The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the genetic code, cells, symbiosis, multicellularity and programmed death up to the emergence of non-genetic information, sight, language or even consciousness. 

Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work,  theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology and advanced simulation modelling techniques, novel perspectives to this problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. At the CSL we want to build a general synthesis of ideas at crossroads between artificial life, synthetic biology and evolutionary robotics.


The list of Major Transitions (MTS) differs from author to author. In its original formulation, as proposed by Jihn Maynard Smith and Eörs Szathmáry, major transitions shared a common feature: they were associated to different kinds of replicators, and some essential properties where considered, particularly in relation to how groups of "smaller" entities interact to give new qualitative functional properties through evolution or because new forms of information transmission arise. In this way, innovations associated to these new properties provide some kind of adaptive advantage to the new, more complex replicators. The problem of major transitions is thus deeply connected to the problem of how complexity arises in evolution. 

How do innovations arise? To answer this question, scientists used available genetic, molecular and anatomic evidence along with phylogenetic analyses, along with theoretical (most game-theoretic) models. But there is actually a parallel path that not only adds useful information to understanding how innovations occur. It also provides a way to explore the old question concerning the role played by contingency versus convergence. It allows us to recreate old biological events, but also exploring the nature of alternative possibilities. Thanks to different ways to approach MTS using physical, biological, simulated and engineered paths to building life in silico and In vivo, we can actually explore the requirements needed to move across complexity thresholds characteristic of the known MTS. By studying them, we find that they not only provide a framework to address classical problem, but also an arena where other issues, such as how difficult are some transitions to occur or whether new transitions might actually exist, can be explored.