1,795 research outputs found
A knowledge representation meta-model for rule-based modelling of signalling networks
The study of cellular signalling pathways and their deregulation in disease
states, such as cancer, is a large and extremely complex task. Indeed, these
systems involve many parts and processes but are studied piecewise and their
literatures and data are consequently fragmented, distributed and sometimes--at
least apparently--inconsistent. This makes it extremely difficult to build
significant explanatory models with the result that effects in these systems
that are brought about by many interacting factors are poorly understood.
The rule-based approach to modelling has shown some promise for the
representation of the highly combinatorial systems typically found in
signalling where many of the proteins are composed of multiple binding domains,
capable of simultaneous interactions, and/or peptide motifs controlled by
post-translational modifications. However, the rule-based approach requires
highly detailed information about the precise conditions for each and every
interaction which is rarely available from any one single source. Rather, these
conditions must be painstakingly inferred and curated, by hand, from
information contained in many papers--each of which contains only part of the
story.
In this paper, we introduce a graph-based meta-model, attuned to the
representation of cellular signalling networks, which aims to ease this massive
cognitive burden on the rule-based curation process. This meta-model is a
generalization of that used by Kappa and BNGL which allows for the flexible
representation of knowledge at various levels of granularity. In particular, it
allows us to deal with information which has either too little, or too much,
detail with respect to the strict rule-based meta-model. Our approach provides
a basis for the gradual aggregation of fragmented biological knowledge
extracted from the literature into an instance of the meta-model from which we
can define an automated translation into executable Kappa programs.Comment: In Proceedings DCM 2015, arXiv:1603.0053
The stochastic behavior of a molecular switching circuit with feedback
Background: Using a statistical physics approach, we study the stochastic
switching behavior of a model circuit of multisite phosphorylation and
dephosphorylation with feedback. The circuit consists of a kinase and
phosphatase acting on multiple sites of a substrate that, contingent on its
modification state, catalyzes its own phosphorylation and, in a symmetric
scenario, dephosphorylation. The symmetric case is viewed as a cartoon of
conflicting feedback that could result from antagonistic pathways impinging on
the state of a shared component.
Results: Multisite phosphorylation is sufficient for bistable behavior under
feedback even when catalysis is linear in substrate concentration, which is the
case we consider. We compute the phase diagram, fluctuation spectrum and
large-deviation properties related to switch memory within a statistical
mechanics framework. Bistability occurs as either a first-order or second-order
non-equilibrium phase transition, depending on the network symmetries and the
ratio of phosphatase to kinase numbers. In the second-order case, the circuit
never leaves the bistable regime upon increasing the number of substrate
molecules at constant kinase to phosphatase ratio.
Conclusions: The number of substrate molecules is a key parameter controlling
both the onset of the bistable regime, fluctuation intensity, and the residence
time in a switched state. The relevance of the concept of memory depends on the
degree of switch symmetry, as memory presupposes information to be remembered,
which is highest for equal residence times in the switched states.
Reviewers: This article was reviewed by Artem Novozhilov (nominated by Eugene
Koonin), Sergei Maslov, and Ned Wingreen.Comment: Version published in Biology Direct including reviewer comments and
author responses, 28 pages, 7 figure
Digital electric field induced switching of plasmonic nanorods using an electro-optic fluid fiber
We demonstrate the digital electric field induced switching of plasmonic
nanorods between 1 and 0 orthogonal aligned states using an electro-optic fluid
fiber component. We show by digitally switching the nanorods, that thermal
rotational diffusion of the nanorods can be circumvented, demonstrating an
approach to achieve submicrosecond switching times. We also show, from an
initial unaligned state, that the nanorods can be aligned into the applied
electric field direction in 110 nanoseconds. The high-speed digital switching
of plasmonic nanorods integrated into an all-fiber optical component may
provide novel opportunities for remote sensing and signaling applications
Chance and Necessity in Evolution: Lessons from RNA
The relationship between sequences and secondary structures or shapes in RNA
exhibits robust statistical properties summarized by three notions: (1) the
notion of a typical shape (that among all sequences of fixed length certain
shapes are realized much more frequently than others), (2) the notion of shape
space covering (that all typical shapes are realized in a small neighborhood of
any random sequence), and (3) the notion of a neutral network (that sequences
folding into the same typical shape form networks that percolate through
sequence space). Neutral networks loosen the requirements on the mutation rate
for selection to remain effective. The original (genotypic) error threshold has
to be reformulated in terms of a phenotypic error threshold. With regard to
adaptation, neutrality has two seemingly contradictory effects: It acts as a
buffer against mutations ensuring that a phenotype is preserved. Yet it is
deeply enabling, because it permits evolutionary change to occur by allowing
the sequence context to vary silently until a single point mutation can become
phenotypically consequential. Neutrality also influences predictability of
adaptive trajectories in seemingly contradictory ways. On the one hand it
increases the uncertainty of their genotypic trace. At the same time neutrality
structures the access from one shape to another, thereby inducing a topology
among RNA shapes which permits a distinction between continuous and
discontinuous shape transformations. To the extent that adaptive trajectories
must undergo such transformations, their phenotypic trace becomes more
predictable.Comment: 37 pages, 14 figures; 1998 CNLS conference; high quality figures at
http://www.santafe.edu/~walte
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