Editing Electrophysiology (section)

==Other methods==

===Solid-supported membrane (SSM)-based===
With this electrophysiological approach, proteo[[liposome]]s, membrane [[Vesicle (biology)|vesicle]]s, or membrane fragments containing the channel or transporter of interest are adsorbed to a lipid monolayer painted over a functionalized electrode. This electrode consists of a glass support, a [[chromium]] layer, a [[gold]] layer, and an octadecyl [[mercaptane]] monolayer. Because the painted membrane is supported by the electrode, it is called a solid-supported membrane. Mechanical perturbations, which usually destroy a biological lipid membrane, do not influence the life-time of an SSM. The [[capacitance|capacitive]] electrode (composed of the SSM and the absorbed vesicles) is so mechanically stable that solutions may be rapidly exchanged at its surface. This property allows the application of rapid substrate/ligand concentration jumps to investigate the electrogenic activity of the protein of interest, measured via capacitive coupling between the vesicles and the electrode.<ref>{{Cite journal |doi=10.1016/j.ymeth.2008.07.002 |title=SSM-based electrophysiology |year=2008 |last1=Schulz |first1=Patrick |last2=Garcia-Celma |first2=Juan J. |last3=Fendler |first3=Klaus |journal=Methods |volume=46 |issue=2 |pages=97–103 |pmid=18675360}}</ref>

=== {{anchor|BERA}} Bioelectric recognition assay (BERA) ===
The bioelectric recognition assay (BERA) is a novel method for determination of various chemical and biological molecules by measuring changes in the membrane potential of cells immobilized in a gel matrix. Apart from the increased stability of the electrode-cell interface, immobilization preserves the viability and physiological functions of the cells. BERA is used primarily in [[Biosensor|biosensor applications]] in order to assay analytes that can interact with the [[Immobilized whole cell|immobilized cells]] by changing the cell membrane potential. In this way, when a positive sample is added to the sensor, a characteristic, "signature-like" change in electrical potential occurs. BERA is the core technology behind the recently launched pan-European FOODSCAN project, about pesticide and food risk assessment in Europe.<ref>Kintzios S., E. Pistola, P. Panagiotopoulos, M. Bomsel, N. Alexandropoulos, F. Bem, I. Biselis, R. Levin (2001) Bioelectric recognition assay (BERA). Biosensors and Bioelectronics 16: 325–36</ref> BERA has been used for the detection of human viruses ([[hepatitis B]] and [[hepatitis C|C]] viruses and [[herpes]] viruses),<ref>Perdikaris, A.; Alexandropoulos, N; Kintzios, S. (2009) Development of a Novel, Ultra-rapid Biosensor for the Qualitative Detection of Hepatitis B Virus-associated Antigens and Anti-HBV, Based on "Membrane-engineered" Fibroblast Cells with Virus-Specific Antibodies and Antigens. Sensors 9: 2176–86.</ref> veterinary disease agents ([[foot and mouth disease]] virus, [[prion]]s, and [[Bluetongue disease|blue tongue virus]]), and plant viruses (tobacco and cucumber viruses)<ref>Moschopoulou G.; Vitsa, K.; Bem, F.; Vassilakos, N.; Perdikaris, A.; Blouhos, P.; Yialouris, C.; Frossiniotis, D.; Anthopoulos, I.; Maggana, O.; Nomikou, K.; Rodeva, V.; Kostova, D.; Grozeva, S.; Michaelides, A.; Simonian, A.; Kintzios, S. (2008) Engineering of the membrane of fibroblast cells with virus-specific antibodies: a novel biosensor tool for virus detection. Biosensors Bioelectron. 24: 1033–36.</ref> in a specific, rapid (1–2 minutes), reproducible, and cost-efficient fashion. The method has also been used for the detection of environmental toxins, such as [[pesticides]]<ref>Flampouri E, Mavrikou S, Kintzios S, Miliaids G, Aplada-Sarli P (2010). Development and Validation of a Cellular Biosensor Detecting Pesticide Residues in Tomatoes. Talanta 80: 1799–804.</ref><ref>Mavrikou, S, Flampouri, E, Moschopoulou, G, Mangana, O, Michaelides, A, Kintzios, S (2008) Assessment of organophosphate and carbamate pesticide residues in cigarette tobacco with a novel cell biosensor. Sensors 8: 2818–32</ref><ref>Lokka K., Skandamis P., Kintzios S. (2013) Screening of Total Organophosphate Pesticides in Agricultural Products with a Cellular Biosensor CellBio 2: 131–37.</ref> and [[mycotoxin]]s<ref>Larou, E., Yiakoumettis, I., Kaltsas, G., Petropoulos, A., Skandamis, P., Kintzios, S. (2012) High throughput cellular biosensor for the ultra-sensitive, ultra-rapid detection of aflatoxin M1. Food Control 29: 208–12</ref> in food, and [[2,4,6-trichloroanisole]] in cork and wine,<ref>Varelas, V., Sanvicens N, Marco MP, Kintzios S (2010) Development of a cellular biosensor for the detection of 2, 4, 6- trichloroanisole (TCA). Talanta 84: 936–40</ref><ref>Apostolou T, Pascual N, Marco M-P, Moschos A, Petropoulos A, Kaltsas G, Kintzios S (2014) Extraction-less, rapid assay for the direct detection of 2,4,6-trichloroanisole (TCA) in cork samples. Talanta 125: 336–40.</ref> as well as the determination of very low concentrations of the [[superoxide]] anion in clinical samples.<ref>Moschopoulou G., Kintzios S. (2006) Application of "membrane-engineering" to bioelectric recognition cell sensors for the detection of picomole concentrations of superoxide radical: a novel biosensor principle. Anal. Chimica Acta 573–74: 90–96.</ref><ref>Moschopoulou, G., Valero, T., Kintzios, S. (2012) Superoxide determination using membrane-engineered cells: An example of a novel concept for the construction of cell sensors with customized target recognition properties. Sens. Actuat.175: 88–94</ref>

