Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(ion channel models). Showing records 1 – 3 of 3 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Indian Institute of Science

1. Metri, Vishal. Stochastic Chemical Kinetics : A Study on hTREK1 Potassium Channel.

Degree: 2013, Indian Institute of Science

Chemical reactions involving small number of reacting molecules are noisy processes. They are simulated using stochastic simulation algorithms like the Gillespie SSA, which are valid when the reaction environment is well-mixed. This is not the case in reactions occuring on biological media like cell membranes, where alternative simulation methods have to be used to account for the crowded nature of the reacting environment. Ion channels, which are membrane proteins controlling the flow of ions into and out of the cell, offer excellent single molecule conditions to test stochastic simulation schemes in crowded biological media. Single molecule reactions are of great importance in determining the functions of biological molecules. Access to their experimental data have increased the scope of com-putational modeling of biological processes. Recently, single molecule experiments have revealed the non-Markovian nature of chemical reactions, due to a phenomenon called `dynamic disorder', which makes the rate constants a deterministic function of time or a random process. This happens when there are additional slow scale conformational transitions, giving the molecule a memory of its previous states. In a previous work, the hTREK1 two pore domain potassium channel was revealed to have long term memory in its kinetics, prompting alternate non-Markovian schemes to analyze its gating. Traditionally, ion channel gating is modeled as Markovian transitions between fixed states. In this work, we have used single channel data from hTREK1 ion channel and have provided a simple diffusion model for its gating. The main assumption of this model is that the ion channel diffuses through a continuum of states on its potential energy landscape, which is derived from the steady state probability distribution of ionic current recorded from patch clamp experiments. A stochastic differential equation (SDE) driven by Gaussian white noise is proposed to model this motion in an asymmetric double well potential. The method is computationally very simple and efficient and reproduces the amplitude histogram very well. For the case when ligands are added, leading to incorporation of long term memory in the kinetics, the SDE is modified to run on coloured noise. This has been done by introducing an auxiliary variable into the equation. It has been shown that increasing the noise correlation with ligand concentration improves the fits to the experimental data. This has been validated for several datasets. These methods are more advantageous for simulation than the Markovian models as they are true to the physical picture of gating and also computationally very efficient. Reproducing the whole raw data trace takes no more than a few seconds with our scheme, with the only input being the amplitude histogram and four parameters. Finally a quantitative model based on a modified version of the Chemical Langevin equation is given, which works on random rate parameters. This model is computationally simple to implement and reproduces the catalytic… Advisors/Committee Members: Raha, Soumyendu.

Subjects/Keywords: Stochastic Chemical Kinetics; Chemical Reactions - Simulation; Ion Channels; Ion Channel Modelling; Chemical Kinetics - Models; Ion Channel Dynamics; Ion Channel Kinetics; hTREK1 Potassium Channel; Diffusion Model; Stochastic Di erential Equations (SDEs); Physical Chemistry

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Metri, V. (2013). Stochastic Chemical Kinetics : A Study on hTREK1 Potassium Channel. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/2005/3329 ; http://etd.iisc.ernet.in/abstracts/4193/G25721-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Metri, Vishal. “Stochastic Chemical Kinetics : A Study on hTREK1 Potassium Channel.” 2013. Thesis, Indian Institute of Science. Accessed June 17, 2019. http://etd.iisc.ernet.in/2005/3329 ; http://etd.iisc.ernet.in/abstracts/4193/G25721-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Metri, Vishal. “Stochastic Chemical Kinetics : A Study on hTREK1 Potassium Channel.” 2013. Web. 17 Jun 2019.

Vancouver:

Metri V. Stochastic Chemical Kinetics : A Study on hTREK1 Potassium Channel. [Internet] [Thesis]. Indian Institute of Science; 2013. [cited 2019 Jun 17]. Available from: http://etd.iisc.ernet.in/2005/3329 ; http://etd.iisc.ernet.in/abstracts/4193/G25721-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Metri V. Stochastic Chemical Kinetics : A Study on hTREK1 Potassium Channel. [Thesis]. Indian Institute of Science; 2013. Available from: http://etd.iisc.ernet.in/2005/3329 ; http://etd.iisc.ernet.in/abstracts/4193/G25721-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

