32,604 research outputs found
The impact of cellular characteristics on the evolution of shape homeostasis
The importance of individual cells in a developing multicellular organism is
well known but precisely how the individual cellular characteristics of those
cells collectively drive the emergence of robust, homeostatic structures is
less well understood. For example cell communication via a diffusible factor
allows for information to travel across large distances within the population,
and cell polarisation makes it possible to form structures with a particular
orientation, but how do these processes interact to produce a more robust and
regulated structure? In this study we investigate the ability of cells with
different cellular characteristics to grow and maintain homeostatic structures.
We do this in the context of an individual-based model where cell behaviour is
driven by an intra-cellular network that determines the cell phenotype. More
precisely, we investigated evolution with 96 different permutations of our
model, where cell motility, cell death, long-range growth factor (LGF),
short-range growth factor (SGF) and cell polarisation were either present or
absent. The results show that LGF has the largest positive impact on the
fitness of the evolved solutions. SGF and polarisation also contribute, but all
other capabilities essentially increase the search space, effectively making it
more difficult to achieve a solution. By perturbing the evolved solutions, we
found that they are highly robust to both mutations and wounding. In addition,
we observed that by evolving solutions in more unstable environments they
produce structures that were more robust and adaptive. In conclusion, our
results suggest that robust collective behaviour is most likely to evolve when
cells are endowed with long range communication, cell polarisation, and
selection pressure from an unstable environment
Robust Multi-Cellular Developmental Design
This paper introduces a continuous model for Multi-cellular Developmental
Design. The cells are fixed on a 2D grid and exchange "chemicals" with their
neighbors during the growth process. The quantity of chemicals that a cell
produces, as well as the differentiation value of the cell in the phenotype,
are controlled by a Neural Network (the genotype) that takes as inputs the
chemicals produced by the neighboring cells at the previous time step. In the
proposed model, the number of iterations of the growth process is not
pre-determined, but emerges during evolution: only organisms for which the
growth process stabilizes give a phenotype (the stable state), others are
declared nonviable. The optimization of the controller is done using the NEAT
algorithm, that optimizes both the topology and the weights of the Neural
Networks. Though each cell only receives local information from its neighbors,
the experimental results of the proposed approach on the 'flags' problems (the
phenotype must match a given 2D pattern) are almost as good as those of a
direct regression approach using the same model with global information.
Moreover, the resulting multi-cellular organisms exhibit almost perfect
self-healing characteristics
Self-repair ability of evolved self-assembling systems in cellular automata
Self-repairing systems are those that are able to reconfigure themselves following disruptions to bring them back into a defined normal state. In this paper we explore the self-repair ability of some cellular automata-like systems, which differ from classical cellular automata by the introduction of a local diffusion process inspired by chemical signalling processes in biological development. The update rules in these systems are evolved using genetic programming to self-assemble towards a target pattern. In particular, we demonstrate that once the update rules have been evolved for self-assembly, many of those update rules also provide a self-repair ability without any additional evolutionary process aimed specifically at self-repair
Optimizing Associative Information Transfer within Content-addressable Memory
Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe
Computational Evolutionary Embryogeny
Evolutionary and developmental processes are used to evolve the configurations of 3-D structures in silico to achieve desired performances. Natural systems utilize the combination of both evolution and development processes to produce remarkable performance and diversity. However, this approach has not yet been applied extensively to the design of continuous 3-D load-supporting structures. Beginning with a single artificial cell containing information analogous to a DNA sequence, a structure is grown according to the rules encoded in the sequence. Each artificial cell in the structure contains the same sequence of growth and development rules, and each artificial cell is an element in a finite element mesh representing the structure of the mature individual. Rule sequences are evolved over many generations through selection and survival of individuals in a population. Modularity and symmetry are visible in nearly every natural and engineered structure. An understanding of the evolution and expression of symmetry and modularity is emerging from recent biological research. Initial evidence of these attributes is present in the phenotypes that are developed from the artificial evolution, although neither characteristic is imposed nor selected-for directly. The computational evolutionary development approach presented here shows promise for synthesizing novel configurations of high-performance systems. The approach may advance the system design to a new paradigm, where current design strategies have difficulty producing useful solutions
