Reaction-diffusion modelling for microphysiometry on cellular specimens.

Using modeling and simulation, we quantify the influence of spatiotemporal dynamics on the accuracy of data obtained from sensors placed in microscaled reaction volumes. The model refers to cellular reaction (i.e. proton extrusion and oxygen consumption) in complex, buffering solutions. Whole cells...

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Published in:Medical & Biological Engineering & Computing Vol. 51; no. 4; pp. 387 - 396
Main Authors: Grundl, Daniel, Zhang, Xiaorui, Messaoud, Safa, Pfister, Cornelia, Demmel, Franz, Mommer, Mario S, Wolf, Bernhard, Brischwein, Martin
Format: research Journal Article
Published: Springer Nature Apr2013
Online Access:View this record in EBSCOhost
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      dt: Apr2013
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      pub: Springer Nature
      place: New York, New York
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        atl: Reaction-diffusion modelling for microphysiometry on cellular specimens.
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        au:
          Grundl, Daniel
          Zhang, Xiaorui
          Messaoud, Safa
          Pfister, Cornelia
          Demmel, Franz
          Mommer, Mario S
          Wolf, Bernhard
          Brischwein, Martin
        affil: Department Heinz Nixdorf-Lehrstuhl Medizinische Elektronik, Technische Universität München, Theresienstrasse 90/N3, 80333, Munich, Germany.
      sug:
        subj:
          Cytological Techniques Equipment and Supplies
          Metabolism
          Models, Biological
          Oxygen Metabolism
          Computer Simulation
          Cytological Techniques Methods
          Diffusion
          Finite Element Analysis
          Hydrogen-Ion Concentration
          Kinetics
          Reproducibility of Results
      ab: Using modeling and simulation, we quantify the influence of spatiotemporal dynamics on the accuracy of data obtained from sensors placed in microscaled reaction volumes. The model refers to cellular reaction (i.e. proton extrusion and oxygen consumption) in complex, buffering solutions. Whole cells or viable tissues cultured in such devices are monitored in real time with integrated sensors for pH and dissolved oxygen. A 3D finite element model of diffusion and metabolic reaction was set up. With respect to pH, the effect of buffering species on proton diffusion is analysed in detail. To account for the delayed time response of real sensors, the sensor impulse response time was implemented by linear convolution. A validation of the model has been achieved by an electrochemical approach. The model reveals significant deviations of measured pH and O2, and values of these parameters actually occurring at different sites of the cell culture volume. It is applicable to any setting of (bio-) sensors involving reaction and diffusion of dissolved gases and particularly H(+) ions in buffered solutions.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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