34,333 research outputs found
Does self-replication imply evolvability?
The most prominent property of life on Earth is its ability to evolve. It is
often taken for granted that self-replication--the characteristic that makes
life possible--implies evolvability, but many examples such as the lack of
evolvability in computer viruses seem to challenge this view. Is evolvability
itself a property that needs to evolve, or is it automatically present within
any chemistry that supports sequences that can evolve in principle? Here, we
study evolvability in the digital life system Avida, where self-replicating
sequences written by hand are used to seed evolutionary experiments. We use 170
self-replicators that we found in a search through 3 billion randomly generated
sequences (at three different sequence lengths) to study the evolvability of
generic rather than hand-designed self-replicators. We find that most can
evolve but some are evolutionarily sterile. From this limited data set we are
led to conclude that evolvability is a likely--but not a guaranteed-- property
of random replicators in a digital chemistry.Comment: 8 pages, 5 figures. To appear in "Advances in Artificial Life":
Proceedings of the 13th European Conference on Artificial Life (ECAL 2015
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
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
Malware "Ecology" Viewed as Ecological Succession: Historical Trends and Future Prospects
The development and evolution of malware including computer viruses, worms,
and trojan horses, is shown to be closely analogous to the process of community
succession long recognized in ecology. In particular, both changes in the
overall environment by external disturbances, as well as, feedback effects from
malware competition and antivirus coevolution have driven community succession
and the development of different types of malware with varying modes of
transmission and adaptability.Comment: 13 pages, 3 figure
Does the Red Queen reign in the kingdom of digital organisms?
In competition experiments between two RNA viruses of equal or almost equal
fitness, often both strains gain in fitness before one eventually excludes the
other. This observation has been linked to the Red Queen effect, which
describes a situation in which organisms have to constantly adapt just to keep
their status quo. I carried out experiments with digital organisms
(self-replicating computer programs) in order to clarify how the competing
strains' location in fitness space influences the Red-Queen effect. I found
that gains in fitness during competition were prevalent for organisms that were
taken from the base of a fitness peak, but absent or rare for organisms that
were taken from the top of a peak or from a considerable distance away from the
nearest peak. In the latter two cases, either neutral drift and loss of the
fittest mutants or the waiting time to the first beneficial mutation were more
important factors. Moreover, I found that the Red-Queen dynamic in general led
to faster exclusion than the other two mechanisms.Comment: 10 pages, 5 eps figure
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