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Michael Jones › Comments

HL36: Zhidong Tu - Pathway and network based approaches to prioritize reliable hits from high throughput RNAi screening experiments
He points out that there is work just published that is quite similar to their work - - Shannon McWeeney
integrative approach - see work in Drosophila BMC Genomics 2009 10:2220; siRNA and PPI - Genome Research 2009-19:1057-1067 - Shannon McWeeney
key assumption - real hits will not be randomly distributed in physical interaction network - should reside in subnetworks - Shannon McWeeney
Ranking algorithm to compare query node with rest of nodes with known hit/non-hit status (NePhe score is then summarized) - Shannon McWeeney
trade-off between quality of hits and algorithm choice - Shannon McWeeney
Assumption that hit is not randomly distributed assumes that RNAi is hitting its Target. Results from random naming seems to validate that this assumption is reasonable. - Michael Jones from Android
Keynote: Chris Sander - Systems Biology of Cancer Cells
An interview with Chris Sander ... - Venkata P. Satagopam
Kabsch and Sander paper - over 6000 citations - - Shannon McWeeney
Note the subliminal message in the announcement slide - Iddo Friedberg from Android
Prediction by transparency - no computation necessary story - Shannon McWeeney
Awards should be shared: People working with Chris includes: Burkhard Rost, Alfonso Valencia, Liisa Holm and many more - arne
Announcement of unpublished and new work. A good trend at this ISMB. - Roland Krause
Cancer genome atlas: TCGA - arne
Mapping of molecular alterations (cpy number variation) to 200 glioblastoma samples. - Roland Krause
Difference between patients is huge - arne
extract network, find relevant modules. - Roland Krause
illustration of netbox algorithm - Shannon McWeeney
When grouping mutations into pathways up to 85% of GBM have a muation in the most important pathways, while individual genes are down to a few % - arne
Each oncogene may have relatively low frequency across patients; but when you group genes across pathways, a pathway may explain a large fraction of patients with a given type of cancer. - Barb Bryant
"Network pharmacology" - Barb Bryant
can see a change in pathway activation between primary tumor and mets - Mickey Kosloff
Dominant alterations changes between cancer types and states. - Roland Krause
GBM: copy number is rare (and noisier) Ovarian: more regular and higher - arne
profiles of copy numbre variations differ between types of cancers - Mickey Kosloff
Metastatic tumor samples have more copy number changes than primary tumors. Not surprising. But maybe primary samples with more copy number changes than others are more likely to metastasize? Generally, better outcome with fewer somatic copy number changes. - Barb Bryant
BRCA1 and BRCA2 mutations convey germline inherited cancer risk - Barb Bryant
These genes act in the homologous repair pathway. Half of all patients have mutations in some homologous repair pathway gene. - Barb Bryant
and more generally, homologous repair genes are altered in > 50% of ovarian cancer - Mickey Kosloff
Tumor suppressor genes can be inactivated in various ways: germline mutation, somatic mutation, epigenetic silencing, etc. - Barb Bryant
There are drugs under development that might work particularly well in patients with defects in this particular pathway. - Barb Bryant
Cancer genomics portal: - Barb Bryant - Barb Bryant
Topic shift: now, perturbation cell biology. "and belief propagation". (eh?) - Barb Bryant
Perturbation Cell Biology - arne
In recent past, says Chris, you make a few perturbations: overexpress or knock down a gene; inhibit with a compound, etc. - Barb Bryant
use network inference algorithms - Mickey Kosloff
goal = predictive models for therapy - Mickey Kosloff
with only 200 datapoints -> derive validated (known) pathways - Mickey Kosloff
Prediction of networks does not scale to larger networks - arne
Large data generation with the number of pertubation > than proteins. - Roland Krause
Still prohibitively large number of networks even for small number of nodes. - Roland Krause
Use statistical physics methods to tackle combinatorial explosion of possible networks. - Barb Bryant
Inference using belief propagation known from statistical physics. - Roland Krause
Ah, here is where "belief" comes in. Network inference using belief propagation. Reference Riccardo Zecchina et al. - Barb Bryant
Instead of going through all the models that are possible, you derive statistical properties across a set of good models for each of the Wij weights in the model. - Barb Bryant
This is sort of like partition functions in statistical physics - Barb Bryant
evolving work on Wij (transition from Nelander et al 2008- - Shannon McWeeney
