486 research outputs found
Surfaces, depths and hypercubes: Meyerholdian scenography and the fourth dimension
An appreciation of Meyerhold’s engagement with theatrical space is fundamental to understanding his directorial and pedagogic practice. This article begins by establishing Meyerhold’s theoretical and practical engagement with theatre as a fundamentally scenographic process, arguing for a reconceptualisation of the director as ‘director-scenographer’. Focusing on the construction of depth and surface in Meyerholdian theatre, the article goes on to identify trends in the director’s approach to space, with an emphasis on the de-naturalisation of depth on stage. This denaturalisation is seen as taking three forms: the rejection of depth as a prerequisite in theatrical space, the acknowledgement of the two-dimensional surface as surface, and the restructuring of depth space into a series of restricted planes. The combination of these trends indicates a consistent and systematic process of experimentation in Meyerhold’s work. In addition, this emphasis on depth and surface, and the interaction between the two, also highlights the contextualisation of Meyerhold’s practice within the visual, philosophical and scientific culture of the early twentieth century, echoing the innovations in n-dimensional geometry and particularly, the model of the fourth spatial dimension seen in the work of Russian philosopher P. D. Ouspensky
Exact Results for Amplitude Spectra of Fitness Landscapes
Starting from fitness correlation functions, we calculate exact expressions
for the amplitude spectra of fitness landscapes as defined by P.F. Stadler [J.
Math. Chem. 20, 1 (1996)] for common landscape models, including Kauffman's
NK-model, rough Mount Fuji landscapes and general linear superpositions of such
landscapes. We further show that correlations decaying exponentially with the
Hamming distance yield exponentially decaying spectra similar to those reported
recently for a model of molecular signal transduction. Finally, we compare our
results for the model systems to the spectra of various experimentally measured
fitness landscapes. We claim that our analytical results should be helpful when
trying to interpret empirical data and guide the search for improved fitness
landscape models.Comment: 13 pages, 5 figures; revised and final versio
Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding
The uniform sampling of convex polytopes is an interesting computational
problem with many applications in inference from linear constraints, but the
performances of sampling algorithms can be affected by ill-conditioning. This
is the case of inferring the feasible steady states in models of metabolic
networks, since they can show heterogeneous time scales . In this work we focus
on rounding procedures based on building an ellipsoid that closely matches the
sampling space, that can be used to define an efficient hit-and-run (HR) Markov
Chain Monte Carlo. In this way the uniformity of the sampling of the convex
space of interest is rigorously guaranteed, at odds with non markovian methods.
We analyze and compare three rounding methods in order to sample the feasible
steady states of metabolic networks of three models of growing size up to
genomic scale. The first is based on principal component analysis (PCA), the
second on linear programming (LP) and finally we employ the lovasz ellipsoid
method (LEM). Our results show that a rounding procedure is mandatory for the
application of the HR in these inference problem and suggest that a combination
of LEM or LP with a subsequent PCA perform the best. We finally compare the
distributions of the HR with that of two heuristics based on the Artificially
Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good
agreement with the results of the HR for the small network, while on genome
scale models present inconsistencies.Comment: Replacement with major revision
Evolutionary accessibility of mutational pathways
Functional effects of different mutations are known to combine to the total
effect in highly nontrivial ways. For the trait under evolutionary selection
(`fitness'), measured values over all possible combinations of a set of
mutations yield a fitness landscape that determines which mutational states can
be reached from a given initial genotype. Understanding the accessibility
properties of fitness landscapes is conceptually important in answering
questions about the predictability and repeatability of evolutionary
adaptation. Here we theoretically investigate accessibility of the globally
optimal state on a wide variety of model landscapes, including landscapes with
tunable ruggedness as well as neutral `holey' landscapes. We define a
mutational pathway to be accessible if it contains the minimal number of
mutations required to reach the target genotype, and if fitness increases in
each mutational step. Under this definition accessibility is high, in the sense
