11,746 research outputs found
Qualitative modelling and analysis of regulations in multi-cellular systems using Petri nets and topological collections
In this paper, we aim at modelling and analyzing the regulation processes in
multi-cellular biological systems, in particular tissues.
The modelling framework is based on interconnected logical regulatory
networks a la Rene Thomas equipped with information about their spatial
relationships. The semantics of such models is expressed through colored Petri
nets to implement regulation rules, combined with topological collections to
implement the spatial information.
Some constraints are put on the the representation of spatial information in
order to preserve the possibility of an enumerative and exhaustive state space
exploration.
This paper presents the modelling framework, its semantics, as well as a
prototype implementation that allowed preliminary experimentation on some
applications.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions
Genetic regulatory networks (GRNs) have been widely studied, yet there is a
lack of understanding with regards to the final size and properties of these
networks, mainly due to no network currently being complete. In this study, we
analyzed the distribution of GRN structural properties across a large set of
distinct prokaryotic organisms and found a set of constrained characteristics
such as network density and number of regulators. Our results allowed us to
estimate the number of interactions that complete networks would have, a
valuable insight that could aid in the daunting task of network curation,
prediction, and validation. Using state-of-the-art statistical approaches, we
also provided new evidence to settle a previously stated controversy that
raised the possibility of complete biological networks being random and
therefore attributing the observed scale-free properties to an artifact
emerging from the sampling process during network discovery. Furthermore, we
identified a set of properties that enabled us to assess the consistency of the
connectivity distribution for various GRNs against different alternative
statistical distributions. Our results favor the hypothesis that highly
connected nodes (hubs) are not a consequence of network incompleteness.
Finally, an interaction coverage computed for the GRNs as a proxy for
completeness revealed that high-throughput based reconstructions of GRNs could
yield biased networks with a low average clustering coefficient, showing that
classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio
A Biologically Informed Hylomorphism
Although contemporary metaphysics has recently undergone a neo-Aristotelian revival wherein dispositions, or capacities are now commonplace in empirically grounded ontologies, being routinely utilised in theories of causality and modality, a central Aristotelian concept has yet to be given serious attention – the doctrine of hylomorphism. The reason for this is clear: while the Aristotelian ontological distinction between actuality and potentiality has proven to be a fruitful conceptual framework with which to model the operation of the natural world, the distinction between form and matter has yet to similarly earn its keep. In this chapter, I offer a first step toward showing that the hylomorphic framework is up to that task. To do so, I return to the birthplace of that doctrine - the biological realm. Utilising recent advances in developmental biology, I argue that the hylomorphic framework is an empirically adequate and conceptually rich explanatory schema with which to model the nature of organism
Information content of colored motifs in complex networks
We study complex networks in which the nodes of the network are tagged with
different colors depending on the functionality of the nodes (colored graphs),
using information theory applied to the distribution of motifs in such
networks. We find that colored motifs can be viewed as the building blocks of
the networks (much more so than the uncolored structural motifs can be) and
that the relative frequency with which these motifs appear in the network can
be used to define the information content of the network. This information is
defined in such a way that a network with random coloration (but keeping the
relative number of nodes with different colors the same) has zero color
information content. Thus, colored motif information captures the
exceptionality of coloring in the motifs that is maintained via selection. We
study the motif information content of the C. elegans brain as well as the
evolution of colored motif information in networks that reflect the interaction
between instructions in genomes of digital life organisms. While we find that
colored motif information appears to capture essential functionality in the C.
elegans brain (where the color assignment of nodes is straightforward) it is
not obvious whether the colored motif information content always increases
during evolution, as would be expected from a measure that captures network
complexity. For a single choice of color assignment of instructions in the
digital life form Avida, we find rather that colored motif information content
increases or decreases during evolution, depending on how the genomes are
organized, and therefore could be an interesting tool to dissect genomic
rearrangements.Comment: 21 pages, 8 figures, to appear in Artificial Lif
Toll Based Measures for Dynamical Graphs
Biological networks are one of the most studied object in computational
biology. Several methods have been developed for studying qualitative
properties of biological networks. Last decade had seen the improvement of
molecular techniques that make quantitative analyses reachable. One of the
major biological modelling goals is therefore to deal with the quantitative
aspect of biological graphs. We propose a probabilistic model that suits with
this quantitative aspects. Our model combines graph with several dynamical
sources. It emphazises various asymptotic statistical properties that might be
useful for giving biological insightsComment: 11 page
A biophysical approach to large-scale protein-DNA binding data
About this book * Cutting-edge genome analysis methods from leading bioinformaticians An accurate description of current scientific developments in the field of bioinformatics and computational implementation is presented by research of the BioSapiens Network of Excellence. Bioinformatics is essential for annotating the structure and function of genes, proteins and the analysis of complete genomes and to molecular biology and biochemistry. Included is an overview of bioinformatics, the full spectrum of genome annotation approaches including; genome analysis and gene prediction, gene regulation analysis and expression, genome variation and QTL analysis, large scale protein annotation of function and structure, annotation and prediction of protein interactions, and the organization and annotation of molecular networks and biochemical pathways. Also covered is a technical framework to organize and represent genome data using the DAS technology and work in the annotation of two large genomic sets: HIV/HCV viral genomes and splicing alternatives potentially encoded in 1% of the human genome
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
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