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. 2012 Mar;34(3):236-44.
doi: 10.1002/bies.201100144. Epub 2012 Jan 18.

Scientific Discovery as a Combinatorial Optimisation Problem: How Best to Navigate the Landscape of Possible Experiments?

Free PMC article

Scientific Discovery as a Combinatorial Optimisation Problem: How Best to Navigate the Landscape of Possible Experiments?

Douglas B Kell. Bioessays. .
Free PMC article


A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes.


Figure 1
Figure 1
A ‘mind map’ setting out the main contents of this paper. To read it start at ‘12 o'clock’ and read clockwise.
Figure 2
Figure 2
A two-objective optimisation problem, illustrating the non-dominated or Pareto front. In this case we wish to maximise both objectives. Each individual blob is a candidate solution, with the filled ones representing the approximation (based on the examples tested) to the Pareto front.

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