The goals of the Systems Biology Center New York are to develop and disseminate approaches that provide a mechanistic understanding of how molecular interactions within regulatory networks in cells lead to the physiological function of tissues and organs, and how therapeutic agents affect cellular regulatory networks to alter pathophysiological states i.e. "the therapeutic implications of the design logic of scalability in mammalian systems".
The research focus of SBCNY will be on the development and analysis of scalable models to identify regulatory network motifs and how they are reconfigured by drugs. Models will originate from experimentally measured parameters, and the predictions of models will be used to develop experiments that shed light on the design logic of the system. We have chosen such an approach because it builds on previous successes by members of the SBCNY, including the identification of regulatory motifs such as gates and switches arising from positive feedback loops. In these cases, the subsequent experimental verification of the model predictions supports the general strategy that has allowed us to identify new regulatory network motifs within signaling networks such as gates, which have been verified experimentally and play a major role in physiological functions, such as long-term potentiation of synaptic transmission. We have also used this approach to identify a positive feedback loop that functions as a flexible switch in a proliferative pathway in fibroblasts. Several anti-cancer-drugs are targeted at components in this regulatory motif. Positive feedback loops in signaling pathways can also function as developmental switches. Such signaling networks are "tunable", with different parts of the network activated by different stimuli. Similarly, work by Kaplan and colleagues on neuronal networks in the visual cortex suggest that facilitatory, probably tunable feedback is common. Graph theory-based analysis by Alon and co-workers has shown that such regulatory network motifs are the building blocks of networks suggesting that an improved understanding of the physiological function of cells and tissue can come from considering the regulatory motifs. This approach of studying regulatory motifs complements statistical approaches that track and rank signaling pathways that control phenotypic behavior.