Systems Biology proposes a new approach to describe biological systems aiming at understanding their global organization rather than focusing on specific molecules and instances. This is made possible by way of new experimental methods and of theoretical modeling. Experimentally, the advent of high-throughput techniques has recently allowed to have access to a wealth of data about the whole repertoire of an organism genes, of their mutual regulation, of the expression levels of the corresponding proteins and of the way the latter interact with each other and act on common substrates. This vast amount of data represents a new challenge for theoreticians, who need both novel algorithms to rationalize and make sense out of the experimental data, and subsequent modeling schemes to use the curated data in order to simulate the corresponding biological processes at various scales. Computational techniques are critical to this endeavor, because the sheer size of the data, the heterogeneity of the systems at hand and their dependence on several variables make any analytical treatment impossible. Moreover, as the number of the systems components grows, correspondingly new computational methods must be devised to describe the behavior of biological processes at several time and length scales. The goal of the workshop is to provide an opportunity for computational scientists from different walks of science and experimentalists to discuss the state of the art in the field of Systems Biology. We will invite experts active in the computational and experimental fields, but we also aim at having a vibrant participation of junior researchers and of newcomers to the field. The discussions throughout the meeting should foster new collaborations and lead to new ideas for computations and experiments in this new and exciting area of research. The workshop will focus on the following aspects: - Data mining algorithms: At the frontier between bioinformatics, computer science and mathematics, these algorithms allow to identify the key ingredients in subsequent modeling steps; - Modeling of stochastic phenomena: No realistic description of biological processes at the molecular level can avoid dealing with a number of sources of stochastic fluctuations, ranging from thermal noise to environmental and individual variability, with the consequence that several different computational schemes must be developed to deal with them: stochastic differential equations, master equations and cellular automata are just a few examples; - Modeling of spatial phenomena: The modern description of cells incorporate the spatial localization of the different biological processes, adding a further layer of computational complexity that must now take into account, e.g., transport and delays; - Detailed modeling of subprocesses: Just as the overall goal of Systems Biology is to provide with an integrate and holistic description of biological processes, such an approach must be complemented with a robust implementation of the basic physical/chemical laws underlying such processes; at present this is feasible only for small processes - Analysis and modeling of interaction networks: Networks are one of the tools of choice to describe the complex patterns of interactions of biological molecules, and their analysis and modeling have become a focus of intense research across several disciplines.