Title: Statistical Inference and Model Checking in Systems Biology

Abstract: Advances in genomic technologies have revolutionized molecular biology, enabling biologists to simultaneously probe thousands of genes or proteins in a single experiment in a precise, fast and cost-effective way. Given the high-dimensional data, the big challenge facing the biologists is how to reconstruct the biological network. Statistical inference algorithms play an important role in learning the biological network from high-dimensional data. Different structure learning algorithms might infer different optimal networks from same data due to the limited number of experimental replications and high noise. Without verification, the inferred optimal network cannot help us correctly understand the cellular mechanisms. Model Checking is a formal verification technique for the complex systems, including hardware (e.g., CPU) and software (e.g., aerospace control software). It is the process of determining whether or not a model satisfies a temporal logic formula describing a desired behavior of the system. To overcome the drawback of traditional techniques, we have integrated the statistical learning methods with model checking techniques in a unified framework to automatically infer and verify biological networks from microarray data.