Mathematical biology versus computational biology (where am I?)

Over years of practice, the seasoned professor develops a sophisticated understanding of how their research programme fits into the broader scientific endeavour. Earning a PhD takes a long time largely because graduate students have no such map. Even when directed towards promising new pastures, the apprentice is bound to spend much time rediscovering well-trodden ground or getting bogged down in unproductive swamps that more experienced explorers know how to avoid. Only a lucky few wanderers happen upon hidden treasure.

Understanding one’s place in academia involves knowing how its subfields are demarcated. A problem here is that the discipline definitions used by journals or in textbooks don’t necessarily correspond to research communities that go by the same names. This discrepancy struck me recently when I attended ISMB/ECCB, which combines the European Conference on Computational Biology with the flagship meeting of the International Society for Computational Biology (ISCB).

The official ISCB journal PLOS Computational Biology publishes works that “further our understanding of living systems at all scales – from molecules and cells, to patient populations and ecosystems – through the application of computational methods.” [1] This broad scope is consistent with NIH’s definition of computational biology as “The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.” [2]

ISMB/ECCB, on the other hand, has a narrower ambit in two respects. First, the conference focusses on data-driven, statistical methods. Second, almost all participants specialise in interpreting molecular data. Indeed, whereas the “CB” of ECCB stands for computational biology, the “MB” of ISMB is molecular biology. ISCB’s original mission statement placed emphasis “on the role of computing and informatics in advancing molecular biology.” [3] Although those words have since been removed from the mission statement, one needs only to peruse the society’s Communities of Special Interest (COSI) profiles to see that the emphasis remains in practice. Of more than 500 talks at the meeting, only eight mentioned evolution (or a derived word) in their title, of which four were in a special “non-COSI” session.

I don’t at all mean to complain about this particular conference. I attended many excellent ISMB/ECCB talks spanning diverse methods and applications. But it strikes me as worthwhile to examine why communities have formed within particular boundaries, and what we might gain from eroding the divisions. So I drew a diagram:

Screen Shot 2017-08-08 at 18.29.46

The aim of this plot is to help clarify (for junior researchers like me) how scientists applying mathematical and computational methods to biological problems have organised themselves into communities. Of course I’m biassed, and I expect that some aspects of the diagram are demonstrably wrong. I welcome suggestions for improvement and would be happy to post a revised version. But I think the above picture is a useful starting point for discussion.

I’m particularly interested in the white spaces. I don’t doubt there are people developing computational workflows to analyse non-molecular data sets at the tissue, organ, organism, and population scales, but their research community seems to be less prominent than those pertaining to smaller or larger scales. I suppose this is partly because we have better mechanistic understanding at intermediate scales, where systems can be more readily observed and manipulated. Likewise, I’m well aware that mathematical modelling is applied at every biological scale, but (at least based on conference programmes) the mathematical and theoretical biology communities seem to have stronger ties to evolutionary biology, developmental biology, and the study of infectious disease than to molecular biology.

The picture may be changing. I’m fortunate to belong to a Computational Biology Group that uses molecular data to inform agent-based models of tumour evolution, and that uses and advances methods from pure mathematics to strengthen our theoretical grasp of molecular processes. James Sharpe gave a fantastic ISMB/ECCB keynote talk about investigating vertebrate limb development at levels ranging from gene regulatory networks to the physical interactions between cells and tissues. Sharpe conveyed a vision of systems biology not as a subset of computational biology (as narrowly defined) but as a holistic approach to unravelling life’s complexity.

As for myself, I feel most comfortable near the middle of the diagram, though spreading tendrils in each direction to span as many scales and methods as are needed to address the question at hand. So I reckon I’ll keep on attending ISMB/ECCB as well as SMB/ESMTB (mathematical and theoretical biology) and ESEB (evolutionary biology) conferences, and I’ll try to play a part not just in drawing but in redrawing the map.

References

  1. PLOS Computational Journal Information, retrieved 8th August 2017.
  2. Huerta, Michael, et al. (2000) NIH working definition of bioinformatics and computational biology. US National Institute of Health.
  3. History of ISCB, retrieved 8th August 2017.
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Author: Rob Noble

I use mathematical and computational models to investigate evolutionary and ecological systems. I am currently working, in close collaboration with laboratory scientists, on models of cancer evolution and the development of drug resistance. My methods include game theory, analysis of dynamical systems, spatially structured models, and Bayesian inference. During my PhD at the University of Oxford (2009-2013) I used mathematical models, informed by statistical analysis of laboratory data, to understand the immune evasion mechanisms of the malaria parasite Plasmodium falciparum.

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