Research area
Bioinformatics and computational biology
Research area
Bioinformatics and computational biology

INTEGRATIVE SYSTEMS BIOLOGY

Research

The availability of omics-scale data has transformed biomedical research, enabling a systems-level understanding of genotype-phenotype relationships. At the Integrative Systems Biology Laboratory, we apply this framework to two key research areas of biomedical interest.
First, we investigate the molecular mechanisms underlying adult neurogenesis in mice, leveraging single-cell transcriptomics to map cellular diversity and uncover regulatory dynamics. Second, we develop knowledge-graph-based methods to prioritize therapeutic targets, aiming to reveal hidden treatment opportunities within interconnected biomedical data.
Our work is guided by fundamental questions such as:

  • What active gene regulatory networks sustain specific cell types or states?
  • Can we identify new therapeutic targets for approved drugs through systematic exploration of knowledge graphs?.

Skills & tools

We integrate complex systems theory, statistical mechanics, and network science with data mining and deep learning to extract biologically meaningful patterns from vast omics datasets. Our goal is to decipher the emergent principles governing cellular decision-making, molecular interactions, and therapeutic strategies.

  • Single-Cell Transcriptomics: Cellular heterogeneity analysis, trajectory inference, and gene regulatory network reconstruction.
  • Knowledge Graph Analytics: Multi-layer network integration, network prioritization algorithms, Graph Neural Networks, and graph embedding techniques.
  • Computational Modeling: Statistical mechanics approaches, agent-based simulations, and dynamical systems applied to biological networks.

Collaboration interests

  • Experimentally validate computational predictions of regulatory mechanisms, drug-target interactions, and gene-disease associations.
  • Integrate multi-modal data to construct unified models of cellular processes, disease mechanisms, and drug repurposing knowledge graphs.

Selected publications

  • RASETTO, Natalí B., et al. Transcriptional dynamics orchestrating the development and integration of neurons born in the adult hippocampus. Science Advances, 2024, vol. 10, no 29, p. eadp6039.
  • URÁN LANDABURU, Lionel, et al. TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration. Nucleic Acids Research, 2020, vol. 48, no D1, p. D992-D1005.
  • MANCINI, Estefania, et al. ASpli: integrative analysis of splicing landscapes through RNA-Seq assays. Bioinformatics, 2021, vol. 37, no 17, p. 2609-2616.

Principal investigator

Ariel Chernomoretz, PhD

[email protected]

  • systems biology
  • single-cell transcriptomics
  • drug repurposing
  • knowledge graphs
  • computational modeling