that at least one accessible pathwayexists with a substantial probability that
approaches unity as the dimensionality of the fitness landscape (set by the
number of mutational loci) becomes large. At the same time the number of
alternative accessible pathways grows without bound. We test the model
predictions against an empirical 8-locus fitness landscape obtained for the
filamentous fungus \textit{Aspergillus niger}. By analyzing subgraphs of the
full landscape containing different subsets of mutations, we are able to probe
the mutational distance scale in the empirical data. The predicted effect of
high accessibility is supported by the empirical data and very robust, which we
argue to reflect the generic topology of sequence spaces.Comment: 16 pages, 4 figures; supplementary material available on reques
Coastal Tropical Convection in a Stochastic Modeling Framework
Recent research has suggested that the overall dependence of convection near
coasts on large-scale atmospheric conditions is weaker than over the open ocean
or inland areas. This is due to the fact that in coastal regions convection is
often supported by meso-scale land-sea interactions and the topography of
coastal areas. As these effects are not resolved and not included in standard
cumulus parametrization schemes, coastal convection is among the most poorly
simulated phenomena in global models. To outline a possible parametrization
framework for coastal convection we develop an idealized modeling approach and
test its ability to capture the main characteristics of coastal convection. The
new approach first develops a decision algorithm, or trigger function, for the
existence of coastal convection. The function is then applied in a stochastic
cloud model to increase the occurrence probability of deep convection when
land-sea interactions are diagnosed to be important. The results suggest that
the combination of the trigger function with a stochastic model is able to
capture the occurrence of deep convection in atmospheric conditions often found
for coastal convection. When coastal effects are deemed to be present the
spatial and temporal organization of clouds that has been documented form
observations is well captured by the model. The presented modeling approach has
therefore potential to improve the representation of clouds and convection in
global numerical weather forecasting and climate models.Comment: Manuscript submitted for publication in Journal of Advances in
Modeling Earth System
Quantitative analyses of empirical fitness landscapes
The concept of a fitness landscape is a powerful metaphor that offers insight
into various aspects of evolutionary processes and guidance for the study of
evolution. Until recently, empirical evidence on the ruggedness of these
landscapes was lacking, but since it became feasible to construct all possible
genotypes containing combinations of a limited set of mutations, the number of
studies has grown to a point where a classification of landscapes becomes
possible. The aim of this review is to identify measures of epistasis that
allow a meaningful comparison of fitness landscapes and then apply them to the
empirical landscapes to discern factors that affect ruggedness. The various
measures of epistasis that have been proposed in the literature appear to be
equivalent. Our comparison shows that the ruggedness of the empirical landscape
is affected by whether the included mutations are beneficial or deleterious and
by whether intra- or intergenic epistasis is involved. Finally, the empirical
landscapes are compared to landscapes generated with the Rough Mt.\ Fuji model.
Despite the simplicity of this model, it captures the features of the
experimental landscapes remarkably well.Comment: 24 pages, 5 figures; to appear in Journal of Statistical Mechanics:
Theory and Experimen
Neutral Evolution of Mutational Robustness
We introduce and analyze a general model of a population evolving over a
network of selectively neutral genotypes. We show that the population's limit
distribution on the neutral network is solely determined by the network
topology and given by the principal eigenvector of the network's adjacency
matrix. Moreover, the average number of neutral mutant neighbors per individual
is given by the matrix spectral radius. This quantifies the extent to which
populations evolve mutational robustness: the insensitivity of the phenotype to
mutations. Since the average neutrality is independent of evolutionary
parameters---such as, mutation rate, population size, and selective
advantage---one can infer global statistics of neutral network topology using
simple population data available from {\it in vitro} or {\it in vivo}
evolution. Populations evolving on neutral networks of RNA secondary structures
show excellent agreement with our theoretical predictions.Comment: 7 pages, 3 figure
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