Abstract
How complex traits arise within organisms over evolutionary time is an important question that has relevance both to the understanding of biological systems and to the design of bio-inspired computing systems. This paper investigates the process of acquiring complex traits within epiNet, a recurrent connectionist architecture capable of adapting its topology during execution. Inspired by the biological processes of gene regulation and epigenetics, epiNet captures biological organisms’ ability to alter their regulatory topologies according to environmental stimulus. By applying epiNet to a series of computational tasks, each requiring a range of complex behaviours to solve, and capturing the evolutionary process in detail, we can show not only how the physical structure of epiNet changed when acquiring complex traits, but also how these changes in physical structure affected its dynamic behaviour. This is facilitated by using a lightweight optimisation method which makes minor iterative changes to the network structure so that when complex traits emerge for the first time, a direct lineage can be observed detailing exactly how they evolved. From this we can build an understanding of how complex traits evolve and which regulatory environments best allow for the emergence of these complex traits, pointing us towards computational models that allow more swift and robust acquisition of complex traits when optimised in an evolutionary computing setting.
Highlights
Genetic networks are the fundamental systems through which biological cells regulate their function and development, and this realisation has promoted a sustained effort to understand genetic networks through computational modelling and simulation (Hasty et al, 2001; De Jong, 2002; Hecker et al, 2009; Chen et al, 2010; Le Novère, 2015; Akutsu, 2016)
For the two control tasks, the objective is to optimise an epiNet instance so that it functions as a closed loop controller which is capable of guiding the dynamics of the simulation in a specified manner
In this paper we have investigated how complex behaviours arise within EpiNet, a form of artificial genetic network that captures the important role of biological epigenetic processes such as chromatin modification, allowing for dynamical topological modification during execution
Summary
Genetic networks are the fundamental systems through which biological cells regulate their function and development, and this realisation has promoted a sustained effort to understand genetic networks through computational modelling and simulation (Hasty et al, 2001; De Jong, 2002; Hecker et al, 2009; Chen et al, 2010; Le Novère, 2015; Akutsu, 2016). We consider another group of modelling approaches which take a quite different approach, using evolutionary algorithms (or other metaheuristics) to optimise genetic network models so that they carry out designated computational behaviours (Banzhaf, 2003; Lones, 2016) These behaviours vary from the relatively simple, such as the implementation of logic functions (Bull and Preen, 2009), to computationally challenging, such as controlling the movements of robots through complex environments (Taylor, 2004; Trefzer et al, 2010; Joachimczak et al, 2012; Fuente et al, 2013; Sanchez and Cussat-Blanc, 2014).
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