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Cardiac sodium channel Markov model with temperature dependence and recovery from inactivation


Description

ABSTRACT: A Markov model of the cardiac sodium channel is presented. The model is similar to the CA1 hippocampal neuron sodium channel model developed by Kuo and Bean (1994. Neuron. 12:819 - 829) with the following modifications: 1) an additional open state is added; 2) open-inactivated transitions are made voltage-dependent; and 3) channel rate constants are exponential functions of enthalpy, entropy, and voltage and have explicit temperature dependence. Model parameters are determined using a simulated annealing algorithm to minimize the error between model responses and various experimental data sets. The model reproduces a wide range of experimental data including ionic currents, gating currents, tail currents, steady-state inactivation, recovery from inactivation, and open time distributions over a temperature range of 10C to 25C. The model also predicts measures of single channel activity such as first latency, probability of a null sweep, and probability of reopening.

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Complete network of the Bhalla-Iyengar model


Description

ABSTRACT: Many distinct signaling pathways allow the cell to receive, process, and respond to information. Often, components of different pathways interact, resulting in signaling networks. Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Feedback can result in bistable behavior with discrete steady-state activities, well-defined input thresholds for transition between states and prolonged signal output, and signal modulation in response to transient stimuli. These properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.

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Computational Model for Effects of Ligand/Receptor Binding Properties on Interleukin-2 Trafficking Dynamics and T Cell Proliferation Response


Description

ABSTRACT: Multisubunit cytokine receptors such as the heterotrimeric receptor for interleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance in biotechnology and are crucial regulators of cell proliferation and differentiation behavior. Dynamics of cytokine/receptor endocytic trafficking can significantly impact cell responses through effects of receptor down-regulation and ligand depletion, and in turn are governed by ligand/receptor binding properties. We describe here a computational model for trafficking dynamics of the IL-2 receptor (IL-2R) system, which is able to predict T cell proliferation responses to IL-2. This model comprises kinetic equations describing binding, internalization, and postendocytic sorting of IL-2 and IL-2R, including an experimentally derived dependence of cell proliferation rate on these properties. Computational results from this model predict that IL-2 depletion can be reduced by decreasing its binding affinity for the IL-2R betagamma subunit relative to the alpha subunit at endosomal pH, as a result of enhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2 analogue with such altered binding properties should exhibit increased potency for stimulating the T cell proliferation response. These results are in agreement with our recent experimental findings for the IL-2 analogue termed 2D1 [Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type of model may enable prediction of beneficial cytokine/receptor binding properties to aid development of molecular design criteria for improvements in applications such as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.

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