Cavity approach - optimize locally on global background iteratively cover all local cavities - Shannon McWeeney
Mm, this is rather opaque to me. - Barb Bryant
"Let me give you some intuition about how this all works." Yes, I'd like that. - Barb Bryant
Nice results on toy experiment - constraints from 10 experiments with 5 interactions (the nodes W in factor graph). - Shannon McWeeney
Almost looks too good - arne from iPhone
after step 1 - generation of probability distributions then step 2- decimation - Shannon McWeeney
So you have a probability distribution for each Wij, which represents the interaction between element i and element j. I'm not really getting how you "update" these probability distributions in the iterative steps. I do understand that at the end you take the most "certain" (narrowest) distribution and fix its value (some Wij) at the most probable value, then update all the other Wij's given this fixation. And so on. To get your final model in a sort of greedy fashion. - Barb Bryant
And by the way, the underlying model is a simple differential equation sort of thing: change of one variable xi is a sigmoidal function of weighted (Wij) sum of all variables xj, less a decay term. - Barb Bryant
thanks for the summary bb - Michael Jones
Mike! - Barb Bryant
Mentions bunches of other stuff in passing. Like bioPAX: paper in press. - Barb Bryant
bioPAX is community project on pathways, ontology, and exchange format. - Barb Bryant
"no science without people; science for the people; ask good questions" - Shannon McWeeney - arne from iPhone
Ask good questions !!!!! - arne from iPhone
Question: Interacting network tend to be modular, with strongly-interacting subnetworks that interact weakly with each other. ... - Barb Bryant
Chris: Is the modular approach really useful in confronting the data? [Is that what he said?] - Barb Bryant
Question: can you get at causal relationships? - Barb Bryant
Chris: yes - if the network model allows you to predict correctly the result of a particular perturbation applied to a particular node, then you can simulate using that model. - Barb Bryant
Question: with a big network, how many experiments will you need to model? - Barb Bryant
Chris: Good question. Could use an entropy measure. Help us figure this out. Help us design the experiments. It's important because of the costs of experiment. This is going to be broadly applicable in cell biology. - Barb Bryant
bb - he said one should see if approach is useful by confronting with real data - Shannon McWeeney from BuddyFeed
Ah, thx - Barb Bryant
Chris gets at the difference between a model that tells a story and a model that is truly predictive. - Barb Bryant
Question: yes, but, what are the semantics of the graph? What kinds of interaction? Answer: The semantics are in the mathematics of your model. - Barb Bryant
Question: mean field approach is interesting. Compared to Monte Carlo approach, you are assuming some decoupling. Loss of posterior coupling between weights - is that an issue? - Barb Bryant
Chris: If you look at a coupled system overall, the extent to which the algorithms work depends on correlations within the system. Long-range (in terms of network distance) correlations are problematic. There are some clever approaches to handle some of this. Mentions non-ergotic space; deal with parts of space separately or iteratively. - Barb Bryant
LBR15: David Rossell - Quantification of Alternative Splicing from Paired-End Reads
Ack! We need to know transcript length to get transcript abundance. Did I hear that right? - Michael Jones from Android
Maybe just if you want to discriminate variants - Michael Jones from Android
Michael Jones
SoundCloud » Blog Archive » Need A Light-Weight Artist Or Band Website? Look No Further! -
music - Michael Jones
General Kafka
I've come to take you. Your time on earth is over. / But you can't. I haven't time. I have my performance/ It's cancelled, due to the death of the actor -- 7th seal dialogs.
dude u r really depressing me. - Michael Jones
ah! in the movie (7th seal) most characters find death a deliverance. The movie is beautiful. - General Kafka
General Kafka
Love is nothing but lust and cheating and lies / It hurts all the same / Love is the blackest of all plagues, but you don't even die of it and usually it passes. -- 7th seal dialogs.
dark stuff - Michael Jones
General Kafka
Faith is a terrible affliction. It is like loving someone in the dark who won't answer when you cry out in pain. -- 7th seal dialogs
That is sad. Maybe faith is knowing that there is more then just what you see and knowing that you are part of something amazing no matter how isolated you are. - Michael Jones
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