, Duke University, North Carolina, and Berlin Institute for Medical Systems Biology/Max Delbrück Center & Humboldt Universität Berlin
Understanding how transcription regulation is encoded in the genomes of complex multicellular organisms has been a big challenge, not least due to the large non-coding space where relevant interactions might occur. High throughput technologies now allow it to map putative regulatory regions via their chromatin structure, and have made rapid progress in identifying in vivo binding of transcription factors to DNA at high resolution. Large collections of relevant data have been made available by individual groups as well as large consortia such as mod/ENCODE. I will discuss some of our recent and ongoing efforts that make use of such datasets to define successful computational models for individual sites as well as for cell-type specific expression.
, Chair of Bioinformatics, KU Leuven.
Despite significant advances in omics techniques, the identification of genes causing rare genetic diseases and the understanding of the molecular networks underlying those disorders remains difficult. Gene prioritization attempts to integrate multiple, heterogeneous data sources to identify candidate genes most likely to be associated with or causative for a disorder. Such strategies are useful both to support clinical genetic diagnosis and to speed up biological discovery. Genomic data fusion algorithms are rapidly maturing statistical and machine learning techniques have emerged that integrate complex, heterogeneous information (such as sequence similarity, interaction networks, expression data, annotation, or biomedical literature) towards prioritization, clustering, or prediction. In this talk, we will focus in particular on kernel methods and will propose several strategies for prioritization and clustering in particular. We also go beyond learning methods as such by addressing how such strategies can be embedded into the daily practice of geneticists, mostly through collaborative knowledge bases that integrate tightly with prioritization and network analysis methods.
, Center of Life and Food Sciences Weihenstephan, Technical University Munich.
Elucidating mechanisms of life requires analysis of whole systems and understanding the complex interplay of the individual components. Proteins control and mediate the majority of biological activities and interactions among proteins play a decisive role in the dynamic modulation of cellular behavior. Protein-protein interactions are essential constituents of all cells and interactome analysis is an important component in the quest for a systems level understanding of life.
We explore interactome networks for yeast, human and plant at ever increasing completeness and quality using both experimental and computational mapping and analysis tools. Based on benchmarking and standardized reference sets we have developed experimental approaches and mathematical models for the quantitative evaluation of the completeness and quality of interactome maps. These models enable a critical assessment of current maps and guide development of a roadmap towards completion.
Recently mapping of the first binary interactome network for the reference plant Arabidopsis thaliana was completed. Using tools of graph theory we identify biologically relevant network communities from which a picture of the overall interactome network organization starts to emerge. Combination of interaction and comparative genomics data yielded insights into network evolution, and biological inspection resulted in many hypotheses for unknown proteins and revealed unexpected connectivity between previously studied components of phytohormone signaling pathways.
Using the network we explored how bacterial and fungal pathogens perturb their host's network. Pathogen effectors from evolutionary distant pathogens were found to converge on network hubs, which appear "guarded" by resistance proteins, and which we show to be functionally important for the host's immune responses. Genetically, we were able to validate >90% of the Arabidopsis proteins targeted by both pathogens. Together, we show how high-quality protein interactome network maps provide us with tools for elucidating fundamental laws underlying biological systems.
, EMBL-CRG Systems Biology Unit, Centre for Genomic Regulation, Barcelona.
To what extent is it possible to predict the phenotypic differences among individuals from their completely sequenced genomes? We use model organisms (yeast, worms) to understand when you can, and why you cannot, predict the biology of an individual from their genome sequence.
September 8, 2012, Congress Center Basel, Workshop WS1
Session 1: 8:50am-10:30am*
- Invited Talk: Uwe Ohler, Duke University, Deciphering transcription regulation: from individual sites to cell type specific expression
- Jianlong Qi: Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests
Coffee Break: 10:30am-11:00am
Session 2: 11:00am-12:30pm
- Luna De Ferrari: Active and guided learning of enzyme function
- Joeri Ruyssinck: Inferring gene regulatory networks using ensembles of feature selection techniques
- Thais G. do Rego: Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models*
Session 3: 1:30pm-3:00pm
- Invited Talk: Yves Moreau, KU Leuven, Kernel methods for genomic data fusion
- Bettina Knapp: Efficient, data-based network inference using a linear programming approach
Poster Session 3:00pm-4:30pm
Session 4: 4:30pm-6:00pm
- Celine Brouard: Learning a Markov logic network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes
- Federica Eduati: Integrating literature-constrained and data-driven inference of signalling networks
- Daniela Stojanova: Using PPI Networks in hierarchical multi-label classification trees for gene function prediction
September 9, 2012, Congress Center Basel, Workshop WS1
Session 5: 9:00am-10:30am
- Invited Talk: Pascal Falter-Braun, TU Munich, Signatures of evolution and systems organization from an Arabidopsis interactome network map
- Paurush Praveen: Boosting statistical network inference by incorporating prior knowledge from multiple sources
Coffee Break: 10:30am-11:00am
Session 6: 11:00am-12:30pm
- Mehmet Gönen: Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization
- Elisabeth Georgii: Targeted retrieval of gene expression measurements using regulatory models
- Markus Heinonen: Metabolite identification and molecular fingerprint prediction via machine learning
Session 7: 1:30pm-3:00pm
- Invited Talk: Ben Lehner, Centre for Genomic Regulation Barcelona, The genetics of individuals: why would a mutation kill me, but not you?
- Chris Oates: Network inference using steady-state data and Goldbeter-Koshland kinetics
Coffee Break 3:00pm-3:30pm
Session 8: 3:30pm-4:30pm
- Thomas Sakoparnig: Efficient sampling for Bayesian inference of conjunctive Bayesian networks
- Felix Sanchez-Garcia: Helios: discovering driver oncogenes
Closing Remarks: 4:30pm