A BERA sensor has two parts:
* The consumable biorecognition elements
* The electronic read-out device with embedded [[artificial intelligence]].<ref>Ferentinos K.P., C.P. Yialouris, P. Blouchos, G. Moschopoulou, V. Tsourou, Kintzios, S. (2013) Pesticide Residue Screening Using a Novel Artificial Neural Network Combined with a Bioelectric Cellular Biosensor. BioMed Research International. Article ID 813519.</ref>

A recent advance is the development of a technique called molecular identification through membrane engineering (MIME). This technique allows for building cells with defined specificity for virtually any molecule of interest, by embedding thousands of artificial receptors into the cell membrane.<ref>Kokla A, Blouchos P., Livaniou E., Zikos C., Kakabakos S.E., Petrou P.S., Kintzios, S. (2013) Visualization of the membrane-engineering concept: evidence for the specific orientation of electroinserted antibodies and selective binding of target analytes. Journal of Molecular Recognition 26: 627–232.</ref>

=== Computational electrophysiology ===

While not strictly constituting an experimental measurement, methods have been developed to examine the conductive properties of proteins and biomembranes ''[[in silico]]''. These are mainly [[molecular dynamics]] simulations in which a model system like a [[lipid bilayer]] is subjected to an externally applied voltage. Studies using these setups have been able to study dynamical phenomena like [[electroporation]] of membranes<ref>{{cite journal |doi=10.1529/biophysj.106.094797 |title=Ion Leakage through Transient Water Pores in Protein-Free Lipid Membranes Driven by Transmembrane Ionic Charge Imbalance |year=2007 |last1=Gurtovenko |first1=Andrey A. |last2=Vattulainen |first2=Ilpo |journal=Biophysical Journal |volume=92 |issue=6 |pages=1878–90 |pmid=17208976 |pmc=1861780|bibcode=2007BpJ....92.1878G }}</ref> and ion translocation by channels.<ref>{{Cite journal |doi=10.1016/j.bpj.2011.06.010 |title=Computational Electrophysiology: The Molecular Dynamics of Ion Channel Permeation and Selectivity in Atomistic Detail |year=2011 |last1=Kutzner |first1=Carsten |last2=Grubmüller |first2=Helmut |last3=De Groot |first3=Bert L. |last4=Zachariae |first4=Ulrich |journal=Biophysical Journal |volume=101 |issue=4 |pages=809–17 |pmid=21843471 |pmc=3175076|bibcode=2011BpJ...101..809K }}</ref>

The benefit of such methods is the high level of detail of the active conduction mechanism, given by the inherently high resolution and data density that atomistic simulation affords. There are significant drawbacks, given by the uncertainty of the legitimacy of the model and the computational cost of modeling systems that are large enough and over sufficient timescales to be considered reproducing the macroscopic properties of the systems themselves. While atomistic simulations may access timescales close to, or into the microsecond domain, this is still several orders of magnitude lower than even the resolution of experimental methods such as patch-clamping.{{Citation needed|date=April 2013}}