Abstract
Plants are exposed to a temporally and spatially heterogeneous environment, and photosynthesis is well adapted to these fluctuations. Understanding of the complex, non-linear dynamics of photosynthesis in fluctuating light requires novel-modeling approaches that involve not only the primary light and dark biochemical reactions, but also networks of regulatory interactions. This requirement exceeds the capacity of the existing molecular models that are typically reduced to describe a partial process, dynamics of a specific complex or its particular dynamic feature. We propose a concept of comprehensive model that would represent an internally consistent, integral framework combining information on the reduced models that led to its construction. This review explores approaches and tools that exist in engineering, mathematics, and in other domains of biology that can be used to develop a comprehensive model of photosynthesis. Equally important, we investigated techniques by which one can rigorously reduce such a comprehensive model to models of low dimensionality, which preserve dynamic features of interest and, thus, contribute to a better understanding of photosynthesis under natural and thus fluctuating conditions. The web-based platform www.e-photosynthesis.org is introduced as an arena where these concepts and tools are being introduced and tested.
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Notes
It should be mentioned that, if components are distributed heterogeneously in space, effects, such as diffusion, are important within a system, and other mathematical formalisms, such as partial differential equations, should be used. However, this is outside the scope of this paper.
This argument is not valid for delayed fluorescence (also called delayed light emission).
Here, we also implicitly assume flash intensity is strong enough to excite all the antenna systems in the sample, but is not so strong to generate double-excited antenna systems.
Roughly, controllability reflects existence of input stimuli that could bring the system in a finite time from an initial state to a particular system state. If such stimuli are not available, the particular state can be eliminated from the reduced model. Observability reflects influence that a particular state exerts on any of the modeled outputs.
Abbreviations
- CAM:
-
Crassulacean acid metabolism
- Chl:
-
Chlorophyll
- Fm′:
-
Maximum fluorescence yield measured in light-adapted organism during saturating flash of light
- ΔF :
-
Difference between the maximum fluorescence F m′ and steady state fluorescence yields
- ODE:
-
Ordinary differential equations
- PQ:
-
Plastoquinone
- PSI:
-
Photosystem I
- PSII:
-
Photosystem II
- QA :
-
Primary quinone acceptor of Photosystem II
- QSSA:
-
Quasi steady-state approximation
- SBML:
-
Systems Biology Markup Language
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Acknowledgments
LN was supported by the Czech Academy of Sciences Grant AV0Z60870520, by the Czech Ministry of Education, Sports and Youth Grant MSM6007665808, by the Grant Agency of the Czech Republic GACR 206/05/0894. JČ was supported in part by the grant 1M0567 of the Czech Ministry of Education. HS was supported by a grant from the Swedish Foundation for Strategic Research.
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Nedbal, L., Červený, J., Rascher, U. et al. E-photosynthesis: a comprehensive modeling approach to understand chlorophyll fluorescence transients and other complex dynamic features of photosynthesis in fluctuating light. Photosynth Res 93, 223–234 (2007). https://doi.org/10.1007/s11120-007-9178-9
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DOI: https://doi.org/10.1007/s11120-007-9178-9