6,942 research outputs found
Genetic Algorithms for the Imitation of Genomic Styles in Protein Backtranslation
Several technological applications require the translation of a protein into
a nucleic acid that codes for it (``backtranslation''). The degeneracy of the
genetic code makes this translation ambiguous; moreover, not every translation
is equally viable. The common answer to this problem is the imitation of the
codon usage of the target species. Here we discuss several other features of
coding sequences (``coding statistics'') that are relevant for the ``genomic
style'' of different species. A genetic algorithm is then used to obtain
backtranslations that mimic these styles, by minimizing the difference in the
coding statistics. Possible improvements and applications are discussed.Comment: 17 pages, 13 figures. Submitted to Theor. Comp. Scienc
Deterministic networks for probabilistic computing
Neural-network models of high-level brain functions such as memory recall and
reasoning often rely on the presence of stochasticity. The majority of these
models assumes that each neuron in the functional network is equipped with its
own private source of randomness, often in the form of uncorrelated external
noise. However, both in vivo and in silico, the number of noise sources is
limited due to space and bandwidth constraints. Hence, neurons in large
networks usually need to share noise sources. Here, we show that the resulting
shared-noise correlations can significantly impair the performance of
stochastic network models. We demonstrate that this problem can be overcome by
using deterministic recurrent neural networks as sources of uncorrelated noise,
exploiting the decorrelating effect of inhibitory feedback. Consequently, even
a single recurrent network of a few hundred neurons can serve as a natural
noise source for large ensembles of functional networks, each comprising
thousands of units. We successfully apply the proposed framework to a diverse
set of binary-unit networks with different dimensionalities and entropies, as
well as to a network reproducing handwritten digits with distinct predefined
frequencies. Finally, we show that the same design transfers to functional
networks of spiking neurons.Comment: 22 pages, 11 figure
Modelling the human pharyngeal airway: validation of numerical simulations using in vitro experiments
In the presented study, a numerical model which predicts the flow-induced
collapse within the pharyngeal airway is validated using in vitro measurements.
Theoretical simplifications were considered to limit the computation time.
Systematic comparisons between simulations and measurements were performed on
an in vitro replica, which reflects asymmetries of the geometry and of the
tissue properties at the base of the tongue and in pathological conditions
(strong initial obstruction). First, partial obstruction is observed and
predicted. Moreover, the prediction accuracy of the numerical model is of 4.2%
concerning the deformation (mean quadratic error on the constriction area). It
shows the ability of the assumptions and method to predict accurately and
quickly a fluid-structure interaction
Protein-DNA computation by stochastic assembly cascade
The assembly of RecA on single-stranded DNA is measured and interpreted as a
stochastic finite-state machine that is able to discriminate fine differences
between sequences, a basic computational operation. RecA filaments efficiently
scan DNA sequence through a cascade of random nucleation and disassembly events
that is mechanistically similar to the dynamic instability of microtubules.
This iterative cascade is a multistage kinetic proofreading process that
amplifies minute differences, even a single base change. Our measurements
suggest that this stochastic Turing-like machine can compute certain integral
transforms.Comment: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC129313/
http://www.pnas.org/content/99/18/11589.abstrac
Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype
<p>Abstract</p> <p>Background</p> <p>A finite state machine manipulating information-carrying DNA strands can be used to perform autonomous molecular-scale computations at the cellular level.</p> <p>Results</p> <p>We propose a new finite state machine able to detect and correct aberrant molecular phenotype given by mutated genetic transcripts. The aberrant mutations trigger a cascade reaction: specific molecular markers as input are released and induce a spontaneous self-assembly of a wild type protein or peptide, while the mutational disease phenotype is silenced. We experimentally demostrated in <it>in vitro </it>translation system that a viable protein can be autonomously assembled.</p> <p>Conclusion</p> <p>Our work demostrates the basic principles of computational genes and particularly, their potential to detect mutations, and as a response thereafter administer an output that suppresses the aberrant disease phenotype and/or restores the lost physiological function.</p
Synthetic biology: advancing biological frontiers by building synthetic systems
Advances in synthetic biology are contributing
to diverse research areas, from basic biology to
biomanufacturing and disease therapy. We discuss the
theoretical foundation, applications, and potential of
this emerging field
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