Research and objectives Modern software is incredibly complex: for example, a modern OS has more than 10 millions lines of code, organized in 10s of layers! It is easy to wonder how software of this complexity can work at all, in a reliable way. Computer scientists designed tools to tackle software complexity over the last 50 years. From high level languages, compilers and IDEs to automatic build, testing and model checking of modular components, Computer aided methods helped developers to tackle Complexity in software Biological systems share with software systems this great complexity. Even more, lots of properties emerge from interaction of single component, resulting in an unbelievable dynamic complexity. Can we use ideas and tools from computer science, which proved to be successful in tackling software complexity, to help scientists understand biology? Can we made investigation of utterly complex interaction networks feasible for biologists? The challenge is not only to find a way to “use computers to do biology”, but to do this in a way that scales with complexity and in a way that is easy to use for biologists. We designed a set of tools for a “biological programming language” (Beth, a language derived from process calculi) in which biological entities (genes, proteins, etc.) are described as executable programs. Entities are compiled and executed by our stochastic simulator; the simulator mimics the interactions of molecules and proteins by running our biological program, in a in-silico experiment. The biological reality is reproduced on a computer, as it happens, for example, for flight simulators. We are also adding simulation of diffusion, to track the movement of particles within the cell. The compiler / interpreter: BetaWB Stochastic simulator Tackling biological complexity with BetaWB Lorenzo Dematté The Microsoft Research – University of Trento Centre for Computational and Systems Biology Complexity in biology These biological networks interact at different levels: genetic control, protein interaction, metabolic networks. software complexity. The Plotter: interpreting results Future work Our set of tools is only at the beginning: we want to integrate them better, to make them simpler to use and more powerful; for example we are working on visualization and navigation of big graphs for the plotter. Static analysis, like model checking and evaluation of the underlying Markov chain are also in our plans, as well as the development of parallel algorithms to speed up computations Results of simulations must be interpreted by biologists to refine model and guide experiments. Our tool plots how the system evolved in time and how biological processes communicated, associated and modified during the simulation. [1] The Linux Kernel, Free Code Graphing Project. [2] Microsoft Visual Studio [3] GNOM project, Centro Nacional de Biotecnologia (CBN / CSIC) at the Universidad Autonoma de Madrid. [4] Adai et al. LGL: creating a map of protein function with an algorithm for visualizing very large biological networks. [5] Microsoft Flight Simulator X. [4][3] [1][2] [5] The Designer: visual model composition A programming language may be difficult to use for a biologist: we addressed this issue by adding a graphical IDE to our tools. For now, this don’t mask completely the language, but it helps considerably in building a skeleton of complex systems with just some clicks.