Many of the presentations given during the second part of the meeting reported findings derived from cell-based high- or medium-throughput functional screens, most of them relying on RNAi-mediated knock-down. Here is an overview of the screens presented during this meeting, illustrating by their diversity in scope and scale the versatility of this method:

Perhaps not surprisingly, many speakers emphasized that RNAi screens invariably need to be followed up by time-consuming and tedious validations. The off-target problem in mammalian cell-based RNAi screens appears also to be taken very seriously and it was reported that from 4-7 siRNA directed against the same gene were necessary to reach a good level of confidence. In view of the increasing number of RNAi-based functional screens, standards for the description of such experiments (eg. MIARE, MIACA) are likely to become increasingly useful.

In systems biology, network models are often central for the interpretations of omics data related to molecular interactions and they allow to generate biological insights which are different from those derived from the more classical screening-mechanistic-dissection paradigm. In this regard, Uwe Sauer presented exciting work on the relationship between transcriptional regulatory networks, protein expression and the state of the yeast metabolic network. Using a combination of genetic approach and drug perturbations, a series of parallel 'fluxomic' and metabolomic measurements revealed that metabolic fluxes, in contrast to metabolite concentrations, remain robust to perturbations and are apparently affected only by a handful of transcription factors in a given condition at steady state. At the computational level, integration of different types of data represents significant challenges. For example, it is far from trivial to find ways to exploit the information contained in interaction networks and integrate it with other types of large-scale molecular measurements. Trey Ideker exposed an efficient solution to this problem within the context of microarray profiling of breast cancers and showed that expression data can be combined with information on protein physical interactions to define improved and biologically meaningful pathway-based biomarkers for the classification of metastatic vs non-metastatic tumors.

While superposing parallel datasets leads to a 'vertical' integration of networks, Marian Walhout presented an approach to integrate 'horizontally' transcriptional and miRNA-dependent regulatory links and map a composite transcription factor/miRNA regulatory network in Caenorhabditis elegans. In this elegant work, the yeast one-hybrid assay was used as a gene-centric screening method to identify regulatory links between hundreds of transcription factors and promoters of both miRNA genes and genes encoding transcription factors. Closing the loop, the network was completed by computationally predicting the transcription factors potentially targeted by miRNAs. Interestingly, the resulting network showed numerous composite motifs including negative feedback loops (TF --> miR --| TF), which are otherwise under-represented in pure transcriptional regulatory neworks.

Completion of network models may require tedious and repetitive work. To the question "who will fill the gaps?", Steve Oliver replied: "a Robot Scientist". He showed that an actual implementation of such a robot is able to iteratively use a computational model of the yeast metabolic network to automatically design informative experiments, perform them and use the results to extend the model. In an effort to provide a genome-scale overview of the molecular interactions that underly regulation of gene expression, Tim Hughes presented a variety of microarray-based technologies to systematically map transcription factor-DNA, nucleosome-DNA and protein-RNA interactions. The latter results were particularly intriguing given that the high-throughput identification of targets of RNA-binding proteins remains a relatively unexplored route and may reveal novel insights into the complexity of post-transcriptional regulation.

To conclude on a somewhat different note, it was also interesting to observe that an increasing number of studies were accompanied by extensive web resources providing access to the respective datasets:

This situation underscores the need of a proper infrastructure to host and share (or publish?) large datasets in biology and the central role of web technologies in this regard. In view of the proliferation of biological databases, I wonder whether it might be helpful to have general recommendations on some minimal requirements for this type of databases--eg. type of searching, visualization, data integration functionalities, existence of a (web) APIs, download of datasets, possibility to integrate external datasets, etc...? Or would perhaps something like a 'Minimum Information About a Biological Database' be useful to specify the capabilities of databases? One may also dream that these databases will become progressively interoperable and eventually include web-based APIs facilitating programmatic access to the information stored, ultimately sending Omics in the Cloud...

And, oh yes, the slopes were very nice, even though, I have to admit the air was thin and a little fresh...

In any case, those who survived this harsh test were highly rewarded by attending an extremely exciting meeting, organized by Ruedi Aebersold and Tony Pawson, showcasing the impact of genome-wide and high-throughput technologies, the so-called 'omics', in cell biology.

