​​Scope

The research portfolio of KAUST's Computational Bioscience Research Center (CBRC) encompasses computational biology and bioinformatics with applications in life sciences.

Core activity: Big Data analytics in life sciences

CBRC aims at solving the methodological and practical challenges linked to extracting useful information from Big Data in Biomedicine and Biotechnology. The center focuses on machine learning and high performance computing for efficient knowledge-, data- and text-mining. Computational approaches are integrated with experimental approaches for data generation and validation. Applications are in human health and Biotechnology.

Vision

CBRC aims to advance life sciences by developing innovative methods, systems and resources for targeted knowledge discovery from biological data.

Mission

  • Design novel computational biology/bioinformatics methods, resources, models and tools suited for high performance computing systems that will lead to and speed up development of applications in human health and medicine, synthetic biology and biotechnology, and validate these applications.
  • Improve capacity to extract targeted relevant information from biomedical data through Big Data methods and analysis.
  • Integrate experimental design, data generation and data analysis.
  • Train high profile specialists in our multi- and inter-disciplinary environment.
  • Engage with industrial partners in te​chnology development and transfer.

Background


The latest technological developments in experimental biology, the depth of scientific questions raised and the demands of genomic sciences clearly demonstrate that bioinformatics and computational biology are necessary key components of the process leading to fundamental discoveries and technological developments. CBRC focuses its resources and activities to two domains: 

  • Human Health and Medicine, 
  • Metabolic Engineering and Synthetic Biology. 

Both topics heavily rely not only on the experimental and clinical data, but also on machine learning and integration of information that can only be obtained from high-throughput assays, omics-type experiments including next generation sequencing (NGS), and other information available in biomedical databases. In recent years biology has witnessed a dramatic increase in the volume of data generated through such experiments, which demands novel computational solutions that can fully exploit the modern computational architectures to enable efficient analyses and necessary scientific insights for many important functional aspects of cellular behavior critical for understanding human diseases and necessary for informed (rational) molecular designs for synthetic biology.

Human Health and Medicine: CBRC addresses problems of

  • Identification of disease biomarkers,
  • Finding the new uses of existing drugs (drug repurposing/repositioning),
  • Biomedical knowledge mining,
  • Medical informatics. 

Metabolic Engineering and Synthetic B​iology: As for the activities in these domains, the possibility to efficiently alter the behavior of a living cell, usually a microorganism, or to introduce by design a new functionality, is scientifically challenging and exciting. At the same time, development of such a capability for microbes opens many routes to useful biotechnology applications that may lead to scalable carbon capture combined with production of industrially important chemicals or biomass-derived products. This new, engineered phenotype is achieved by insertion of designed molecular constructs into the cell genome. The road to efficient design of such 'new functionality' molecular blocks, requires sophisticated computational methodologies and systems that still have to be developed.  ​