Graphs, random sums, and sojourn time distributions, with application to ion-channel modeling

DOI10.1016/0025-5564(90)90056-5zbMath0734.92010OpenAlexW2029941861WikidataQ51725445 ScholiaQ51725445MaRDI QIDQ811439

Geoffrey F. Yeo, Robert O. Edeson, Barry W. Madsen, Robin K. Milne

Publication date: 1990

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0025-5564(90)90056-5

zbMATH Keywords

composition of statesdrug-receptor interactionsfive-state model of a single ion channelion-channel kineticskinetic parameter estimationrandom-sum approachsequential models of nicotinic receptor kineticssojourn time distributions

Mathematics Subject Classification ID

Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) (92C45) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Molecular structure (graph-theoretic methods, methods of differential topology, etc.) (92E10) Physiological, cellular and medical topics (92C99)

Related Items (4)

Robust inference and modelling for the single ion channel ⋮ Description of interacting channel gating using a stochastic Markovian model ⋮ Stochastic models for ion channels: Introduction and bibliography ⋮ Graphs, random sums, and sojourn time distributions, with application to ion-channel modeling

Cites Work

• Graphs, random sums, and sojourn time distributions, with application to ion-channel modeling
• Parallel concepts in graph theory
• Ion channel kinetics: A model based on fractal scaling rather than multistate Markov processes
• On aggregated Markov processes
• Aggregated Markov processes incorporating time interval omission
• Unnamed Item
• Unnamed Item

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