How does new genetic information arise? Traditional thinking holds that mutation happens by accident and then spreads in the population by either natural selection or random genetic drift. There have been at least two fundamental conceptual problems with imagining an alternative. First, it seemed that the only alternative is a mutation that responds “smartly” to the immediate environment; but in complex multicellulars, it is hard to imagine how this could be implemented. Second, if there were mechanisms of mutation that “knew” what genetic changes would be favored in a given environment, this would have only begged the question of how they acquired that particular knowledge to begin with. This paper offers an alternative that avoids these problems. It holds that mutational mechanisms act on information that is in the genome, based on considerations of simplicity, parsimony, elegance, etc. (which are different than fitness considerations). This simplification process, under the performance pressure exerted by selection, not only leads to the improvement of adaptations but also creates elements that have the capacity to serve in new contexts they were not originally selected for. Novelty, then, arises at the system level from emergent interactions between such elements. Thus, mechanistically driven mutation neither requires Lamarckian transmission nor closes the door on novelty, because the changes it implements interact with one another globally in surprising and beneficial ways. Finally, I argue, for example, that genes used together are fused together; that simplification leads to complexity; and that evolution and learning are conceptually linked.
Bibliographical noteFunding Information:
Acknowledgements I would like to thank Marc Feldman, Avra-ham Korol, Simon Levin, Amos Livnat, Daniel Melamed, Steve Pac-ala, Christos Papadimitriou, Nick Pippenger, Umesh Vazirani, Kim Weaver and three anonymous referees for invaluable comments and conversations during the course of the study. I would like to thank the Department of Evolutionary and Environmental Biology and the Institute of Evolution at the University of Haifa for providing an intellectual environment conducive to the pursuit of this work. I would like to acknowledge financial support from the Miller Institute for Basic Research in Science and from NSF grant 0964033 to Christos Papadimitriou, Division of Computer Science, UC Berkeley, during a formative part of the work in the years 2006–2011, and from the Israel Science Foundation (grant no. 1986/16) during the final stages of the work.
© 2017, The Author(s).
- Gene fusion
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics