15,954 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
Optimization Aspects of Carcinogenesis
Any process in which competing solutions replicate with errors and numbers of
their copies depend on their respective fitnesses is the evolutionary
optimization process. As during carcinogenesis mutated genomes replicate
according to their respective qualities, carcinogenesis obviously qualifies as
the evolutionary optimization process and conforms to common mathematical
basis. The optimization view accents statistical nature of carcinogenesis
proposing that during it the crucial role is actually played by the allocation
of trials. Optimal allocation of trials requires reliable schemas' fitnesses
estimations which necessitate appropriate, fitness landscape dependent,
statistics of population. In the spirit of the applied conceptual framework,
features which are known to decrease efficiency of any evolutionary
optimization procedure (or inhibit it completely) are anticipated as
"therapies" and reviewed. Strict adherence to the evolutionary optimization
framework leads us to some counterintuitive implications which are, however, in
agreement with recent experimental findings, such as sometimes observed more
aggressive and malignant growth of therapy surviving cancer cells
On the Effectiveness of Genetic Search in Combinatorial Optimization
In this paper, we study the efficacy of genetic algorithms in the context of combinatorial optimization. In particular, we isolate the effects of cross-over, treated as the central component of genetic search. We show that for problems of nontrivial size and difficulty, the contribution of cross-over search is marginal, both synergistically when run in conjunction with mutation and selection, or when run with selection alone, the reference point being the search procedure consisting of just mutation and selection. The latter can be viewed as another manifestation of the Metropolis process. Considering the high computational cost of maintaining a population to facilitate cross-over search, its marginal benefit renders genetic search inferior to its singleton-population counterpart, the Metropolis process, and by extension, simulated annealing. This is further compounded by the fact that many problems arising in practice may inherently require a large number of state transitions for a near-optimal solution to be found, making genetic search infeasible given the high cost of computing a single iteration in the enlarged state-space.NSF (CCR-9204284
Multigame models of innovation in evolutionary economics
We incorporate information measures representing knowledge into an evolutionary model of coevolving firms and markets whereby the growing orderliness of firms potentiates a predictable progression of market exchange innovations which themselves become beneficial only with the growing orderliness of firms. We do this by generalizing Nelson and Winter style evolutionary models which are well suited to the study of entry, exit, and growth dynamics at the level of individual firms or entire industries. The required innovation is to use information measures to impose an order on the routines constituting a firm, and by correlating order with firm profitability, allow the preferential selection of innovations which increase order. In this viewpoint, the coherent mathematical framework provided by information and probability theory describes firm orderliness and variability, as well as all selection operations. This informational approach allows modelling the synergistic interactions between routines in a single firm and between different firms in a general but comprehensive manner, so that we can successfully model and predict innovations specifically focussed on organizational order. In particular, we can predict the coevolution over time of firm organizational complexity and of increasingly sophisticated market exchange mechanisms for routines permitting that increased organizational order. We demonstrate our approach using numerical simulations and analytic techniques exploiting a multigame player environment.Evolution, Knowledge, Markets, Evolutionary dynamics, Games, Multigame Environments
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