149 research outputs found
Origin of life in a digital microcosm
While all organisms on Earth descend from a common ancestor, there is no
consensus on whether the origin of this ancestral self-replicator was a one-off
event or whether it was only the final survivor of multiple origins. Here we
use the digital evolution system Avida to study the origin of self-replicating
computer programs. By using a computational system, we avoid many of the
uncertainties inherent in any biochemical system of self-replicators (while
running the risk of ignoring a fundamental aspect of biochemistry). We
generated the exhaustive set of minimal-genome self-replicators and analyzed
the network structure of this fitness landscape. We further examined the
evolvability of these self-replicators and found that the evolvability of a
self-replicator is dependent on its genomic architecture. We studied the
differential ability of replicators to take over the population when competed
against each other (akin to a primordial-soup model of biogenesis) and found
that the probability of a self-replicator out-competing the others is not
uniform. Instead, progenitor (most-recent common ancestor) genotypes are
clustered in a small region of the replicator space. Our results demonstrate
how computational systems can be used as test systems for hypotheses concerning
the origin of life.Comment: 20 pages, 7 figures. To appear in special issue of Philosophical
Transactions of the Royal Society A: Re-Conceptualizing the Origins of Life
from a Physical Sciences Perspectiv
From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics
Understanding how genotypes map onto phenotypes, fitness, and eventually
organisms is arguably the next major missing piece in a fully predictive theory
of evolution. We refer to this generally as the problem of the
genotype-phenotype map. Though we are still far from achieving a complete
picture of these relationships, our current understanding of simpler questions,
such as the structure induced in the space of genotypes by sequences mapped to
molecular structures, has revealed important facts that deeply affect the
dynamical description of evolutionary processes. Empirical evidence supporting
the fundamental relevance of features such as phenotypic bias is mounting as
well, while the synthesis of conceptual and experimental progress leads to
questioning current assumptions on the nature of evolutionary dynamics-cancer
progression models or synthetic biology approaches being notable examples. This
work delves into a critical and constructive attitude in our current knowledge
of how genotypes map onto molecular phenotypes and organismal functions, and
discusses theoretical and empirical avenues to broaden and improve this
comprehension. As a final goal, this community should aim at deriving an
updated picture of evolutionary processes soundly relying on the structural
properties of genotype spaces, as revealed by modern techniques of molecular
and functional analysis.Comment: 111 pages, 11 figures uses elsarticle latex clas
Evolving hierarchical visually guided neural network agents to investigate complex interactions.
A complex system is a system with a large number of interacting components without any mechanism for central control that displays self organisation. Understanding how these interactions affect the overall behaviour of a system is of great interest to science. Indeed, researchers use a wide variety of models to investigate complex systems. The problem with most models is that they disregard the hierarchical nature of complex systems: they ignore the fact that components of real world systems tend to be complex systems as well. This prevents researchers from investigating the interactions taking place between the lower and the higher levels of the model which may be crucial in order to gain a full understanding of the examined phenomena and of complex systems in general. Therefore, this thesis introduces Mosaic World, a multi-agent model for the purpose of investigating interactions (focusing on 'complex' multilevel interactions) within a hierarchical complex system, in addition to other computational and biological hypotheses. Mosaic World comprises a population of evolving neural network agents that inhabit a changing visual environment. By analysing the interactions that occur within Mosaic World, this thesis demonstrates the importance of incorporating hierarchical complexity into a model, and contributes to our understanding of hierarchical complex systems by showing how selective pressures cause differentiation across levels. Additionally, the study of multilevel interactions is used to probe several hypotheses and provides the following contributions among others: Analysis of agent evolvability as affected by the usage of different types of structural mutations in the evolutionary process. Demonstration that agents controlled by modular neural networks are fitter than agents that are controlled by non-modular neural networks the improvement in fitness occurs through specialisation of modules. Empirical support for a biological theory suggesting that colour vision evolved as a method of dealing with ambiguous stimuli
Most Networks in Wagner's Model Are Cycling
In this paper we study a model of gene networks introduced by Andreas Wagner in the 1990s that has been used extensively to study the evolution of mutational robustness. We investigate a range of model features and parameters and evaluate the extent to which they influence the probability that a random gene network will produce a fixed point steady state expression pattern. There are many different types of models used in the literature, (discrete/continuous, sparse/dense, small/large network) and we attempt to put some order into this diversity, motivated by the fact that many properties are qualitatively the same in all the models. Our main result is that random networks in all models give rise to cyclic behavior more often than fixed points. And although periodic orbits seem to dominate network dynamics, they are usually considered unstable and not allowed to survive in previous evolutionary studies. Defining stability as the probability of fixed points, we show that the stability distribution of these networks is highly robust to changes in its parameters. We also find sparser networks to be more stable, which may help to explain why they seem to be favored by evolution. We have unified several disconnected previous studies of this class of models under the framework of stability, in a way that had not been systematically explored before
A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA
Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging.
This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
Emergent Behaviors from A Cellular Automaton Model for Invasive Tumor Growth in Heterogeneous Microenvironments
Understanding tumor invasion and metastasis is of crucial importance for both
fundamental cancer research and clinical practice. In vitro experiments have
established that the invasive growth of malignant tumors is characterized by
the dendritic invasive branches composed of chains of tumor cells emanating
from the primary tumor mass. The preponderance of previous tumor simulations
focused on non-invasive (or proliferative) growth. The formation of the
invasive cell chains and their interactions with the primary tumor mass and
host microenvironment are not well understood. Here, we present a novel
cellular automaton (CA) model that enables one to efficiently simulate invasive
tumor growth in a heterogeneous host microenvironment. By taking into account a
variety of microscopic-scale tumor-host interactions, including the short-range
mechanical interactions between tumor cells and tumor stroma, degradation of
extracellular matrix by the invasive cells and oxygen/nutrient gradient driven
cell motions, our CA model predicts a rich spectrum of growth dynamics and
emergent behaviors of invasive tumors. Besides robustly reproducing the salient
features of dendritic invasive growth, such as least resistance and intrabranch
homotype attraction, we also predict nontrivial coupling of the growth dynamics
of the primary tumor mass and the invasive cells. In addition, we show that the
properties of the host microenvironment can significantly affect tumor
morphology and growth dynamics, emphasizing the importance of understanding the
tumor-host interaction. The capability of our CA model suggests that
well-developed in silico tools could eventually be utilized in clinical
situations to predict neoplastic progression and propose individualized optimal
treatment strategies.Comment: 30 pages, 10 figures, 4 tables; to be appear in PLoS Comput. Bio
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