Make hay while the sun shines

The best known secret for successful collaborations with computational biologists

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Computational biology enables and often drives biomedical research. The contribution of computational researchers may involve “routine” data analysis for supporting fundamental research, techniques for generating and testing complex biological hypotheses, and approaches to diagnosing diseases or guiding their treatment.

The journal Cell Systems recently asked 15 top researchers: “What Is the Key Best Practice for Collaborating with a Computational Biologist?” Answers to this question are crucial because biomedical research significantly relies on inter-disciplinary cooperation.

Although all of the interviewed researchers provided a good variety of insightful and practical advice, a unifying recommendation stood out: Get computational biologists involved in your research as early as possible. Moreover, it is necessary to maintain continuous, evolving interactions.

The benefits of this approach are numerous, among them:

Sound research.  Early and iterative interactions with computational researchers allow the selection of suitable methodologies for generating and analyzing data. This also depends on a solid understanding of the scientific question being addressed, the limitations of existing experimental and computational techniques, and the expectations of the different research stakeholders.

Trust building. Frequent interactions based on openness and mutual respect facilitate the development of sustainable relations. This in turn creates the conditions for research environments where collaborators feel welcome to ask questions, try out new ideas and fail if necessary.

Unexpected, exciting journeys. Bringing computational researchers to the early stages of biomedical research may offer new, unanticipated ways to look at problems. Recurrent conversations may not only enrich the generation of ideas for designing and conducting a study, but also it may result in the re-framing of questions and identification of innovative applications.

Value for money. Many projects crash or waste financial resources because they begin with the generation of incorrect, highly-noisy or insufficient data for a particular type of analysis. Without the early involvement of computational researchers, opportunities for the optimal use of technologies in specific research contexts may also be missed.

These benefits can be fully realized through a strong commitment to life-long learning, a passion for tackling complex problems and the necessary doses of intellectual humility.

The take-outs from “What Is the Key Best Practice for Collaborating with a Computational Biologist?” should also be applied by computational researchers. For example, if you have an idea for a new algorithm or software application, try to obtain the input from biologists or clinical researchers as early as possible. As above, this may lead to tangible benefits in the short and longer terms, and at least new learning opportunities will arise.

These are exciting and challenging times for biomedical research. There is no time for outdated science management styles and marinated egos. If researchers do not make the most of the collaboration opportunities available, they risk missing bigger future opportunities for the benefit of society.

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