In genomics and bioinformatics, biological network modelling aims at describing interactions between the components present in a cell. These interactions may furthermore evolve along time. Undergoing processes or structures can often be described via latent variables. Models combining all these dimensions raise strong difficulties in terms of inference, mainly because of very intricate dependency structures. Some distributions can then only be approximated and classical statistical methodology therefore needs to be adapted.

For this meeting scientists from both (i) computational science - computer scientists, mathematicians (including modellers, statisticians, machine learning or artificial intelligence persons, operations research people, etc.) interested in tackling complex biological issues by developing graphical/computational solutions to represent the system and the knowledge contained in the data and (ii) biology - scientists who feel the need for such computational approaches and would like to know whether a complex systems view could help them answer newly raised biological issues have been invited

Two types of submissions were welcome: (i) short papers concerning work in progress or presentation of a methodological issue are encouraged (ii) longer papers presenting more mature works and results

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