Computational Biology

2 minute read

Molecular model of protein indicating shape of protein backbone. Image by CSIRO.

Molecular model of protein indicating shape of protein backbone

Recent technological advances have led to new kinds, and large amounts of data in the biological and biomedical sciences – such as images at a very fine scale, DNA sequencing and other measurements at a molecular level. This has created computational challenges in managing, describing and modelling key features using these data. The Computational Biology Hallmark Research Initiative was created to strengthen cross-disciplinary relationships to solve these challenges.

Outcomes

The initiative built and strengthened cross-disciplinary and external collaborations in computational biology. And it raised public awareness and engagement with the computational biology community at the University. The initiative achieved this through:

  • supporting seminar programs, including the Melbourne Integrative Genomics seminar series – this  continues to provide opportunities for local researchers to interact with research leaders from key international and national institutions.
  • providing seed funding for 14 interdisciplinary projects
  • supporting early and mid-career researchers to develop leadership and collaborative skills, through their role as Chief Investigators on seven of the 14 seed funded projects.
  • creating opportunities for researchers to apply to larger funding schemes
  • supporting the new Bachelor of Science, major in Computational Biology and Masters of Science Computational Biology program. This involved the initiative’s steering group members.
  • driving a mentoring workshop for early and mid-career researchers.

The Computational Biology Hallmark Research Initiative was announced in 2014 with funding from the Deputy Vice-Chancellor Research and ran for three years. Professor Edmund Crampin and Dr Andrew Siebel continue as key contacts for the active community of computational biology researchers at the University.

Computational biology uses mathematical models and statistical inference techniques to help understand biological processes. Computational approaches are already prominent in:

  • genomics
  • brain imaging
  • in systems and synthetic biology.

Computational approaches are increasing in importance in many other areas of biology including:

  • immunology and infectious diseases
  • evolutionary and ecological modelling.

The initiative developed a networked approach to strengthening collaborations, particularly engaging researchers from:

  • Faculty of Science
  • Melbourne School of Engineering
  • Faculty of Medicine Dentistry and Health Sciences.

Image: CSIRO (CC BY 3.0)

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