The newly formed Systems Pathobiology Unit will focus on developing multidisciplinary approaches to complex diseases.
Control theory deals with dynamic systems that respond to external inputs with specific output signals. Concepts common in control theory, from dynamics, feedback to signal processing, found their way into biology, especially in systems biology, where the need to capture systems-level knowledge requires us to conceptualize and organize large amount of quantitative biological interactions. Consequently, control theoretical tools have been successfully used to analyze biochemical reaction networks and genetic regulatory networks. Many dynamic properties of complex biological systems can be learned from the structure of the underlying networks. We will combine tools from control theory and network science to address a series of questions related to complex biological systems, from systems identification to optimal control.
By uncovering the molecular pathways through which multiple genetic factors jointly affect a disease phenotype, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules that are typically represented as an interaction network or interactome. Identifying the disease associated modules or pathways can often be formalized as combinatorial optimization problems on the interaction network, e.g., the set cover, maximum-weighted connected subgraph, prize-collecting Steiner tree problems. We will develop new formalizations of the disease module detection problem and use tools from statistical physics to guide the algorithmic development.