In biological systems, similarly to the tenet of modern architecture, form and function are solidly intertwined. Thus to gain complete understanding in various contexts, the curation and study of form turns out to be a mandatory first phase.

Biology is in the era of the ''Omes'': Genome, Proteome, Toponome, Transcriptome, Metabolome, Interactome, ORFeome, Recombinome, and so on. Each Ome refers to carefully gathered data in a specific domain. While biotechnology provides the data for most of the Omes (sequencing technology for genomes, mass spectrometry and toponome screening for proteomes and metabolomes, high throughput DNA microarray technology for transcriptomes, protein chips for interactomes), bioinformatics algorithms often help to process the raw data, and sometimes even produce the basic data such as the ORFeome and the recombinome.

The problem is: biological data are accumulating at a much faster rate than the resulting datasets can be understood. For example, the 1000-genomes project alone will produce more than 10^12 raw nucleic acid bases to make sense of. Thus, databases in the terabytes, even petabytes (10^15 bytes) range are the norm of the day. One of the issues today is that our ability to analyze and understand massive datasets lags far behind our ability to gather and store the data with the ever advancing bio- and computing technologies. So, while the sheer size of data can be daunting, this provides a golden opportunity for testing (bioinformatic) structure-discovery primitives and methods.

Almost all of the repositories mentioned here are accompanied by intelligent sifting tools. In spite of the difficulties of structure discovery, supervised or unsupervised, there are reasons to believe that evolution endowed biological systems with some underlying principles of organization (based on optimization, redundancy, similarity, and so on) that appear to be present across the board. Correspondingly, using evolutionary thoughts as a "guiding light", it should be possible to identify a number of primitive characteristics of the various embodiments of form and structure (for instance, simply notions of maximality, irredundancy, etc.) and to build similarly unified discovery tools around them. Again, the forms may be organized as linear strings (say, as in the genome), graphs (say, as in the interactome), or even just conglomerates (say, as in the transcriptome). And the fact that even the rate of data accumulation increases continuously becomes rather a blessing in this context than a curse. It is therefore a worthwhile effort to try and identify these primitives. This seminar was intended to focus on combinatorial and algorithmic techniques of structure discovery relating to biological data that are at the core of understanding a coherent body of such data, small or large. The goal of the seminar was twofold: on one hand to identify concise characterizations of biological structure that span across multiple domains; on the other to develop combinatorial insight and algorithmic techniques to effectively unearth structure from data.

The seminar began with a town-hall, round-table style meeting where each participant shared with the others a glimpse of their work and questions that they were most excited about. This formed the basis of the program that was drawn up democratically. As the days progressed, the program evolved organically to make an optimal fit of lectures to the interest of the participants.

The first session was on population genomics, covered by Shuhua Xu and Laxmi Parida. The second was on methods on genomic sequences, covered by Rahul Siddharthan and Jonas Almeida. The next talks were on clinical medicine: an interesting perspective from a practicing physician, Walter Schubert, on treatment of chronic diseases, and Yupeng Cun spoke about prognostic biomarker discovery. Algorithms and problems in strings or genomic sequences were covered in an after-dinner session on Monday and in two sessions on Tuesday morning and late afternoon. The speakers were Sven Rahmann, Burkhard Morgenstern, Eduardo Corel, Fabio Cunial, Gilles Didier, Tobias Marschall, Matthias Gallé, Susana Vinga and Gabriel Valiente. The last speaker presented a system called "Tango" on metagenomics, and in a bizarre twist concluded the session and the day with a surprise live Argentine Tango dance performance with one of the organizers of the seminar. The early afternoon session was on metabolic networks, with lectures by Jörg Ackermann, Jun Yan and Qiang Li.

The Wednesday morning session was loosely on proteomics, with lectures by Alex Pothen, Benny Chor, Axel Mosig, Alex Grossmann, and Deok-Soo Kim. Coincidentally, three lecturers of this session shared very similar first names, leading to some gaffes and some light moments at the otherwise solemn meeting.

The Thursday sessions were on phylogenies and networks, with lectures by Mareike Fischer, Mike Steel, Katharina T. Huber, Christoph Mayer, James A. Lake, Péter L. Erdös, Stefan Gruenewald and Peter F. Stadler. James A. Lake presented an interesting shift in paradigm, based in biology, called cooperation and competition in phylogeny. Péter L. Erdös gave a fascinating talk on the realization of degree sequences. Yet another session on strings was covered by Matteo Comin and Funda Ergun on Thursday. The day concluded with a lecture by Andreas Dress on pandemic modeling.

There were a few after-dinner sessions on big data, thanks to Jonas Almeida. An eclectic set of lectures were given on the last session on Friday, by Raffaele Giancarlo on clustering and by Concettina Guerra on network motifs. The meeting concluded with a fascinating lecture by Matthias Löwe on the combinatorics of graph sceneries. The impact of this on biology may not be immediately clear, but such is the intent of these far-reaching, outward-looking seminars.