The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?
Vı́ctor de Lorenzo · PLoS Biology · 2024
Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications.Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles.Will this herald a decline in mechanistic studies?Biology as a scientific and research domain seems to have undergone major breakthroughs and paradigm shifts every time external disciplines have intersected with it.Typically, after a given time of somewhat uneasy coexistence, the community embraces the new conceptual frame and its associated technologies as a lens through which biological phenomena can be (re)interpreted.The happy encounter between biology and chemistry gave birth to biochemistry, enzymology, and metabolism.Much later, the interest of post-war physicists for live systems brought about the onset of molecular biology, which reached its biggest milestones in the elucidation of the DNA double helix and the deciphering of the genetic code.During the many decades dominated by molecular biology and molecular genetics, the emphasis has been on mechanistic understanding of biological phenomena, enabled by rigorous hypothesis-driven approaches imported from physics and formal mathematical logic.These departed from mere trial-and-error approaches that prevailed in previous stages and have enabled all-rational understanding of many key biological processes on the same principles that govern the rest of the material world.This is, after all, the ultimate mission of science as a human endeavour: rational understanding of reality with universal laws and principles.Yet, the notion that by knowing the functioning of specific biological components we can understand the functioning of whole live systems became insufficient in view of the avalanche of data later generated by the plethora of "omics" technologies.Molecular biology reduces the complexity of a given phenomenon to a point where rigorous logic can be applied, experimental results unambiguously interpreted, and conclusions fixed as permanent pieces of knowledge.Yet, for this to happen, there should be a limited number of actors in an experiment.It thus follows that molecular biology is not capable of handling systems with too many components.Another conceptual and technical framework was clearly required.And thus systems biology [1], which largely relies on network theory (ultimately a branch of physics [2]), came about with the motto that "for understanding the whole, one has to study the whole".