Computational systems biology

Developing predictive models for precision medicine

With the advances of high-throughput experimental techniques, biomedical research is turning into information science. This requires the use of machine and deep-learning approaches, statistics and mathematical modelling. Individual cellular processes that comprise the interplay of several molecular players, such as cell signaling, can now be quantitatively characterized to allow a systematic view of biological processes. A better understanding of biological processes is crucial in order to provide robust predictive models that improve disease prognoses and treatment strategies.

Our group is exploiting a large variety of data — multi-omics datasets, single-cell proteomics and mass spectrometry-based quantitative proteomics — to dissect the molecular mechanisms of cancer. Our goal is to develop predictive models for precision medicine.


Data

  • Omics: RNAseq, CNV, SNP, miRNA, SWATH–MS
  • Single cell: Mass cytometry
  • Clinical data: Survival outcome, treatment
  • Literature: Publications
  • Compound structure: SMILES, graph representation of molecules
  • Networks: protein–protein interactions, pathways
Genomics overview

Methods

  • Machine learning: Deep learning, dimensionality reduction, clustering, classification
  • Statistical inference: Probabilistic models, network inference
  • Mathematical modeling: Stochastic hybrid models, Boolean networks
 

Research goals

  • Tumor heterogeneity
  • Leading drivers of cancer
  • Molecular mechanisms
Genomics overview

Personalized medicine

  • Patient stratification
  • Early diagnosis
  • Targeted treatment

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Funding

Maria Rodriguez Martinez

María Rodríguez Martínez
IBM Research scientist

We gratefully acknowledge generous funding from


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