Microscale modeling of gas exchange during C4 photosythesis

Research output: Thesisinternal PhD, Joint degree


Improving the efficiency of photosynthesis could contribute to better food security under an unprecedented rise in global population and climate-change. The photosynthesis pathway in C4 plants, such as maize (Zea mays L.), Miscanthus (Miscanthus x giganteus), and sugarcane (Saccharum officinarum L.), results in higher productivity and photosynthetic nitrogen and water-use efficiencies than in C3 plants. The mechanism of photosynthesis in C4 crops depends on the archetypal Kranz anatomy, which determines the leaf internal environment, for it influences gas diffusion and light distribution. The low permeability of bundle sheath cell walls to CO2 (gbs) and the high CO2 conductance of mesophyll cells (gm) are crucial for a high C4 photosynthetic efficiency. So far, the relationship between leaf anatomical properties and CO2 conductances such as gbs and gm in C4 plants received less attention than in C3 plants. In addition, these conductances lump a number of anatomical features; mechanistic understanding of the role of each microstructure element in the efficiency of photosynthesis is, therefore, limited. Furthermore, there are only few studies addressing the potential limitations of C4 leaf anatomy on light propagation and efficiency of photosynthesis.

To investigate the role of leaf anatomy, as altered by leaf nitrogen content and age on the efficiency of C4 photosynthesis, maize (Zea mays L.) plants were grown under three contrasting nitrogen levels. Combined gas exchange and chlorophyll fluorescence measurements were carried out on fully grown leaves at two leaf ages: young and old. The measured data were combined with a biochemical model of C4 photosynthesis to estimate gbs. The leaf microstructure and ultrastructure were quantified using images obtained from micro-computed tomography and microscopy. Increased nitrogen supply resulted in higher leaf nitrogen content and rate of photosynthesis, whereas leaf aging decreased them. There was a strong positive correlation between gbs and leaf nitrogen content (LNC) while old leaves had lower gbs than young leaves. gm also increased with LNC and decreased with leaf aging. The increase of gbs with LNC was little explained by a change in leaf anatomy. By contrast, the combined effects of LNC and leaf age on anatomical features were responsible for differences in gbs between young leaves and old leaves. It is recommended that changes in the leaf ultrastructure at levels of membranes and plasmodesmata should be investigated to unravel the relationship between anatomy and CO2 conductances further. Furthermore, since gbs thus estimated, lumps a number of microstructural features, the contribution of each individual leaf microstructural feature could not be determined. Therefore, a microscale modeling approach that accounts for each leaf microstructural and ultrastructural features is recommended.

A two-dimensional microscale model of gas diffusion and photosynthesis in C4 leaves that incorporates the physical obstructions of leaf anatomy and ultrastructure on gas transport was developed. The leaf anatomical geometry was developed from light microscopy images of the same leaf that was also used in gas exchange measurements. Features such as cell walls, biological membranes, plasmodesmata and suberin layers around bundle sheath cell walls were modeled as resistances. Reaction-diffusion equations for CO2 and bicarbonate in liquid phase media were developed and discretized over the two-dimensional leaf geometry. The model predicted the responses of photosynthesis to irradiance and intercellular CO2 in agreement with that obtained from measurement. The impact of components of the CO2 diffusion pathway on photosynthesis was evaluated quantitatively. The CO2 permeability of the mesophyll-bundle sheath and air space-mesophyll interfaces strongly affected the rate of photosynthesis and gbs. Carbonic anhydrase influenced the rate of photosynthesis, especially at low intercellular CO2 levels. In addition, the suberin layer at the exposed surface of the bundle sheath cells was found beneficial in reducing the retro-diffusion of CO2.

One or two-dimensional gas transport models, when applied to analyze the gas diffusion in leaves understate the three-dimensional nature of gas exchange. Therefore, a 3-D microscale model incorporating the actual leaf microstructure was developed. The distribution of light through the leaf tissue was modeled using an adapted Monte Carlo photon transport method. Diffusion of CO2 and O2 was coupled with C4 photosynthesis kinetics and a model of light penetration inside the leaf tissue. The temperature dependency of biochemical and biophysical parameters was incorporated. The typical Kranz-anatomy of the leaf tissue caused large gradients of light intensity and concentration of gases. Maximum photosynthesis at low leakiness was obtained when chlorophyll contents of mesophyll and bundle sheath cells were equal. At elevated CO2, photosynthesis in bundle sheath cells of juvenile leaves could potentially be supported by direct diffusion. Simulations also suggest that the effect of temperature on biophysical processes, in contrast to that on biochemical processes, has little influence on the temperature response of C4 photosynthesis and leakiness. In addition, a systematic analysis showed that cytosolic CO2 release due to decarboxylation of C4 acids would reduce the efficiency of photosynthesis only moderately. The model may serve as a tool to further investigate improving C4 photosynthesis in relation to gas exchange and light propagation.

Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Wageningen University
  • Nicolai, B., Promotor, External person
  • Struik, Paul, Promotor
  • Verboven, P., Co-promotor, External person
Award date9 Jan 2017
Place of PublicationWageningen
Publication statusPublished - 2017


Dive into the research topics of 'Microscale modeling of gas exchange during C4 photosythesis'. Together they form a unique fingerprint.

Cite this