15,443 research outputs found
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Regulatory motif discovery using a population clustering evolutionary algorithm
This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences
Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer
Methods for extracting marker genes that trigger the growth
of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution
can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification
accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac
Optimal signal processing in small stochastic biochemical networks
We quantify the influence of the topology of a transcriptional regulatory
network on its ability to process environmental signals. By posing the problem
in terms of information theory, we may do this without specifying the function
performed by the network. Specifically, we study the maximum mutual information
between the input (chemical) signal and the output (genetic) response
attainable by the network in the context of an analytic model of particle
number fluctuations. We perform this analysis for all biochemical circuits,
including various feedback loops, that can be built out of 3 chemical species,
each under the control of one regulator. We find that a generic network,
constrained to low molecule numbers and reasonable response times, can
transduce more information than a simple binary switch and, in fact, manages to
achieve close to the optimal information transmission fidelity. These
high-information solutions are robust to tenfold changes in most of the
networks' biochemical parameters; moreover they are easier to achieve in
networks containing cycles with an odd number of negative regulators (overall
negative feedback) due to their decreased molecular noise (a result which we
derive analytically). Finally, we demonstrate that a single circuit can support
multiple high-information solutions. These findings suggest a potential
resolution of the "cross-talk" dilemma as well as the previously unexplained
observation that transcription factors which undergo proteolysis are more
likely to be auto-repressive.Comment: 41 pages 7 figures, 5 table
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