OnGuard, a Computational Platform for Quantitative Kinetic Modeling of Guard Cell Physiology

• Published in: Plant physiology (Bethesda) 2012-07, Vol.159 (3), p.1026-1042
• Main Authors: Hills, Adrian, Chen, Zhong-Hua, Amtmann, Anna, Blatt, Michael R., Lew, Virgilio L.
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; CELL BIOLOGY AND SIGNAL TRANSDUCTION; Cell membranes; Computational Biology - methods; Computer software; Electric potential; Environment; Fundamental and applied biological sciences. Psychology; Guard cells; Kinetics; Membrane Transport Proteins - metabolism; Modeling; Models, Biological; Parametric models; Physiological regulation; Plant physiology and development; Plant Stomata - cytology; Plant Stomata - physiology; Reference Standards; Software; Solutes; Tonoplast
• ISSN: 0032-0889; 1532-2548; 1532-2548
• DOI: 10.1104/pp.112.197244

Stomatal guard cells play a key role in gas exchange for photosynthesis while minimizing transpirational water loss from plants by opening and closing the stomatal pore. Foliar gas exchange has long been incorporated into mathematical models, several of which are robust enough to recapitulate transpirational characteristics at the whole-plant and community levels. Few models of stomata have been developed from the bottom up, however, and none are sufficiently generalized to be widely applicable in predicting stomatal behavior at a cellular level. We describe here the construction of computational models for the guard cell, building on the wealth of biophysical and kinetic knowledge available for guard cell transport, signaling, and homeostasis. The OnGuard software was constructed with the HoTSig library to incorporate explicitly all of the fundamental properties for transporters at the plasma membrane and tonoplast, the salient features of osmolite metabolism, and the major controls of cytosolic-free Ca²⁺ concentration and pH. The library engenders a structured approach to tier and interrelate computational elements, and the OnGuard software allows ready access to parameters and equations 'on the fly' while enabling the network of components within each model to interact computationally. We show that an OnGuard model readily achieves stability in a set of physiologically sensible baseline or Reference States; we also show the robustness of these Reference States in adjusting to changes in environmental parameters and the activities of major groups of transporters both at the tonoplast and plasma membrane. The following article addresses the predictive power of the OnGuard model to generate unexpected and counterintuitive outputs.