4,438 research outputs found
Estimating the size of the solution space of metabolic networks
In this work we propose a novel algorithmic strategy that allows for an
efficient characterization of the whole set of stable fluxes compatible with
the metabolic constraints. The algorithm, based on the well-known Bethe
approximation, allows the computation in polynomial time of the volume of a non
full-dimensional convex polytope in high dimensions. The result of our
algorithm match closely the prediction of Monte Carlo based estimations of the
flux distributions of the Red Blood Cell metabolic network but in incomparably
shorter time. We also analyze the statistical properties of the average fluxes
of the reactions in the E-Coli metabolic network and finally to test the effect
of gene knock-outs on the size of the solution space of the E-Coli central
metabolism.Comment: 8 pages, 7 pdf figure
Computational tools for low energy building design : capabilities and requirements
Integrated building performance simulation (IBPS) is an established technology, with the ability to model the heat, mass, light, electricity and control signal flows within complex building/plant systems. The technology is used in practice to support the design of low energy solutions and, in Europe at least, such use is set to expand with the advent of the Energy Performance of Buildings Directive, which mandates a modelling approach to legislation compliance. This paper summarises IBPS capabilities and identifies developments that aim to further improving integrity vis-Ã -vis the reality
Learning Combinatorial Embedding Networks for Deep Graph Matching
Graph matching refers to finding node correspondence between graphs, such
that the corresponding node and edge's affinity can be maximized. In addition
with its NP-completeness nature, another important challenge is effective
modeling of the node-wise and structure-wise affinity across graphs and the
resulting objective, to guide the matching procedure effectively finding the
true matching against noises. To this end, this paper devises an end-to-end
differentiable deep network pipeline to learn the affinity for graph matching.
It involves a supervised permutation loss regarding with node correspondence to
capture the combinatorial nature for graph matching. Meanwhile deep graph
embedding models are adopted to parameterize both intra-graph and cross-graph
affinity functions, instead of the traditional shallow and simple parametric
forms e.g. a Gaussian kernel. The embedding can also effectively capture the
higher-order structure beyond second-order edges. The permutation loss model is
agnostic to the number of nodes, and the embedding model is shared among nodes
such that the network allows for varying numbers of nodes in graphs for
training and inference. Moreover, our network is class-agnostic with some
generalization capability across different categories. All these features are
welcomed for real-world applications. Experiments show its superiority against
state-of-the-art graph matching learning methods.Comment: ICCV2019 oral. Code available at
https://github.com/Thinklab-SJTU/PCA-G
Rigidity and flexibility of biological networks
The network approach became a widely used tool to understand the behaviour of
complex systems in the last decade. We start from a short description of
structural rigidity theory. A detailed account on the combinatorial rigidity
analysis of protein structures, as well as local flexibility measures of
proteins and their applications in explaining allostery and thermostability is
given. We also briefly discuss the network aspects of cytoskeletal tensegrity.
Finally, we show the importance of the balance between functional flexibility
and rigidity in protein-protein interaction, metabolic, gene regulatory and
neuronal networks. Our summary raises the possibility that the concepts of
flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl
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