The Future of Computational Biology — #CBSymp16

What is computational biology? Where do we go from here?

These questions and more were addressed in today’s panels “Biggest challenges and greatest opportunities in Computational Biology over the next 10 years” and “How computational biology, and computing in general, will affect human health”. What followed was a fascinating discourse on the strengths and weaknesses of the field and where those strengths can be applied to basic research, human health, and the environment.

The morning panel included: Maricel Kann (UMBC), Feilim Mac Gabhann (Johns Hopkins University), Costas D. Maranas (Penn State), and was moderated by Steven Brenner (UC-Berkley). Here are some of the questions that came up:

1. Computational biology is going to be key for driving the integration of molecular data and clinical data towards personalised medicine. How do we take advantage of the vast amounts of biological data and modelling that exists and apply it to human disease states and treatments, while considering human variability?
2. In a similar vein, how can computational biology facilitate the development of research technology (e.g., high resolution microscopy, single cell analysis, genome editing)? New tools can give us key insights into where experimental intervention is best, whether for targeting pathways for medical treatment or in managing microbial pathogens for biofuel production.
3. Is it advisable to try and adopt a common platform? One advantage to this could be the dissemination of modelling data to medical or environmental application (by non-computational researchers). A common language could help users better understand details of how a given model works, or perhaps provide a benchmark for comparing how different models perform.
4. On the other hand, is it reasonable to ask scientists to restrict the environment in which they can write their code? Would it rather be more fair to develop more user-friendly, interactive models so other researchers can test and run the model themselves? Or perhaps that is undue burden on the developers, potentially delaying publication timelines. But then, maybe the traditional publication is not the optimal format! Finally, what is the role of the NIH to regulate this as part of the concern over reproducibility in science?
5. How do we talk to the general public about computational biology? How can we get more scientists — especially experimentalists and clinicians — aware of the approaches computational biology has to offer?

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Of course, there were far more questions than answers!

At the end of the day, we revisited these thoughts in another panel with Kim ‘Avrama’ Blackwell (GMU), Lilia Iakoucheva (UCSD), Jeffrey J. Saucerman (UVA), moderated by Mark Gerstein (Yale).

Many of the topics that came up were an extension of the morning’s discussion of how computational biology impacts medicine. In particular, the panelists mentioned imaging data sets (e.g., Magnetic Resonance Imaging, MRI) and whole genome sequencing data sets. Not only is the challenge to deal with ‘big data’ sets, but what is key is finding a way to integrate the data sets, towards use in the clinical setting (e.g., for classifying patients), or in drug discovery. Ultimately, this brings up another limitation, which is how this data is stored and shared.

There were several closing comments, from the organizers, but in particular I was happy to hear Michael Gottesman’s (NIH Deputy Director for Intramural Research and Chief of the Laboratory of Cell Biology at the Center for Cancer Research of the National Cancer Institute) vision for the future. Biology is in a period of change, he says. We are in the era of big data. He talked about the steps that the NIH Intramural Program is taking to make sure that there is integration of mathematical and computational science with biological research happening at the NIH. It was clear that he finds the hiring of individuals with mathematical backgrounds and the fostering of collaborations between experimentalists and computational scientists as a key component of the advancement of medical research at the NIH.

Views expressed are not necessarily those of PLOS.