67 research outputs found
Canalization and control in automata networks: body segmentation in Drosophila melanogaster
We present schema redescription as a methodology to characterize canalization
in automata networks used to model biochemical regulation and signalling. In
our formulation, canalization becomes synonymous with redundancy present in the
logic of automata. This results in straightforward measures to quantify
canalization in an automaton (micro-level), which is in turn integrated into a
highly scalable framework to characterize the collective dynamics of
large-scale automata networks (macro-level). This way, our approach provides a
method to link micro- to macro-level dynamics -- a crux of complexity. Several
new results ensue from this methodology: uncovering of dynamical modularity
(modules in the dynamics rather than in the structure of networks),
identification of minimal conditions and critical nodes to control the
convergence to attractors, simulation of dynamical behaviour from incomplete
information about initial conditions, and measures of macro-level canalization
and robustness to perturbations. We exemplify our methodology with a well-known
model of the intra- and inter cellular genetic regulation of body segmentation
in Drosophila melanogaster. We use this model to show that our analysis does
not contradict any previous findings. But we also obtain new knowledge about
its behaviour: a better understanding of the size of its wild-type attractor
basin (larger than previously thought), the identification of novel minimal
conditions and critical nodes that control wild-type behaviour, and the
resilience of these to stochastic interventions. Our methodology is applicable
to any complex network that can be modelled using automata, but we focus on
biochemical regulation and signalling, towards a better understanding of the
(decentralized) control that orchestrates cellular activity -- with the
ultimate goal of explaining how do cells and tissues 'compute'
Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration
The search for symmetry as an unusual yet profoundly appealing phenomenon,
and the origin of regular, repeating configuration patterns have long been a
central focus of complexity science and physics. To better grasp and understand
symmetry of configurations in decentralized toroidal architectures, we employ
group-theoretic methods, which allow us to identify and enumerate these inputs,
and argue about irreversible system behaviors with undesired effects on many
computational problems. The concept of so-called configuration shift-symmetry
is applied to two-dimensional cellular automata as an ideal model of
computation. Regardless of the transition function, the results show the
universal insolvability of crucial distributed tasks, such as leader election,
pattern recognition, hashing, and encryption. By using compact enumeration
formulas and bounding the number of shift-symmetric configurations for a given
lattice size, we efficiently calculate the probability of a configuration being
shift-symmetric for a uniform or density-uniform distribution. Further, we
devise an algorithm detecting the presence of shift-symmetry in a
configuration.
Given the resource constraints, the enumeration and probability formulas can
directly help to lower the minimal expected error and provide recommendations
for system's size and initialization. Besides cellular automata, the
shift-symmetry analysis can be used to study the non-linear behavior in various
synchronous rule-based systems that include inference engines, Boolean
networks, neural networks, and systolic arrays.Comment: 22 pages, 9 figures, 2 appendice
Deep Reinforcement Learning for Control of Probabilistic Boolean Networks
Probabilistic Boolean Networks (PBNs) were introduced as a computational
model for the study of complex dynamical systems, such as Gene Regulatory
Networks (GRNs). Controllability in this context is the process of making
strategic interventions to the state of a network in order to drive it towards
some other state that exhibits favourable biological properties. In this paper
we study the ability of a Double Deep Q-Network with Prioritized Experience
Replay in learning control strategies within a finite number of time steps that
drive a PBN towards a target state, typically an attractor. The control method
is model-free and does not require knowledge of the network's underlying
dynamics, making it suitable for applications where inference of such dynamics
is intractable. We present extensive experiment results on two synthetic PBNs
and the PBN model constructed directly from gene-expression data of a study on
metastatic-melanoma
Gene expression profiling associated with the progression to poorly differentiated thyroid carcinomas
Poorly differentiated thyroid carcinomas (PDTC) represent a heterogeneous, aggressive entity, presenting features that suggest a progression from well-differentiated carcinomas. To elucidate the mechanisms underlying such progression and identify novel therapeutic targets, we assessed the genome-wide expression in normal and tumour thyroid tissues.info:eu-repo/semantics/publishe
MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal
Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio
Measurement of the total cross section from elastic scattering in pp collisions at sâ = 7 TeV with the ATLAS detector
A measurement of the total pp cross section at the LHC at âs = 7 TeV is presented. In a special run with
high-ÎČ beam optics, an integrated luminosity of 80 ”bâ1 was accumulated in order to measure the differential
elastic cross section as a function of the Mandelstam momentum transfer variable t. The measurement
is performed with the ALFA sub-detector of ATLAS. Using a fit to the differential elastic cross section in
the |t| range from 0.01 GeV2 to 0.1 GeV2 to extrapolate to |t| â 0, the total cross section, Ïtot(pp â X),
is measured via the optical theorem to be:
Ïtot(pp â X) = 95.35 ± 0.38 (stat.) ± 1.25 (exp.) ± 0.37 (extr.) mb,
where the first error is statistical, the second accounts for all experimental systematic uncertainties and the\ud
last is related to uncertainties in the extrapolation to |t| â 0. In addition, the slope of the elastic cross
section at small |t| is determined to be B = 19.73 ± 0.14 (stat.) ± 0.26 (syst.) GeVâ2
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