2019/05/27 by Sepehr Ehsani, Ehsani, Sepehr · 2 citations
Biochemistry, Genetics and Molecular Biology · Psychology · #Artificial intelligence #Cognitive science #Computer science #Economics #Epistemology #FOS: Biological sciences #Gene Regulatory Network Analysis #Generative grammar #Hierarchy #Management science #Mechanism (biology) #Microtubule and mitosis dynamics #Other Quantitative Biology (q-bio.OT) #Philosophy #Political science #Protein Structure and Dynamics #Psychology #Subject (documents) #q-bio.OT
paper · pdf · doi:10.48550/arxiv.1905.10916
published in arXiv (Cornell University) (Cornell University) · 38 pages, 3 figures
arxiv created 2019/05/27 · openalex publication_date 2019/05/27 · arxiv updated 2019/05/28 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28
Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic models such as hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world.