On the origin of synthetic life: Attribution of output to a particular algorithm

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin\u27s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples\u27 origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers

On the origin of synthetic life: Attribution of output to a particular algorithm

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin\u27s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples\u27 origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers

Ab Initio Modeling of Ecosystems with Artificial Life

Artificial Life provides the opportunity to study the emergence and evolution of simple ecosystems in real time. We give an overview of the advantages and limitations of such an approach, as well as its relation to individual-based modeling techniques. The Digital Life system Avida is introduced and prospects for experiments with ab initio evolution (evolution "from scratch"), maintenance, as well as stability of ecosystems are discussed.Comment: 13 pages, 2 figure

Computing phylogenies by comparing biosequences following principles of traditional systematics

Füllen G. Computing phylogenies by comparing biosequences following principles of traditional systematics. Bielefeld (Germany): Bielefeld University; 2000.Phylogeny estimation, that is the inference of the evolutionary history of the various life forms (species) on earth, is a widely studied problem that is not yet solved to satisfaction. Studying the strengths and weaknesses of current methods that work on biosequence data, branch attraction phenomena due to unequal amounts of evolutionary change in different parts of the phylogeny are one major problem, placing the species that evolved fast in one part of the phylogenetic tree, and the species that evolved slowly in the other. We improve the current state of the art by describing a way to avoid the attraction of species that evolved slowly, and hence share old ("symplesiomorphic") character states. These leftover character states have "eroded" away in the other species. They are detected using a calibrated comparison with an outgroup, and contrasted with shared novel ("synapomorphic") character states that testify the exclusive common heritage of a subset of the species. Torn apart, these shared novelties indicate conflict in a split of all species considered, and only the split at the root of the phylogenetic tree cannot have such conflict. Therefore, we can work top-down, by heuristically searching for a minimum-conflict split, and tackling the resulting two subsets in the same way. This application of the divide-and-conquer principle, together with an intelligent search for mininum-conflict splits based on the exchange of species that carry the conflict, results in a fast, simple and transparent phylogeny estimation algorithm. The algorithm, called "minimum conflict phylogeny estimation" (MCOPE), is validated intensively using both real and artificial data

Biology of Applied Digital Ecosystems

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the 'ecological succession' (development).Comment: 9 pages, 4 figure, conferenc

Is there something like bio-dynamic breeding?

In this paper the differentiation made within the concept of naturalness will be used to explain the different approaches in breeding within the organic movement. The differentiation of the concept will result into a framework, which will be used as a tool to describe and explain the elements of bio-dynamic animal breeding. Bio-dynamics is a practical result out of the anthroposophical worldview. Bio-dynamics developed its own way to judge about relationships in the physical world, used its own language to express the holistic approach in agriculture and focused on additional relationships within the world

In silico transitions to multicellularity

The emergence of multicellularity and developmental programs are among the major problems of evolutionary biology. Traditionally, research in this area has been based on the combination of data analysis and experimental work on one hand and theoretical approximations on the other. A third possibility is provided by computer simulation models, which allow to both simulate reality and explore alternative possibilities. These in silico models offer a powerful window to the possible and the actual by means of modeling how virtual cells and groups of cells can evolve complex interactions beyond a set of isolated entities. Here we present several examples of such models, each one illustrating the potential for artificial modeling of the transition to multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to multicellular life (Springer