ISCB Overton Prize Lecture: Trey Ideker, University of California, San Diego
Introduction to Trey (before he starts his talk): Received his PhD working with Leroy Hood in 2001. The curse of systems biology: you will be a jack of all trades, rather than a master of one. On to the talk.
Also worked with Richard Karp. Big question: How does one automatically assemble pathways? Design new perturbations to maximize information gain (this is what he did for his PhD). Ideker et al.: Ann Rev Genomics Hum Genetics 2001 – his PhD work (Systems Biology: A new approach to decoding life).
Let’s think about all the public interaction data: protein-dna interactions, PPIs, biochemical reactions. (Ideker et al Science 2001). The final figure of that Science manuscript, he feels, launched his career.
Querying biological networks for “Active Modules”, where you can paint the network with colors: for patient expression profile, protein states, any functional assay. This highlights the Interaction Database Dump, aka “Hairballs”, which aren’t good for a whole lot. (Ideker Bioinformatics 2002). In recent work with Chanda and Bandyopadhyay, he’s worked on Project siRNA pheotypes onto a network of Human-human and human-HIV protein interactions. Look at the network modules associated with infection (Konig et al. Cel 2008).
Next: Moving Network Biology into the Clinic: the working map. Importantly, this map doesn’t have to be complete, and there can be some toleration for FP and FN. Their research wants to move from network assembly from genome-scale data to network-based study of disease. From this map, you could get: network-based diagnoses, functional separation of disease gene families, moving from GWAS to network-wide PAS (Pathway AS). Input is: network evolutionary comparison/cross-species alignment to identify conserved modules, projection of molecular profiles on protein networks to reveal active modules, integration of transcriptional interactions with causal or functional links, etc. These working maps are still essentially hairballs, even if they are represented as pretty pictures. But isn’t the cell really a hairball inside anyway? Maybe the secret isn’t figuring out this thing – maybe it’s to use this thing.
Extracting conserved pathways and complexes from cross-species network comparison (with Sharan and Karp): PathBLAST and NetworkBLAST for cross-comparison of networks. Start with two large hairballs; next realize that there is a third network implicit there of protein sequence homologies/orthologies between the two networks; given it is a many-to-many relationships between the networks, find the particular one that is the maximum alignment; highly score dense conserved complexes; then look for conserved interactions and find matched protein pairs (he does use sequence similarity for some things); the interaction scores come from logistic regression on number of observations, expression correlation, and clustering coefficient. They applied it for Plasmodium and Sarcchomyces.
Also did work on Human vs mouse TF-TF networks in brain (Tim Ravasi). You combine these quite readily, and id2, rb1 and cepbd are some examples. What follows is a very nice slide on the timeline of both biological sequence comparison and biological network comparison (Sharan & Ideker. Nat Biotech 2006). Trey thinks there are better things out there now than PathBLAST and networkBLAST.
Genetic interactions (non-physical) form a distinct type of network map (Tong et al. Science 2001). Here, there exists a genetic interaction between gene A and B if phenotype of mutant a is OK, mutant b is OK, and mutant ab is sick. How can you compare these to physical networks? Kelley and Ideker Nat Biotech 2005 worked on systematic identification of parallel pathway relations. Genetic interactions run between clusters of physical interactions, not within them.
Functional maps of protein complexes (Bandyopadhyay et al. PLoS Comp Bio 2008). (Roguev, Science 2008) Genetic interaction maps are conserved between species (S cerevisiae, S pombe) (Thanks to Oliver for that article – I missed it on the slide).
Using ChIP-chip to assemble transcriptional networks underlying genotoxicity (Craig Mak and Chris Workman), and doing network comparison. Firstly, integrate cause-and-effect interactions with physical networks (Yeang, Mak et al. Genome Biology 2005). What if a lot of transcriptional binding is real but inconsequential to cellular function? They’d try to systematically functionally validate all the ChIP-chip data they generated. Workman, Mak et al. Science 2006. Recent extensions to this work: Mak et al. Genome Research 2009. Here, about 10% of TF show an interesting spatial distribution on the genome. Characterize based on the distance to the closest telomere for a given gene. Then characterize a TF by looking at distribution of distances of each one to its chromosome end. There does seem to be condition-specific behaviour: probably it isn’t the TF moving from one part of chromosome to another, but perhaps the genome is moving to and fro around them.
Network-based disease diagnosis. Much work is increasingly moving in this direction. Using protein networks to diagnose breast cancer metastasis. Breast cancers are very heterogeneous. Can we improve the work in terms of reproducibility and classification using further interaction information? If each patient has a mutation in a different gene, what do we do? What if these genes are sequential steps in a pathway, or are subunits in a common complex? Might you then be able to learn a rule for this? Nature Biotech 2009 Taylor et al.
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!