Our research is at the intersection of computational systems biology, systems medicine, mathematical biology, and bioinformatics. We delve into the design, software implementation, and application of mathematical algorithms that enable us to model, simulate, and strategically control or reprogram biological systems. Our approaches put an emphasis on data-driven approaches and the integration of multi-modal omics data profiles. Key areas of our research focus encompass:

Development and Application of Methods to Infer Gene Regulatory and Signaling Networks. Much of our research work is related to the development and application of algorithms for data-driven inference (reverse-engineering) of biological systems, with an emphasis on discrete dynamical models such as Boolean networks. These undertakings include: (1) Data discretization, (2) Static Network Inference, (3) Dynamical Model’s Inference, (4) Model Analysis and Simulation and, (5) Model Verification.

Development and Application of Algorithms to Identify and Prioritize Cell Reprogramming Targets. At our laboratory, we leverage cutting-edge computational techniques to identify and prioritize targets for cellular reprogramming. This transformative approach holds immense potential for applications in regenerative medicine and cancer research. Cell reprogramming is a multifaceted process, not merely governed by a cell’s gene regulatory topology, but also profoundly influenced by the dynamic interplay within the cell’s system and its microenvironment. In response to this intricate landscape, we integrate principles from dynamical systems and control theory, aided by machine learning methods for prioritization studies. Our collaborative endeavors span a range of fields, including cancer research, stem cell biology, immunology, and biogerontology.

Cancer Reversion: Computational Systems Biology Approaches. Cancer reversion is the process by which tumorigenic cells lose their malignant phenotype. In this pivotal project, our aim is to design and apply innovative computational tools, coupled with mathematical modeling, to unearth the molecular architects of cancer reversion, decode their operational mechanisms, and chart a path for their clinical deployment. One of our visionary objectives is to curate a comprehensive catalog for reversion potential of various cancer cells, offering an invaluable resource for ongoing and future research.

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