Article writing guidelines
Photos from the meeting
What? Meet fellow bioinformaticians in Heidelberg, and discuss Biggest Challenges in Bioinformatics
When? Thursday October 18th, 2012, 19.00 - 21.00
Where? Grabengasse 3-5, Building 2170, Groundfloor, Hörsaal 12a.
How? At the 3rd HUB we'll be trying something new! Because you get to shape the programme, we want to try and write a collaborative article to help us disseminate what we think are the Biggest Challenges in Bioinformatics, and more importantly how we plan to solve them.
Who is coming? Check out the list of registered participants
|19:00||Arrival, welcome and introduction to tonights concept. What is the World Cafe?|
|19:10||All participant discussion of aims and brain-storming/thought-shower!/selection of Biggest Challenges in Bioinformatics.|
|19:30||World cafe - 3 rounds of conversations of 20 minutes each).|
|20:30||Collation of ideas|
|20:55||Further planning and planning of next steps...|
|21:00||To the pub i.e. Essighaus, Plöck 97|
Your contributions to shaping the programme
Propose a Biggest Challenge in Bioinformatics
You don't have to suggest a Biggest Challenge in Bioinformatics before the meeting, but it would certainly be useful! You can of course just bring your ideas along on the night. If you've got an idea add it to the list here:
- Reconstruct the tree of life
- What's a species?
- The Data Avalanche
- Rule-/Network-based inference
- Personalized medical/clinical dimensions
- Common modelling platforms: networks, ontologies, vocabularies
- Information retrieval in the tangle of data
- Combining heterogeneous data to make quantitative predictions
- Inference: causation on top of correlation in heterogeneous biological data
- Clarity/availability of methods, tests and data - enabling replication and downstream use
- Predicting, not just explaining
Might be worth having a look at this book The Ten Most Wanted Solutions in Protein Bioinformatics by Anna Tramontano.
The problems presented there are:
- Problem One considers the challenge involved with detecting the existence of an evolutionary relationship between proteins.
- Two and Three studies the detection of local similarities between protein sequences and analysis in order to determine functional assignment.
- Four, Five, and Six look at how the knowledge of the three-dimensional structures of proteins can be experimentally determined or inferred, and then exploited to understand the role of a protein.
- Seven and Eight explore how proteins interact with each other and with ligands, both physically and logically.
- Nine moves us out of the realm of observation to discuss the possibility of designing completely new proteins tailored to specific tasks.
- And lastly, Problem Ten considers ways to modify the functional properties of proteins.
Of course, very much protein structure specific, but might be a starting point.
Propose a Flash Talk
The list of flash talks are here. We won't have time for one this time, but they'll be back at HUB4!
Please take a look at the Guidelines.
Adding details to interaction networks
- Matthew Betts
- CellNetworks / University of Heidelberg
- Many experimental methods for detecting protein-protein interactions tell us what interacts with what but not how. Protein 3D structure can provide these details in many cases. I'll explain how we use protein structure to explain interactions and to predict new ones, and how we use it to investigate the impact of mutations and post-translational modifications.
Fitting high throughput data to a Boolean network model
- Guy Karlebach
- DKFZ - Heidelberg
- Fitting between a high throughout dataset and a Boolean network model is an interesting problem with practical implications. In this flash talk I define the problem, analyze its complexity and present an algorithm for it.
- The talk is based on the following paper
PhenoTimer: connecting time-resolved phenotypes
- Maria Secrier
- Connecting genetic and phenotypic information in the context of temporal variation is an ongoing challenge in systems biology. I will introduce PhenoTimer, a visualization tool for mapping time-resolved phenotypic links in a genetic context. Its capabilities in discovering dynamic regulatory patterns within the cell cycle and potential in identifying links between diseases will be illustrated.