2. Hendrickson, Eric B. Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization.

Degree: PhD, Biomedical Engineering, 2010, Georgia Tech

The dynamics of biological neural networks are of great interest to neuroscientists and are frequently studied using conductance-based compartmental neuron models. For speed and ease of use, neuron models are often reduced in morphological complexity. This reduction may affect input processing and prevent the accurate reproduction of neural dynamics. However, such effects are not yet well understood. Therefore, for my first aim I analyzed the processing capabilities of 'branched' or 'unbranched' reduced models by collapsing the dendritic tree of a morphologically realistic 'full' globus pallidus neuron model while maintaining all other model parameters. Branched models maintained the original detailed branching structure of the full model while the unbranched models did not. I found that full model responses to somatic inputs were generally preserved by both types of reduced model but that branched reduced models were better able to maintain responses to dendritic inputs. However, inputs that caused dendritic sodium spikes, for instance, could not be accurately reproduced by any reduced model. Based on my analyses, I provide recommendations on how to construct reduced models and indicate suitable applications for different levels of reduction. In particular, I recommend that unbranched reduced models be used for fast searches of parameter space given somatic input output data. The intrinsic electrical properties of neurons depend on the modifiable behavior of their ion channels. Obtaining a quality match between recorded voltage traces and the output of a conductance based compartmental neuron model depends on accurate estimates of the kinetic parameters of the channels in the biological neuron. Indeed, mismatches in channel kinetics may be detectable as failures to match somatic neural recordings when tuning model conductance densities. In my first aim, I showed that this is a task for which unbranched reduced models are ideally suited. Therefore, for my second aim I optimized unbranched reduced model parameters to match three experimentally characterized globus pallidus neurons by performing two stages of automated searches. In the first stage, I set conductance densities free and found that even the best matches to experimental data exhibited unavoidable problems. I hypothesized that these mismatches were due to limitations in channel model kinetics. To test this hypothesis, I performed a second stage of searches with free channel kinetics and observed decreases in the mismatches from the first stage. Additionally, some kinetic parameters consistently shifted to new values in multiple cells, suggesting the possibility for tailored improvements to channel models. Given my results and the potential for cell specific modulation of channel kinetics, I recommend that experimental kinetic data be considered as a starting point rather than as a gold standard for the development of neuron models. Advisors/Committee Members: Jaeger, Dieter (Committee Chair), Butera, Robert (Committee Member), Calabrese, Ronald (Committee Member), Lee, Robert H. (Committee Member), Prinz, Astrid (Committee Member), Smith, Yoland (Committee Member).

Subjects/Keywords: Ion channel kinetics; Computational modeling; Sensitivity analysis; Optimization techniques; Neuron models; Neural networks (Neurobiology); Neurosciences; Mathematical optimization; Computational neuroscience

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hendrickson, E. B. (2010). Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33905

Chicago Manual of Style (16th Edition):

Hendrickson, Eric B. “Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization.” 2010. Doctoral Dissertation, Georgia Tech. Accessed June 17, 2019. http://hdl.handle.net/1853/33905.

MLA Handbook (7th Edition):

Hendrickson, Eric B. “Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization.” 2010. Web. 17 Jun 2019.

Vancouver:

Hendrickson EB. Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Jun 17]. Available from: http://hdl.handle.net/1853/33905.

Council of Science Editors:

Hendrickson EB. Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33905


EPFL

3. Ramaswamy, Srikanth. Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column.

Degree: 2011, EPFL

The cerebral cortex occupies nearly 80% of the entire volume of the mammalian brain and is thought to subserve higher cognitive functions like memory, attention and sensory perception. The neocortex is the newest part in the evolution of the cerebral cortex and is perhaps the most intricate brain region ever studied. The neocortical microcircuit is the smallest Œecosystem‚ of the neocortex that consists of a rich assortment of neurons, which are diverse in both their morphological and electrical properties. In the neocortical microcircuit, neurons are horizontally arranged in 6 distinct sheets called layers. The fundamental operating unit of the neocortical microcircuit is believed to be the Neocortical Column (NCC). Functionally, a single NCC is an arrangement of thousands of neurons in a vertical fashion spanning across all the 6 layers. The structure of the entire neocortex arises from a repeated and stereotypical arrangement of several thousands of such columns, where neurons transmit information to each other through specialized points of information transfer called synapses. The dynamics of synaptic transmission can be as diverse as the neurons defining a connection and are crucial to foster the functional properties of the neocortical microcircuit. The Blue Brain Project (BBP) is the first comprehensive endeavour to build a unifying model of the NCC by systematic data integration and biologically detailed simulations. Through the past 5 years, the BBP has built a facility for a data-constraint driven approach towards modelling and integrating biological information across multiple levels of complexity. Guided by fundamental principles derived from biological experiments, the BBP simulation toolchain has undergone a process of continuous refinement to facilitate the frequent construction of detailed in silico models of the NCC. The focus of this thesis lies in characterizing the functional properties of in silico synaptic transmission by incorporating principles of synaptic communication derived through biological experiments. In order to study in silico synaptic transmission it is crucial to gain an understanding of the key players influencing the manner in which synaptic signals are processed in the neocortical microcircuit - ion channel kinetics and distribution profiles, single neuron models and dynamics of synaptic pathways. First, by means of exhaustive literature survey, I identified ion channel kinetics and their distribution profiles on neocortical neurons to build in silico ion channel models. Thereafter, I developed a prototype framework to analyze the somatic and dendritic features of single neuron models constrained by ion channel kinetics. Finally, within a simulation framework integrating the ion channels, single… Advisors/Committee Members: Markram, Henry, Hill, Sean Lewis.

Subjects/Keywords: neocortical column; in silico; in vitro; calibration; validation; ion channel models; single neuron models; synaptic transmission; probabilistic synapse model; synaptic pathways; excitatory and inhibitory connections; colonne néocorticale; in silico; in vitro; calibration; validation; modèles de canaux ioniques; modèles de neurones individuels; modèle synaptique probabiliste; voies synaptiques; connexions excitatrices et inhibitrices

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ramaswamy, S. (2011). Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/168660

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ramaswamy, Srikanth. “Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column.” 2011. Thesis, EPFL. Accessed June 17, 2019. http://infoscience.epfl.ch/record/168660.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ramaswamy, Srikanth. “Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column.” 2011. Web. 17 Jun 2019.

Vancouver:

Ramaswamy S. Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column. [Internet] [Thesis]. EPFL; 2011. [cited 2019 Jun 17]. Available from: http://infoscience.epfl.ch/record/168660.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ramaswamy S. Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column. [Thesis]. EPFL; 2011. Available from: http://infoscience.epfl.ch/record/168660

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.