title: Universality, complexity and the praxis of biology: Two case studies.
abstract: The phenomenon of biology provides a prime example for a naturally occurring complex system. The approach to this complexity reflects the tension between a reductionist, reverse-engineering stance, and more abstract, systemic ones. Both of us are reductionists, but our observations challenge reductionism, at least the naive version of it. Here we describe the challenge, focusing on two universal characteristics of biological complexity: two-way microscopic-macroscopic degeneracy, and lack of time scale separation within and between levels of organization. These two features and their consequences for the praxis of experimental biology, reflect inherent difficulties in separating the dynamics of any given level of organization from the coupled dynamics of all other levels, including the environment within which the system is embedded. Where these difficulties are not deeply acknowledged, the impacts of fallacies that are inherent to naive reductionism are significant. In an era where technology enables experimental high-resolution access to numerous observables, the challenge faced by the mature reductionist-identification of relevant microscopic variables-becomes more demanding than ever. The demonstrations provided here are taken from two very different biological realizations: populations of microorganisms and populations of neurons, thus making the lesson potentially general.
authors.affiliation: Technion-Israel Institute of Technology, Israel.
authors.forename: Erez
authors.initials: E
authors.lastname: Braun
authors.affiliation: Technion-Israel Institute of Technology, Israel. Electronic address: marom@technion.ac.il.
authors.forename: Shimon
authors.initials: S
authors.lastname: Marom
mesh_terms: D019143:Evolution, Molecular; D053263:Gene Regulatory Networks; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D049490:Systems Biology; D013997:Time Factors; D015003:Yeasts / Q000235:genetics*
keywords: Biology; Complexity; Dynamics; Population; Universality