nodes: reaction inputs and outputs  Edges: gene/protein enzyme  Must be able to lable  Bi-directional and directionally weighted  Sometimes a different type of node used as gene  Regulatory networks would require additional relationships 5/10/2015Gene networks and pathways18

19.  KEGG  Kyoto Encyclopedia of Genes and Genomes  EcoCyc  Encyclopedia of Escherichia coli K-12 Genes and Metabolism  BIND  Biomolecular Interaction Network Database 5/10/201519Gene networks and pathways

20.  API  Markup languages  XML interfaces 5/10/2015Gene networks and pathways20 #!/usr/bin/env perl use SOAP::Lite; $wsdl = 'http://soap.genome.jp/KEGG.wsdl'; $serv = SOAP::Lite->service($wsdl); $offset = 1; $limit = 5; $top5 = $serv->get_best_neighbors_by_gene('eco:b0002', $offset, $limit); foreach $hit (@{$top5}) { print "$hit->{genes_id1}\t$hit->{genes_id2}\t$hit->{sw_score}\n"; }

21.  Objective: build a simulation of a biological system  Predict behavior of the system given a set of conditions  Bottom up: develop system from constituent parts (molecular interactions)  Top down: develop underlying mechanisms to explain observed behaviors 5/10/2015Gene networks and pathways21

22. 5/10/201522Gene networks and pathways