Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?

    Research output: Contribution to journalArticlepeer-review

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)236-244
    Number of pages8
    JournalBioEssays : news and reviews in molecular, cellular and developmental biology
    Volume34
    Issue number3
    DOIs
    Publication statusPublished - Mar 2012

    Keywords

    • Automation
    • Epistemology
    • Evolutionary computing
    • Heuristics
    • Scientific discovery

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