1 Having genome data allows collection of other ‘omic’ datasets Systems biology takes a different perspective on the entire dataset, often from a Network Perspective Networks consist of nodes (entities) and interactions between nodes

2 Having genome data allows collection of other ‘omic’ datasets Systems biology takes a different perspective on the entire dataset, often from a Network Perspective Ongoing questions in Systems Biology: Types of network structures and their properties Effects of positive/negative feedback, feed-forward Dynamics of signal processing through network Insulation of signal through the network Ultimately, using information to predict output of the network given some input

3 Certain network features are of interest Connectivity (degree): Number of connections Centrality (betweenness): How central a node is Assortativity: Density of a node neighborhood Distance: shortest path between 2 nodes Average Distance : average between all node pairs Node: entity (protein, gene, metabolite) Edge: connection (physical, genetic) between entities DAG: Directed Acyclic Graph

4 Data can be collected in several ways: Large-scale yeast two-hybrid assays (in vivo in yeast) Fuse bait to DNA binding domain of TF Co-express in yeast: library of proteins fused to activation domain of TF Reporter (often drug resistance gene) only expressed if BD and AD are brought together through ppi Protein-protein interaction (ppi) networks Goal is to capture every ppi in the cell

5 Protein-protein interaction (ppi) networks Data can be collected in several ways: Goal is to capture every ppi in the cell Bait immunoprecipitation + tandem mass spectrometry (MS/MS) high throughput bait pull downs and tons of MS/MS

6 Protein-protein interaction (ppi) networks Data can be collected in several ways: From Ho et al. Nature 2002 arrow indicates bait to target blue = previously known, red = novel this study Goal is to capture every ppi in the cell Bait immunoprecipitation + tandem mass spectrometry (MS/MS) high throughput bait pull downs and tons of MS/MS

7 Currently, there are several major issues with ppi * Only partial data: False Negatives (missed interactions) some interactions hard to measure * Often noisy: False positives (incorrect interactions) different types of noise inherent to different approaches * Affected (sometimes) by high false-positive interactions * So far mostly collected under standard growth conditions likely to be many condition-specific interactions & ‘rewiring’  Still relatively low overlap between different ppi datasets  Most reliable data: that observed in >1 study Protein-protein interaction (ppi) networks Goal is to capture every ppi in the cell

8 Evolution of networks through: * Adding new nodes to an network * Addition/loss of connections * Higher-order rewiring How do networks evolve?

9 Conservation of ppi’s across species ‘interlogs’ (M. Vidal): conserved protein-protein interaction pair Matthews et al. Gen Res Tested Y2H interactions in worm ‘interlogs’ - only 25% of previously shown Y2H ppi could be verified in yeast! - 6/19 (31%) were conserved ppi - another assessment found 19% of ppi were conserved so, % of ppi were conserved between yeast and C. elegans Other methods emerging to compare networks in a more complex way … but it’s challenging due to partial/noisy networks.

10 Do ppi’s constrain protein evolution? Fraser et al. Science 2001: significant correlation between rate of protein evolution and connectivity (# ppi) reported slower evolution rates for proteins with lots of contacts But other studies reported no significant correlation … Bloom & Adami. BMC Evo Biol. 2003: Reason for Fraser correlation was an artifact of some of the datasets - compiled 7 different yeast largescale datasets - argue that affinity purification = more artifactual ppi’s measured, specifically for abundant proteins - after controlling for this, the remaining partial correlation explained by protein abundance.

11 Genetic interaction networks Synthetic genetic (epistatic) interactions for double-gene knock outs: Gene 1 knock-out: no phenotype Gene 2 knock-out: no phenotype Gene 1 & 2 knocked out: sickly Negative interaction: double knockout phenotype worse than singles Gene 1 knock-out: sickly Gene 2 knock-out: no phenotype or sickly Gene 1 & 2 knocked out: less sickly Positive interaction: double knockout phenotype improves over singles Generally more (>2X in yeast) negative than positive interactions detected in a single species

12 Nat Gen 2008 Identified synthetic lethal (extreme negative) genetic interactions in S. cerevisiae Only 6 (0.7%) of pairs were synthetic lethal in C. elegans Adjust to ~5% given error rate not explained by paralogy, as these are all 1:1 orthologs Compared to >60% essentiality conserved across species (individual essential genes) >30% protein-protein interactions conserved across species Then used RNAi to knock down 837 pairs of orthologs in C. elegans

13 Nevan Krogan E-maps (epistatic interactions between pairs of gene xo’s) Science genes, 118,000 different gene-gene knockouts, focusing on chromatin/nuclear * Matches a similar network designed in S. cerevisiae % of negative interactions were conserved between species (>500 my) more than C. elegans-yeast comparison by Tischler et al. >50% of positive interactions were conserved

Much higher conservation of genetic interactions if only look at interacting proteins

15 Roguev et al Several networks appear to have evolved significantly MSC1 Sz. pombe -specific paralog of SWR-C RPD3LMED. WHY? 1. Could be subfunctionalization in Sz. pombe by SWR-C paralog MSC1 2. Could be compensation in S. cerevevisiae for loss of RNAi 3. Could be missed interactions (different environment, etc)

16 Many remaining questions … * What types of protein-protein interactions are most conserved and why? * What types of networks are more constrained and why? specific functions, structures, features more constrained? * What processes allow/promote network ‘rewiring’? * What effect do network interactions have on protein evolution rates? * How to ppi networks vary across environmental space and time?

Can also look at evolution of protein modification: phophorylation, acetylation, ubiquitination, glycosylation, etc ATP Kinase Protein target P IMAC: metal affinity purification: recovers phospho-peptides

Can also look at evolution of protein modification: phophorylation, acetylation, ubiquitination, glycosylation, etc Protein target Acetyl Immunoprecipitation to recovery modified proteins