Systems biology and data integration

Systems biology and data integration

To identify new biomarkers and pathways characterizing different immune and clinical phenotypes within people living with HIV (PLHIV), as well as to predict the clinical outcome of the disease, we follow a robust data integration and systems biology approach. The aim is to apply statistical and mathematical models to different omics data layers (genomics, epigenomics, transcriptomics, metabolomics, proteomics, metagenomics) obtained from approximately 2000 PLHIV, who present substantial variability in disease progression and viral control.

The systems biology approach helps address a number of questions:

  • 1. To identify whether regulation of inflammation in PLHIV is different from healthy volunteers,
  • 2. To identify clinical, immunological and omics profiles distinct to comorbidities in PLHIV,
  • 3. To characterize factors associated with extreme HIV clinical phenotypes (i.e. rapid progression, immune non- responder, elite controller, etc), and finally
  • 4. To pinpoint biomarkers and/or mechanisms underlying the immune responses in PLHIV that may be used in translational research (i.e. pre-clinical and clinical).

To ensure the quality and integrity of the results, each step in our omics workflow, including data generation and processing, quality control (e.g., standardization, normalization, and batch correction), data reduction (e.g., feature extraction, clustering techniques, and feature selection), data analysis, and integration techniques, is executed in a detailed and systematic manner. Specifically, for data analysis, various bioinformatics approaches, such as statistical and mathematical models, network and pathway analyses, are applied to explore how each omics data contributes to the biological insight of the study objectives.

Furthermore, omics data are integrated using robust data integration strategies in combination with machine learning approaches (e.g., artificial neural networks, regression models, support vector machines, and partial least squares) to assess complementary effects and synergistic interactions of multiple molecular data, and finally, pinpoint novel biomarkers and/or mechanisms   that may not be apparent through traditional statistical analysis. Subsequently, the novel biomarkers and mechanisms can be further validated in immunological studies through the use of pharmacological or genetic manipulation, in in vitro or in vivo experimental models or even patient studies, when pharmacological modulators are available and safe for human use.

Overall, a systems biology approach will provide a more comprehensive view of the mechanisms underlying different clinical and immune phenotypes within PLHIV and pinpoint novel biomarkers that best represent disease progression and viral control.