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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

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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 predictoutput of the network given some input

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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

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Evolution of networks through:

* Adding new nodes to an network

* Addition/loss of connections

* Higher-order rewiring

How do networks evolve?

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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

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Protein-protein interaction (ppi) networks

Data can be collected in several ways:

From Ho et al. Nature 2002

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

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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) networksGoal is to capture every ppi in the cell

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Currently, there are several major issues with ppi

* Only partial data some interactions hard to measure

* Often noisydifferent types of noise inherent to different approaches

* Affected (sometimes) by high false-positive interactions

* So far collected only under standard conditionslikely to be many condition-specific interactions

Still relatively low overlap between different ppi datasets

Most reliable data: that observed in >1 study

Protein-protein interaction (ppi) networksGoal is to capture every ppi in the cell

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Conservation of ppi’s across species

‘interlogs’ (M. Vidal): conserved protein-protein interaction pair

Matthews et al. Gen Res 2001. 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, 19 - 31% 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.

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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 wasan artifact of some of the datasets

- compiled 7 different yeast largescale datasets

- argue that affinity purification = more artifactual ppi’s measured, specificallyfor abundant proteins

- after controlling for this, the remaining partial correlation explained by protein abundance.

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Genetic interaction networksSynthetic genetic (epistatic) interactions for double-gene knock outs:

Gene 1 knock-out: no phenotypeGene 2 knock-out: no phenotypeGene 1 & 2 knocked out: sickly

Negative interaction: double knockout phenotype worse than singles

Gene 1 knock-out: sicklyGene 2 knock-out: no phenotype or sicklyGene 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

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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 ratenot 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

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Nevan Krogan E-maps (epistatic interactions between pairs of gene xo’s)

Science 2008

550 genes, 118,000 different gene-gene knockouts, focusing on chromatin/nuclear

* Matches a similar network designed in S. cerevisiae

15 - 30% 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

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Roguev et al. 2008

Several networks appear to have evolved significantly

MSC1

Sz. pombe -specificparalog of SWR-CRPD3L MED.

WHY?1. Could be subfunctionalization in Sz. pombe by SWR-C paralog MSC12. Could be compensation in S. cerevevisiae for loss of RNAi3. Could be missed interactions (different environment, etc)

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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?