Modeling and simulation of ion channels.

摘要: Transport of ions through pores in membranes is a process fundamental importance to cell biology. In living organisms, such transport facilitated by ion channels that utilize the ionic flux perform diverse biological functions, as cell-cell communication and signaling, osmotic stress response, muscle contraction, etc. The action responsible for most what we (humans) perceive reality form sound, smell, sight, taste touch, forms physiological basis thought. Biomimetic are ubiquitous engineering, with application ranging from water desalination fuel cells. Since discovery excitable membranes, modeling simulation have been an integral part development field. From early studies Hodgkin Huxley recent fully atomistic simulations conductance, key challenge this area remains prediction electrical response membrane incorporating external stimuli transmembrane voltage, chemical ligands, tension, ever increasing complexity computational models reflects dramatic advances our experimental knowledge about these systems, importantly, structures several channels1–3 direct observations single channel’s action,4–7 more discoveries yet come. Here, review efforts model simulate occurred within past ten years. First, briefly describe phenomenological developments area. Next, channel systems studied extensively various approaches. Our selection based solely on their popularity among modelers neither intended provide representative overview evolutionary nor presented any particular historical order. common methods used study channels. Table 1 links providing explicit references specific performed using methods. second half organized according typical questions interest: binding permeation pathways, selectivity gating. last section summarizes field stochastic sensors—biomimetic promising applications biomedical diagnostics. At end review, perspective next years. Table 1 Modeling general system type employed. 2 Early models Early work actually well before existence had established, or even surmised.395 Rather, researchers were attempting understand mechanism signal propagation nerve cells. Nerve cells at rest maintain potential, defined potential interior relative exterior. Rest potentials negative generally range −40 −95 mV.395 Interestingly, axons support transmission pulse slightly positive which carries signals neural network. technique Huxley, called voltage clamp, illustrated schematically Figure 1a. clamp experiment, held constant, resulting current measured. Such experiments determine permeability function time. Figure 1 Evolution equivalent circuit diagrams axon models. (a) Schematic representation axon. Conductance maintaining given measuring current. (b) Cable ... Early described “cable”, conductive core surrounded less conductive, capacitive sheath, later identified membrane. This corresponds diagram shown 1b. Further showed during excitation increases dramatically. Additionally, it was found assigning variable electromotive force, emf, provided better fit data, yielding 1c. Finally, brilliant insight Huxley396 established currents associated changes fact carried multiple species, primarily K+ Na+. conceptual leap removed need emf diagram, instead separate emfs resistances Na+ species. They also small, so-called “leakage” constant resistance. final 1d. The realization manifested species major advance. Experiments isolating revealed fascinating twist: under externally applied resistance drops stays low, while initially but then returns its previous level. 2 shows conductance squid sodium potassium. Figure 2 Conductance potassium voltages. Voltage value −65 mV, increased displayed t = 0. While rises saturates ... The Hodgkin-Huxley, HH, describes behavior two independent introduced 1d. For convenience, restate quantities inverses, conductances gK gNa. model, gNa vary between zero maximum values ḡK ḡNa, respectively. other words, gK=xKg¯KgNa=xNag¯Na The goal HH coefficients xK xNa. xNa only dependent time voltage. We first how coefficient represented model. To best supposes four particles control conductance. Although did not know channels, here will assume channel. 3a controlling particles. Each particle may be one states: active inactive. order conduct, all must active. Following let us say probability being n. n4. average then Figure 3 (a, b) Schematics considered Hodgkin- controlled activating (black circles), ... IK=n4g¯K(V-eK) (1) where V eK originates concentration gradient across membrane, driven pumps Na+-K+ ATPase.398