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Comparative study of circadian oscillatory network models of Drosophila.
Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition.
Space in systems biology of signaling pathways--towards intracellular molecular crowding in silico.
E-Cell 2: multi-platform E-Cell simulation system.
Toward large-scale modeling of the microbial cell for computer simulation.
A general computational model of mitochondrial metabolism in a whole organelle scale.
[E-Cell simulation system and its application to the modeling of circadian rhythm]
Whole-cell simulation: a grand challenge of the 21st century.
E-CELL: software environment for whole-cell simulation.
E-CELL: Software Environment for Whole Cell Simulation.
Robust effects of Tsr-CheBp and CheA-CheYp affinity in bacterial chemotaxis.
A multi-algorithm, multi-timescale method for cell simulation.
Dynamic modeling of genetic networks using genetic algorithm and S-system.
Kinetic simulation of signal transduction system in hippocampal long-term potentiation with dynamic modeling of protein phosphatase 2A.
Simulation study of methemoglobin reduction in erythrocytes. Differential contributions of two pathways to tolerance to oxidative stress.
Roles of hemoglobin Allostery in hypoxia-induced metabolic alterations in erythrocytes: simulation and its verification by metabolome analysis.
A mathematical model for the Kai-protein-based chemical oscillator and clock gene expression rhythms in cyanobacteria.
Construction of a biological tissue model based on a single-cell model: a computer simulation of metabolic heterogeneity in the liver lobule.
Dynamic simulation of an in vitro multi-enzyme system.
Computational Challenges in Cell Simulation: A Software Engineering Approach
Simulation of developmental changes in action potentials with ventricular cell models

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Kouichi Takahashi, Katsuyuki Yugi, Kenta Hashimoto, Yohei Yamada, Christopher J Pickett, and Masaru Tomita (2002)

Computational Challenges in Cell Simulation: A Software Engineering Approach

IEEE Intelligent Systems, 17(5):64-71.