On the Advantages of Variable Length GRNs for the Evolution of Multicellular Developmental Systems

Abstract

Biological genomes have evolved over a period of millions of years and comprise thousands of genes, even for the simplest organisms. However, in nature, only 1-2% of the genes play an active role in creating and maintaining the organism, while the majority are evolutionary fossils. This raises the question of whether a considerably larger number of (partly redundant) genes are required in order to effectively build a functional developmental system, of which, in the final system only a fraction is required for the latter to function. This paper investigates different approaches to creating artificial developmental systems (ADSs) based on variable length gene regulatory networks (GRNs). The GRNs are optimized using an evolutionary algorithm (EA). A comparison is made between the different variable length representations and fixed length representations. It is shown that variable length GRNs can achieve both reducing computational effort during optimization and increasing speed and compactness of the resulting ADS, despite the higher complexity of the encoding required. The results may also improve the understanding of how to effectively model GRN based developmental systems. Taking results of all experiments into account makes it possible to create an overall ranking of the different patterns used as a testbench in terms of their complexity. This ranking may aid to compare related work against. In addition, this allows a detailed assessment of the ADS used and enables the identification of missing mechanisms.Index Terms-Artificial development, computational evolution, evolvability, gene regulatory network (GRN).

CA-NEAT: Evolved Compositional Pattern Producing Networks for Cellular Automata Morphogenesis and Replication

“…Many different kinds of CA encodings have been previously investigated. These include conditionally matching rules [51], [52], [53] where conditions have to be satisfied to determine the next state of a cell, instruction-based development [54], [55] where transition functions are replaced by a program, self-modifying cartesian genetic programming [56] where a variation of genetic programming is used, and variable length gene regulatory networks [57]. Nichele and Tufte [58] investigated an instruction based encoding where genomes could complexify during evolution.…”

The Influence of Cellular Characteristics on the Evolution of Shape Homeostasis

“…The choice of n = 25 reflects this trade-off. It would be possible to use network with variable size [36,25], but for simplicity we use a fixed size.…”