5,997 research outputs found
âThe Action of the Brainâ. Machine Models and Adaptive Functions in Turing and Ashby
Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turingâs and Ashbyâs work on modelling âthe action of the brainâ (letter from Turing to Ashby, 1946) will help to shed light on the seemingly strict symbolic/embodied dichotomy: While it is clear that Turing was committed to formal, computational and Ashby to material, analogue methods of modelling, there is no straightforward mapping of these approaches onto symbol-based AI and embodiment-centered views respectively. Instead, it will be demonstrated that both approaches, starting from a formal core, were at least partly concerned with biological and embodied phenomena, albeit in revealingly distinct ways
A Problem Solving Environment for Modelling Stony Coral Morphogenesis
Apart from experimental and theoretical approaches, computer simulation is an important tool in testing hypotheses about stony coral growth. However, the construction and use of such simulation tools needs extensive computational skills and knowledge that is not available to most research biologists. Problem solving environments (PSEs) aim to provide a framework that hides implementation details and allows the user to formulate and analyse a problem in the language of the subject area. We have developed a prototypical PSE to study the morphogenesis of corals using a multi-model approach. In this paper we describe the design and implementation of this PSE, in which simulations of the coral's shape and its environment have been combined. We will discuss the relevance of our results for the future development of PSEs for studying biological growth and morphogenesis
Mathematical models for chemotaxis and their applications in self-organisation phenomena
Chemotaxis is a fundamental guidance mechanism of cells and organisms,
responsible for attracting microbes to food, embryonic cells into developing
tissues, immune cells to infection sites, animals towards potential mates, and
mathematicians into biology. The Patlak-Keller-Segel (PKS) system forms part of
the bedrock of mathematical biology, a go-to-choice for modellers and analysts
alike. For the former it is simple yet recapitulates numerous phenomena; the
latter are attracted to these rich dynamics. Here I review the adoption of PKS
systems when explaining self-organisation processes. I consider their
foundation, returning to the initial efforts of Patlak and Keller and Segel,
and briefly describe their patterning properties. Applications of PKS systems
are considered in their diverse areas, including microbiology, development,
immunology, cancer, ecology and crime. In each case a historical perspective is
provided on the evidence for chemotactic behaviour, followed by a review of
modelling efforts; a compendium of the models is included as an Appendix.
Finally, a half-serious/half-tongue-in-cheek model is developed to explain how
cliques form in academia. Assumptions in which scholars alter their research
line according to available problems leads to clustering of academics and the
formation of "hot" research topics.Comment: 35 pages, 8 figures, Submitted to Journal of Theoretical Biolog
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
Numerical analysis of a mechanotransduction dynamical model reveals homoclinic bifurcations of extracellular matrix mediated oscillations of the mesenchymal stem cell fate
We perform one and two-parameter numerical bifurcation analysis of a
mechanotransduction model approximating the dynamics of mesenchymal stem cell
differentiation into neurons, adipocytes, myocytes and osteoblasts. For our
analysis, we use as bifurcation parameters the stiffness of the extracellular
matrix and parameters linked with the positive feedback mechanisms that
up-regulate the production of the YAP/TAZ transcriptional regulators (TRs) and
the cell adhesion area. Our analysis reveals a rich nonlinear behaviour of the
cell differentiation including regimes of hysteresis and multistability, stable
oscillations of the effective adhesion area, the YAP/TAZ TRs and the
PPAR receptors associated with the adipogenic fate, as well as
homoclinic bifurcations that interrupt relatively high-amplitude oscillations
abruptly. The two-parameter bifurcation analysis of the Andronov-Hopf points
that give birth to the oscillating patterns predicts their existence for soft
extracellular substrates (), a regime that favours the neurogenic and
the adipogenic cell fate. Furthermore, in these regimes, the analysis reveals
the presence of homoclinic bifurcations that result in the sudden loss of the
stable oscillations of the cell-substrate adhesion towards weaker adhesion and
high expression levels of the gene encoding Tubulin beta-3 chain, thus
favouring the phase transition from the adipogenic to the neurogenic fate
Differentiated cell behavior: a multiscale approach using measure theory
This paper deals with the derivation of a collective model of cell
populations out of an individual-based description of the underlying physical
particle system. By looking at the spatial distribution of cells in terms of
time-evolving measures, rather than at individual cell paths, we obtain an
ensemble representation stemming from the phenomenological behavior of the
single component cells. In particular, as a key advantage of our approach, the
scale of representation of the system, i.e., microscopic/discrete vs.
macroscopic/continuous, can be chosen a posteriori according only to the
spatial structure given to the aforesaid measures. The paper focuses in
particular on the use of different scales based on the specific functions
performed by cells. A two-population hybrid system is considered, where cells
with a specialized/differentiated phenotype are treated as a discrete
population of point masses while unspecialized/undifferentiated cell aggregates
are represented with a continuous approximation. Numerical simulations and
analytical investigations emphasize the role of some biologically relevant
parameters in determining the specific evolution of such a hybrid cell system.Comment: 25 pages, 6 figure
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
This article deals with the links between the enaction paradigm and
artificial intelligence. Enaction is considered a metaphor for artificial
intelligence, as a number of the notions which it deals with are deemed
incompatible with the phenomenal field of the virtual. After explaining this
stance, we shall review previous works regarding this issue in terms of
artifical life and robotics. We shall focus on the lack of recognition of
co-evolution at the heart of these approaches. We propose to explicitly
integrate the evolution of the environment into our approach in order to refine
the ontogenesis of the artificial system, and to compare it with the enaction
paradigm. The growing complexity of the ontogenetic mechanisms to be activated
can therefore be compensated by an interactive guidance system emanating from
the environment. This proposition does not however resolve that of the
relevance of the meaning created by the machine (sense-making). Such
reflections lead us to integrate human interaction into this environment in
order to construct relevant meaning in terms of participative artificial
intelligence. This raises a number of questions with regards to setting up an
enactive interaction. The article concludes by exploring a number of issues,
thereby enabling us to associate current approaches with the principles of
morphogenesis, guidance, the phenomenology of interactions and the use of
minimal enactive interfaces in setting up experiments which will deal with the
problem of artificial intelligence in a variety of enaction-based ways
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