Optimization in Networks
The recent surge in the network modeling of complex systems has set the stage
for a new era in the study of fundamental and applied aspects of optimization
in collective behavior. This Focus Issue presents an extended view of the state
of the art in this field and includes articles from a large variety of domains
where optimization manifests itself, including physical, biological, social,
and technological networked systems.Comment: Opening article of the CHAOS Focus Issue "Optimization in Networks",
available at http://link.aip.org/link/?CHA/17/2/htmlto
Modeling gravitational instabilities in self-gravitating protoplanetary disks with adaptive mesh refinement techniques
The astonishing diversity in the observed planetary population requires
theoretical efforts and advances in planet formation theories. Numerical
approaches provide a method to tackle the weaknesses of current planet
formation models and are an important tool to close gaps in poorly constrained
areas. We present a global disk setup to model the first stages of giant planet
formation via gravitational instabilities (GI) in 3D with the block-structured
adaptive mesh refinement (AMR) hydrodynamics code ENZO. With this setup, we
explore the impact of AMR techniques on the fragmentation and clumping due to
large-scale instabilities using different AMR configurations. Additionally, we
seek to derive general resolution criteria for global simulations of
self-gravitating disks of variable extent. We run a grid of simulations with
varying AMR settings, including runs with a static grid for comparison, and
study the effects of varying the disk radius. Adopting a marginally stable disk
profile (Q_init=1), we validate the numerical robustness of our model for
different spatial extensions, from compact to larger, extended disks (R_disk =
10, 100 and 300 AU, M_disk ~ 0.05 M_Sun, M_star = 0.646 M_Sun). By combining
our findings from the resolution and parameter studies we find a lower limit of
the resolution to be able to resolve GI induced fragmentation features and
distinct, turbulence inducing clumps. Irrespective of the physical extension of
the disk, topologically disconnected clump features are only resolved if the
fragmentation-active zone of the disk is resolved with at least 100 cells,
which holds as a minimum requirement for all global disk setups. Our
simulations illustrate the capabilities of AMR-based modeling techniques for
planet formation simulations and underline the importance of balanced
refinement settings to reproduce fragmenting structures.Comment: 12 pages, 12 figures; accepted for publication in A&A; for associated
movie files, see http://timlichtenberg.net/publications/gi1
Self-similarity, small-world, scale-free scaling, disassortativity, and robustness in hierarchical lattices
In this paper, firstly, we study analytically the topological features of a
family of hierarchical lattices (HLs) from the view point of complex networks.
We derive some basic properties of HLs controlled by a parameter . Our
results show that scale-free networks are not always small-world, and support
the conjecture that self-similar scale-free networks are not assortative.
Secondly, we define a deterministic family of graphs called small-world
hierarchical lattices (SWHLs). Our construction preserves the structure of
hierarchical lattices, while the small-world phenomenon arises. Finally, the
dynamical processes of intentional attacks and collective synchronization are
studied and the comparisons between HLs and Barab{\'asi}-Albert (BA) networks
as well as SWHLs are shown. We show that degree distribution of scale-free
networks does not suffice to characterize their synchronizability, and that
networks with smaller average path length are not always easier to synchronize.Comment: 26 pages, 8 figure
Highly Optimized Tolerance: Robustness and Power Laws in Complex Systems
We introduce highly optimized tolerance (HOT), a mechanism that connects
evolving structure and power laws in interconnected systems. HOT systems arise,
e.g., in biology and engineering, where design and evolution create complex
systems sharing common features, including (1) high efficiency, performance,
and robustness to designed-for uncertainties, (2) hypersensitivity to design
flaws and unanticipated perturbations, (3) nongeneric, specialized, structured
configurations, and (4) power laws. We introduce HOT states in the context of
percolation, and contrast properties of the high density HOT states with random
configurations near the critical point. While both cases exhibit power laws,
only HOT states display properties (1-3) associated with design and evolution.Comment: 4 pages, 2 figure
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