Open problems in artificial life

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres

Artificial Evolution of Arbitrary Self-Replicating Cellular Automata

Since John von Neumann's seminal work on developing cellular automata models of self-replication, there have been numerous computational studies that have sought to create self-replicating structures or "machines". Cellular automata (CA) has been the most widely used method in these studies, with manual designs yielding a number of specific self-replicating structures. However, it has been found to be very difficult, in general, to design local state-transition rules that, when they operate concurrently in each cell of the cellular space, produce a desired global behavior such as self-replication. This has greatly limited the number of different self-replicating structures designed and studied to date. In this dissertation, I explore the feasibility of overcoming this difficulty by using genetic programming (GP) to evolve novel CA self-replication models. I first formulate an approach to representing structures and rules in cellular automata spaces that is amenable to manipulation by the genetic operations used in GP. Then, using this representation, I demonstrate that it is possible to create a "replicator factory" that provides an unprecedented ability to automatically generate a whole class of new self-replicating structures and that allows one to systematically investigate the properties of replicating structures as one varies the initial configuration, its size, shape, symmetry, and allowable states. This approach is then extended to incorporate multi-objective fitness criteria, resulting in production of diversified replicators. For example, this allows generation of target structures whose complexity greatly exceeds that of the seed structure itself. Finally, the extended multi-objective replicator factory is further generalized into a structure/rule co-evolution model, such that replicators with unspecified seed structures can also be concurrently evolved, resulting in different structure/rule combinations and having the capability of not only replicating but also carrying out a secondary pre-specified task with different strategies. I conclude that GP provides a powerful method for creating CA models of self-replication

A Sequence-to-Function Map for Ribozyme-catalyzed Metabolisms

We introduce a novel genotype-phenotype mapping based on the relation between RNA sequence and its secondary structure for the use in evolutionary studies. Various extensive studies concerning RNA folding in the context of neutral theory yielded insights about properties of the structure space and the mapping itself. We intend to get a better understanding of some of these properties and especially of the evolution of RNA-molecules as well as their effect on the evolution of the entire molecular system. We investigate the constitution of the neutral network and compare our mapping with other artificial approaches using cellular automatons, random boolean networks and others also based on RNA folding. We yield the highest extent, connectivity and evolvability of the underlying neutral network. Further, we successfully apply the mapping in an existing model for the evolution of a ribozyme-catalyzed metabolism

Replicator Dynamics in Protocells

Replicator equations have been studied for three decades as a generic dynamical system modelling replication processes. Here we show how they arise naturally in models of self-replicating polymers and discuss some of their basic properties. We then concentrate on a minimal dynamic model of a protocell by coupling replicating polymers with a growing membrane

A guided tour of asynchronous cellular automata

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We survey the recent research that has been carried out on this topic and present a wide state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat