The major synthetic evolutionary transitions

Abstract

Evolution is marked by well-defined events involving profound innovations that are known as 'major evolutionary transitions'. They involve the integration of autonomous elements into a new, higher-level organization whereby the former isolated units interact in novel ways, losing their original autonomy. All major transitions, which include the origin of life, cells, multicellular systems, societies or language (among other examples), took place millions of years ago. Are these transitions unique, rare events? Have they instead universal traits that make them almost inevitable when the right pieces are in place? Are there general laws of evolutionary innovation? In order to approach this problem under a novel perspective, we argue that a parallel class of evolutionary transitions can be explored involving the use of artificial evolutionary experiments where alternative paths to innovation can be explored. These 'synthetic' transitions include, for example, the artificial evolution of multicellular systems or the emergence of language in evolved communicating robots. These alternative scenarios could help us to understand the underlying laws that predate the rise of major innovations and the possibility for general laws of evolved complexity. Several key examples and theoretical approaches are summarized and future challenges are outlined.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

Keywords: artificial life; evolutionary robotics; major transitions; phase transitions; synthetic biology.

Figure 1.

Natural versus synthetic transitions. In the left column (a–d), four instances of observable types of evolutionary novelties are shown. From top to bottom, cells, multicellular systems, a symbiotic association between photosynthetic algae and a sea slug and language, as illustrated by written texts. The two columns on the right illustrate some examples of synthetic counterparts of these examples. These include synthetic cells using a genome reduction strategy (e) or a bottom-up protocell approach (i), evolved (f) and designed (j) multicellular systems, engineered cooperation (g–k) as well as evolved communicating robots and (l) artificial neural networks capable of pattern recognition and language processing.

Figure 2.

Artificial life simulation of a realistic protocell cycle incorporating a compartment, a minimal (single-reaction) metabolism and an information molecule (an eight-base string). Here, the initial aggregate (a) is feeded with lipid precursor droplets (yellow spheres) that coalesce with the initial micelle (b) and are then transformed into new surfactant molecules (c). Information-carrying molecules and precursors are also supplied and get attached to the droplet surface (d), where they can also replicate through a mechanism of template replication. The coupled reactions lead to growth and division (e). Computer renderings courtesy of Rayn Norkus, Bruce Damer and Steen Rasmussen.

Figure 3.

Electronic circuits (a) are the iconic representations of standard computing machines. Brains (b) and computer circuits have been often compared as implementations of hardware systems capable of performing computations. The analogy has some realistic elements (such as common laws for minimization of wiring costs) and obvious drawbacks.