Draft:Triangulation sensing
| Submission declined on 18 September 2024 by LR.127 (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner.
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| Submission declined on 4 February 2024 by The Herald (talk). This submission reads more like an essay than an encyclopedia article. Submissions should summarise information in secondary, reliable sources and not contain opinions or original research. Please write about the topic from a neutral point of view in an encyclopedic manner. This submission does not appear to be written in the formal tone expected of an encyclopedia article. Entries should be written from a neutral point of view, and should refer to a range of independent, reliable, published sources. Please rewrite your submission in a more encyclopedic format. Please make sure to avoid peacock terms that promote the subject. Declined by The Herald 9 months ago. | |
Comment: Issues have not been fixed since the last decline. LR.127 (talk) 01:18, 18 September 2024 (UTC)
- Comment: Requires complete rewrite and more references to prove notability. The Herald (Benison) (talk) 07:01, 4 February 2024 (UTC)
Triangulation sensing is a theory describing the computational steps of a cell containing on its surface small windows, to estimate the location of a source emitting random particles in a medium. Particles are absorbed by the receptors.This theory is relevant for studying neuron navigation in the Brain[1][2]. Indeed, reconstructing the source location allows a navigating cell to triangulate its position. The reconstruction steps of the gradient source from the fluxes of diffusing particles arriving to small absorbing receptors are:
- Arrival of the Brownian particles to the small windows
- Counting particles at each window to estimated the fluxes
- Inversion of the Laplace's equation to estimate the source position from combining the fluxes.
- Accounr for possible noise reduction by applying the same procedure to multple several window triplets[3]
The mathematical formulation consists in considering diffusing molecules that have to bind to N narrow windows located on the surface of a three dimensional shaped object, typically a ball (in dimension 3) or a disk in dimension 2. The number N can range between 10 and 50, but can be much higher for other receptor types. Individual Brownian particles are released from a source at position outside the ball. The triangulation sensing method consists in reconstructing the source position from estimated steady-state fluxes at each narrow window for fast binding (i.e. the probability density has an absorbing boundary condition at the windows).[5]
To reconstruct the location of a source from the measured fluxes, at least three windows are needed. Reconstructing the source location [6] requires to invert a system equation. With $N>3$ windows, numerical procedures are used to find the position of the source . The procedure can be accelerated by hybrid stochastic simulations.
References
[edit]- ^ Kolodkin, A. L.; Tessier-Lavigne, M. (2010-12-01). "Mechanisms and Molecules of Neuronal Wiring: A Primer". Cold Spring Harbor Perspectives in Biology. 3 (6): a001727. doi:10.1101/cshperspect.a001727. ISSN 1943-0264. PMC 3098670. PMID 21123392.
- ^ Blockus, Heike; Chédotal, Alain (August 2014). "The multifaceted roles of Slits and Robos in cortical circuits: from proliferation to axon guidance and neurological diseases". Current Opinion in Neurobiology. 27: 82–88. doi:10.1016/j.conb.2014.03.003. ISSN 0959-4388. PMID 24698714. S2CID 8858588.
- ^ Dobramysl, U., & Holcman, D. (2018). Reconstructing the gradient source position from steady-state fluxes to small receptors. Scientific reports, 8(1), 1-8.
- ^ Dobramysl, Ulrich; Holcman, David (2022-10-01). "Computational methods and diffusion theory in triangulation sensing to model neuronal navigation". Reports on Progress in Physics. 85 (10): 104601. Bibcode:2022RPPh...85j4601D. doi:10.1088/1361-6633/ac906b. ISSN 0034-4885. PMID 36075196.
- ^ Shukron, O., Dobramysl, U., & Holcman, D. (2019). Chemical Reactions for Molecular and Cellular Biology. Chemical Kinetics: Beyond The Textbook, 353.
- ^ Schuss, Zeev (2009). Theory and applications of stochastic processes : an analytical approach. Springer. ISBN 978-1-4614-2542-7. OCLC 1052813540.