281 research outputs found
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
On the Neutrality of Flowshop Scheduling Fitness Landscapes
Solving efficiently complex problems using metaheuristics, and in particular
local searches, requires incorporating knowledge about the problem to solve. In
this paper, the permutation flowshop problem is studied. It is well known that
in such problems, several solutions may have the same fitness value. As this
neutrality property is an important one, it should be taken into account during
the design of optimization methods. Then in the context of the permutation
flowshop, a deep landscape analysis focused on the neutrality property is
driven and propositions on the way to use this neutrality to guide efficiently
the search are given.Comment: Learning and Intelligent OptimizatioN Conference (LION 5), Rome :
Italy (2011
An Evolutionary Reduction Principle for Mutation Rates at Multiple Loci
A model of mutation rate evolution for multiple loci under arbitrary
selection is analyzed. Results are obtained using techniques from Karlin (1982)
that overcome the weak selection constraints needed for tractability in prior
studies of multilocus event models. A multivariate form of the reduction
principle is found: reduction results at individual loci combine topologically
to produce a surface of mutation rate alterations that are neutral for a new
modifier allele. New mutation rates survive if and only if they fall below this
surface - a generalization of the hyperplane found by Zhivotovsky et al. (1994)
for a multilocus recombination modifier. Increases in mutation rates at some
loci may evolve if compensated for by decreases at other loci. The strength of
selection on the modifier scales in proportion to the number of germline cell
divisions, and increases with the number of loci affected. Loci that do not
make a difference to marginal fitnesses at equilibrium are not subject to the
reduction principle, and under fine tuning of mutation rates would be expected
to have higher mutation rates than loci in mutation-selection balance. Other
results include the nonexistence of 'viability analogous, Hardy-Weinberg'
modifier polymorphisms under multiplicative mutation, and the sufficiency of
average transmission rates to encapsulate the effect of modifier polymorphisms
on the transmission of loci under selection. A conjecture is offered regarding
situations, like recombination in the presence of mutation, that exhibit
departures from the reduction principle. Constraints for tractability are:
tight linkage of all loci, initial fixation at the modifier locus, and mutation
distributions comprising transition probabilities of reversible Markov chains.Comment: v3: Final corrections. v2: Revised title, reworked and expanded
introductory and discussion sections, added corollaries, new results on
modifier polymorphisms, minor corrections. 49 pages, 64 reference
Impact of Epistasis and Pleiotropy on Evolutionary Adaptation
Evolutionary adaptation is often likened to climbing a hill or peak. While
this process is simple for fitness landscapes where mutations are independent,
the interaction between mutations (epistasis) as well as mutations at loci that
affect more than one trait (pleiotropy) are crucial in complex and realistic
fitness landscapes. We investigate the impact of epistasis and pleiotropy on
adaptive evolution by studying the evolution of a population of asexual haploid
organisms (haplotypes) in a model of N interacting loci, where each locus
interacts with K other loci. We use a quantitative measure of the magnitude of
epistatic interactions between substitutions, and find that it is an increasing
function of K. When haplotypes adapt at high mutation rates, more epistatic
pairs of substitutions are observed on the line of descent than expected. The
highest fitness is attained in landscapes with an intermediate amount of
ruggedness that balance the higher fitness potential of interacting genes with
their concomitant decreased evolvability. Our findings imply that the synergism
between loci that interact epistatically is crucial for evolving genetic
modules with high fitness, while too much ruggedness stalls the adaptive
process.Comment: 20 pages, 8 figures, plus 10 supporting figure
Language control and parallel recovery of language in individuals with aphasia
Background: The causal basis of the different patterns of language recovery following stroke in bilingual speakers is not well understood. Our approach distinguishes the representation of language from the mechanisms involved in its control. Previous studies have suggested that difficulties in language control can explain selective aphasia in one language as well as pathological switching between languages. Here we test the hypothesis that difficulties in managing and resolving competition will also be observed in those who are equally impaired in both their languages even in the absence of pathological switching.
Aims: To examine difficulties in language control in bilingual individuals with parallel recovery in aphasia and to compare their performance on different types of conflict task.