After the two first days of the meeting, dedicated to 'cell signaling' and 'sub-cellular organization', a series of impressive talks had already delivered a clear and strong message: beyond generating comprehensive 'part lists', omics data lead to important and novel biological insights when integrated with functional and phenotypic data and when applied in experiments addressing well defined aspects of the biology of the system under study. This was particularly well illustrated in the talks dedicated to signaling, which all reported on analyses of well defined systems: ephrin-Eph receptor bidirectional signaling in cell-cell contact (T. Pawson), insulin signaling and growth regulation (E. Hafen), notch signaling and sensory organ development (J. Mummery-Widmer), cytokines and hepatotoxicity (B. Cosgrove), Rho signaling & cell migration (C. Bakal).

I have the feeling that this transition from descriptive catalogs to functional and mechanistc insights can be envisioned as the result, at least in part, of two series of developments:

First, experimental design is evolving and an increasing number of projects combine and integrate functional readouts with genetic approaches and high-throughput molecular measurements. For example, Tony Pawson illustrated how the integration of quantitative (SILAC) proteomics, phenotypic siRNA screens and protein complex identification could shed light on the components and mechanisms involved in ephrin-Eph receptor bidirectional signaling and their impact on cell-cell contacts. A combination of quantitative proteomics and genetic approaches was illustrated by Ruedi Aebersold, whose lab is charting a comprehensive kinase-substrate network in yeast by systematically performing quantitative proteomics on deletion mutants of all kinases and phosphatases. Other experiments link even more intimately, by design, systematical perturbations and molecular measurements to phenotypic outcome. Ben Cosgrove presented such work in the context of the study of drug hepatotoxicity. Systematical measurements of the phophorylation status of 17 signaling proteins and monitoring of cell death rates were performed in HepG2 cells under a variety of cytokine stimulation conditions. Multi-variate statistical analysis enable then to construct correlative models, which have not only predictive power but also reveal key players in the process and provide insight into how signaling components contribute to the phenotypic outcome. The power of data integration was also beautifully demonstrated in the work of Jennifer Mummery-Widmer, who performed genome-wide and tissue specific RNAi screens in Drosophila to identify modifiers of the notch signaling pathway. Integration of the genes identified in the screen with a map of known genetic and physical interactions resulted in a network model whose predictive power was exploited to identify and validate in vivo novel regulators of notch signaling.

Second, the technological platforms are maturing, data quality is increasing and protocols are streamlined, making these technologies progressively more accessible. This might be particularly to relevant for mass spectrometry proteomic approaches, which were omnipresent in the signaling talks. One of the consequences of a relative and progressive 'democratization' of MS proteomics platforms is that their application is not obligatorily restricted anymore to an initial exploratory phase traditionally aimed at providing an unbiased view of a particular system, but can now also be engaged in follow-up, often more focused, investigations to gain deeper mechanistic insights. An example of this was provided by Ernst Hafen who presented his work on growth regulation in Drosophila and showed data on a genome-wide and tissue-specific mutagenesis screen aimed at the identification of modifiers of growth regulation. Selected hits of the screen were then analyzed further in time course experiments upon insulin stimulation and mass spectrometry identification of TAP co-immunoprecipitated protein complexes could reveal the nature and dynamics of signaling complex assembly. One can thus predict that further development of optimized omics technologies for targeted follow-up experimentation will have a profound impact in molecular and cell biology.

Mass spectrometry based proteomics was clearly one of the predominant platforms in many of the studies presented during the sessions devoted to signaling. It was therefore particularly fascinating to listen to Mathias Uhlen's talk, who emphasized the need for complementary approaches based on affinity probes and presented foundational work towards antibody-based proteomics. The scale of the this work is such that it is hardly possible to summarize it in just a few sentences. Fortunately, the resource resulting from this enormous effort can be consulted directly online at the Human Protein Atlas portal. I will only add that Mathias Uhlen estimated that this resource will be able to provide quality controlled antibodies for 50% of human proteins within the coming years and that a first draft of the complete human proteome might be ready around 2014!

Beyond omics based on high-throughput measurements at the molecular level, one very exciting development is the application of imaging techniques for automated measurements of cellular and cytological parameters. Lucas Pelkmans showed that measurements of local cellular features (eg nucleus size, local density, mitotic stage, cell edges etc...) at the single cell level could be correlated to various cellular activities such as viral entry, clathrin distribution etc... He insisted that accounting for such local population parameters may have considerable implications for the interpretation of siRNA screens given the unavoidable heterogeneity of cellular populations. This strategy was then applied in the context of a large-scale siRNA screen for modifiers of viral entry performed on 8 different viruses. Cluster analysis of the resulting hits beautifully reveals a hierarchical 'functional phylogenetic' tree of the various virus strains according to the subset of cellular activities required for their entry. This information could in turn be used for the identification of a novel regulatory mechanism of viral entry essential for most of the viruses tested.