| 研究実績の概要 |
In this research line, we have combined several types of data, from metabolic fluxes, gene expression profiles, protein interaction networks, metabolic pathways and signaling transduction pathways and proposed several control methods and algorithms to study controllability in biological systems.
Our previous research has led to the development of a probabilistic control framework that integrates metabolic fluxes information to detect important nodes engaged in network control when transitioning between healthy to cancer states. Moreover, we also analysed dynamically protein networks by identifying critical control proteins driving the aging process. Then, a new model to address directionality was proposed to investigate cellular networks whose physical interactions or biochemical reactions are directed and not reversible. The model was used to identify critical proteins in signaling pathways obtained in the context of a probabilistic model. The critical proteins involved in rare diseases, viruses and other prominent biological functions were examined. Moreover, we defined a new metric that captures the importance of intermittent nodes in biological systems and demonstrated that they play a more important role than previously thought. Although the metric is simple, its computation is challenging, and an efficient algorithm was proposed. Moreover, different control frameworks, such as maximum matching, were also investigated, and algorithms for multilayer network analysis are currently under development. We expect to publish additional papers on the above mentioned topics.
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