Methods & procedures: Two right-handed, non-native English-speaking participants who showed parallel recovery of two languages after stroke and a group of non-native English-speaking, bilingual controls described a scene in English and in their first language and completed three explicit conflict tasks. Two of these were verbal conflict tasks: a lexical decision task in English, in which individuals distinguished English words from non-words, and a Stroop task, in English and in their first language. The third conflict task was a non-verbal flanker task.
Outcomes & Results: Both participants with aphasia were impaired in the picture description task in English and in their first language but showed different patterns of impairment on the conflict tasks. For the participant with left subcortical damage, conflict was abnormally high during the verbal tasks (lexical decision and Stroop) but not during the non-verbal flanker task. In contrast, for the participant with extensive left parietal damage, conflict was less abnormal during the Stroop task than the flanker or lexical decision task.
Conclusions: Our data reveal two distinct control impairments associated with parallel recovery. We stress the need to explore the precise nature of control problems and how control is implemented in order to develop fuller causal accounts of language recovery patterns in bilingual aphasia
Red Queen Dynamics with Non-Standard Fitness Interactions
Antagonistic coevolution between hosts and parasites can involve rapid fluctuations of genotype frequencies that are known as Red Queen dynamics. Under such dynamics, recombination in the hosts may be advantageous because genetic shuffling can quickly produce disproportionately fit offspring (the Red Queen hypothesis). Previous models investigating these dynamics have assumed rather simple models of genetic interactions between hosts and parasites. Here, we assess the robustness of earlier theoretical predictions about the Red Queen with respect to the underlying host-parasite interactions. To this end, we created large numbers of random interaction matrices, analysed the resulting dynamics through simulation, and ascertained whether recombination was favoured or disfavoured. We observed Red Queen dynamics in many of our simulations provided the interaction matrices exhibited sufficient ‘antagonicity’. In agreement with previous studies, strong selection on either hosts or parasites favours selection for increased recombination. However, fast changes in the sign of linkage disequilibrium or epistasis were only infrequently observed and do not appear to be a necessary condition for the Red Queen hypothesis to work. Indeed, recombination was often favoured even though the linkage disequilibrium remained of constant sign throughout the simulations. We conclude that Red Queen-type dynamics involving persistent fluctuations in host and parasite genotype frequencies appear to not be an artefact of specific assumptions about host-parasite fitness interactions, but emerge readily with the general interactions studied here. Our results also indicate that although recombination is often favoured, some of the factors previously thought to be important in this process such as linkage disequilibrium fluctuations need to be reassessed when fitness interactions between hosts and parasites are complex
Analysis of cancer metabolism with high-throughput technologies
<p>Abstract</p> <p>Background</p> <p>Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect – a long-standing puzzle in cancer energy metabolism – at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.</p> <p>Results</p> <p>We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (<it>ACO2</it>) in the brain exceeded both citrate synthase (<it>CS</it>) and isocitrate dehydrogenase 2 (<it>IDH2</it>), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (<it>GAPDH</it>) and enolase (<it>ENO</it>) were considerably increased when compared to the brain sample.</p> <p>Conclusions</p> <p>The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.</p
On the expected number of internal equilibria in random evolutionary games with correlated payoff matrix
The analysis of equilibrium points in random games has been of great interest
in evolutionary game theory, with important implications for understanding of
complexity in a dynamical system, such as its behavioural, cultural or
biological diversity. The analysis so far has focused on random games of
independent payoff entries. In this paper, we overcome this restrictive
assumption by considering multi-player two-strategy evolutionary games where
the payoff matrix entries are correlated random variables. Using techniques
from the random polynomial theory we establish a closed formula for the mean
numbers of internal (stable) equilibria. We then characterise the asymptotic
behaviour of this important quantity for large group sizes and study the effect
of the correlation. Our results show that decreasing the correlation among
payoffs (namely, of a strategist for different group compositions) leads to
larger mean numbers of (stable) equilibrium points, suggesting that the system
or population behavioural diversity can be promoted by increasing independence
of the payoff entries. Numerical results are provided to support the obtained
analytical results.Comment: Revision from the previous version; 27 page
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