Mechanistic and Data-Driven Modelling Approaches for Regulatory Networks and Diseases

Dear Colleagues,

The complexity of biological regulatory networks presents significant challenges for understanding their roles in health and disease. Unravelling the dynamics of these networks is crucial for deciphering disease mechanisms. Mechanistic modelling is a powerful tool in this field, enabling researchers to predict system behaviours, identify potential therapeutic targets, and simulate the effects of drug interventions. In addition, advances in high-throughput technologies, such as genomics, transcriptomics, proteomics, and single-cell sequencing, have generated vast datasets, offering unprecedented opportunities to develop data-driven models that uncover underlying regulatory mechanisms and provide predictive insights into disease processes. The integration of mechanistic models, which leverage established biological knowledge, with data-driven approaches, which utilize cutting-edge machine learning and AI techniques, has opened new frontiers in systems biology.

I invite you to contribute research articles and review papers that highlight novel mechanistic modelling and data-driven approaches for understanding regulatory networks and discovering mechanisms for disease intervention.

By highlighting diverse methodologies and applications, this Special Issue will demonstrate how computational, statistical, and information technology tools transform biological data into actionable knowledge, fostering innovation in disease research and therapeutic development.

I look forward to receiving your contributions.

Dr. Tianhai Tian
Guest Editor

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