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  • Open Access

Towards a dynamic clamp for neuro-chemical modalities

  • 1Email author,
  • 2,
  • 2,
  • 1,
  • 2,
  • 2 and
  • 2
BMC Neuroscience201314 (Suppl 1) :P232

https://doi.org/10.1186/1471-2202-14-S1-P232

  • Published:

Keywords

  • Computational Fluid Dynamics
  • Model Neuron
  • Microfluidic Chip
  • Chemical Concentration
  • Distinct Chemical
The classic dynamic clamp technique uses a real-time electrical interface between living cells and neural simulations in order to investigate hypotheses about neural function and structure [3]. However, it has two major drawback [2]: the electrodes can clamp electrically only a small section of a cell, and hence frequently do not control the behavior of whole cells; and all control to-date has been concentrated on the electric properties of neurons, neglecting their chemical state. As noted in [2], the latter has been done by necessity, since until recently registering or controlling the chemical state of either neurons or the chemical environment in which they reside has been essentially impossible with the level of precision needed to simulate the appropriate dynamics of the various extracellular chemical players. In this manuscript we present an expansion of the dynamic clamp method to include simulation and control of the effects of a variety of chemicals associated with neural signaling. To achieve that, we use the emergent discipline of microfluidics [4], which deals specifically with the behavior, precise control and manipulation of fluids at the microscale. We use a novel combination of microfluidic and nanosensor technology to add sensing and control of chemical concentrations to the dynamic clamp technique. Specifically, we use a microfluidic chip to generate distinct chemical concentration gradients (ions or neuromodulators), register the concentrations with embedded nanosensors, and use the processed signals as an input to simulations of a neural cell. The ultimate goal of this project is to close the loop, and provide control signals to the microfluidic lab to mimic the interaction of the simulated cell with other cells in its chemical environment, by modifying the chemical concentrations in the microfluidic lab environment to reflect simulated outputs of the model neurons. Here we used Hodgkin-Huxley type neurons, C d V d t = I o + I H H + I C a with added calcium-gated currents,
I C a = m ( V ) 2 h P m a x z 2 F 2 R T V ( [ C a ] i n - [ C a ] o u t e - z V F R T 1 - e - z V F R T )
In order to predict the distribution of specific chemicals inside a microfluidic chamber, 2-D Computational Fluid Dynamics technique was used. In this study, the commercial software FLUENT® 6.3 was used to build the computational domain, the models for a microfluidic mixer and a neuron chamber by using finite element methods.
Figure 1
Figure 1

Shows the simulated microfluidic device (A) and simulated square pulse flow at pts C1 and C2 (B). The responses of the model neuron to changes in Ca++ concentration are shown in panel C. The device in (A) was realized physically, using standard lithography and bonding methods as described in [1]. The Ca++ concentration was recorded and the record was used as an input to the same model neuron, with results indistinguishable from (C).

Authors’ Affiliations

(1)
Department of Mathematics, Washington State University, Vancouver, WA 98686, USA
(2)
Department of Mechanical Engineering, Washington State University, Vancouver, WA 98686, USA

References

  1. Li P, Lei N, Sheadel DA, Xu J, Xue W: Integration of nanosensors into a sealed microchannel in a hybrid lab-on-a-chip device. Sensors and Actuators B: Chemical. 2012, 166-167: 751-9.Google Scholar
  2. Prinz AA, Cudmore RH: Dynamic clamp. Scholarpedia. 2011, 6 (5): 1470-10.4249/scholarpedia.1470.View ArticleGoogle Scholar
  3. Sharp AA, O'Neil MB, Abbott LF, Marder E: Dynamic clamp: Computer-generated conductances in real neurons. J Neurophysiol. 1993, 69: 992-5.PubMedGoogle Scholar
  4. Squires TM, Quake SR: Microfluidics: Fluid physics at the nanoliter scale. Rev Mod Phy. 2005, 77: 977-1026. 10.1103/RevModPhys.77.977.View ArticleGoogle Scholar

Copyright

© Dimitrov et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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