The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

The possibility of controlling the Calvin cycle has paramount implications for increasing the production of biomass. Multistationarity, as a dynamical feature of systems, is the first obvious candidate whose control could find biotechnological applications. Here we set out to resolve the debate on the multistationarity of the Calvin cycle. Unlike the existing simulation-based studies, our approach is based on a sound mathematical framework, chemical reaction network theory and algebraic geometry, which results in provable results for the investigated model of the Calvin cycle in which we embed a hierarchy of realistic kinetic laws. Our theoretical findings demonstrate that there is a possibility for multistationarity resulting from two sources, homogeneous and inhomogeneous instabilities, which partially settle the debate on multistability of the Calvin cycle. In addition, our tractable analytical treatment of the bifurcation parameters can be employed in the design of validation experiments.

Mathematical modeling of biological systems is a powerful tool to systematically investigate the functions of biological processes and their relationship with the environment. To obtain accurate and biologically interpretable predictions, a modeling framework has to be devised whose assumptions best approximate the examined scenario and which copes with the trade-off of complexity of the underlying mathematical description: with attention to detail or high coverage. Correspondingly, the system can be examined in detail on a smaller scale or in a simplified manner on a larger scale. In this thesis, the role of photosynthesis and its related biochemical processes in the context of plant metabolism was dissected by employing modeling approaches ranging from kinetic to stoichiometric models. The Calvin-Benson cycle, as primary pathway of carbon fixation in C3 plants, is the initial step for producing starch and sucrose, necessary for plant growth. Based on an integrative analysis for model ranking applied on the largest compendium of (kinetic) models for the Calvin-Benson cycle, those suitable for development of metabolic engineering strategies were identified. Driven by the question why starch rather than sucrose is the predominant transitory carbon storage in higher plants, the metabolic costs for their synthesis were examined. The incorporation of the maintenance costs for the involved enzymes provided a model-based support for the preference of starch as transitory carbon storage, by only exploiting the stoichiometry of synthesis pathways. Many photosynthetic organisms have to cope with processes which compete with carbon fixation, such as photorespiration whose impact on plant metabolism is still controversial. A systematic model-oriented review provided a detailed assessment for the role of this pathway in inhibiting the rate of carbon fixation, bridging carbon and nitrogen metabolism, shaping the C1 metabolism, and influencing redox signal transduction. The demand of understanding photosynthesis in its metabolic context calls for the examination of the related processes of the primary carbon metabolism. To this end, the Arabidopsis core model was assembled via a bottom-up approach. This large-scale model can be used to simulate photoautotrophic biomass production, as an indicator for plant growth, under so-called optimal, carbon-limiting and nitrogen-limiting growth conditions. Finally, the introduced model was employed to investigate the effects of the environment, in particular, nitrogen, carbon and energy sources, on the metabolic behavior. This resulted in a purely stoichiometry-based explanation for the experimental evidence for preferred simultaneous acquisition of nitrogen in both forms, as nitrate and ammonium, for optimal growth in various plant species. The findings presented in this thesis provide new insights into plant system's behavior, further support existing opinions for which mounting experimental evidences arise, and posit novel hypotheses for further directed large-scale experiments.

We investigated the systems response of metabolism and growth after an increase in irradiance in the nonsaturating range in the algal model Chlamydomonas reinhardtii. In a three-step process, photosynthesis and the levels of metabolites increased immediately, growth increased after 10 to 15 min, and transcript and protein abundance responded by 40 and 120 to 240 min, respectively. In the first phase, starch and metabolites provided a transient buffer for carbon until growth increased. This uncouples photosynthesis from growth in a fluctuating light environment. In the first and second phases, rising metabolite levels and increased polysome loading drove an increase in fluxes. Most Calvin-Benson cycle (CBC) enzymes were substrate-limited in vivo, and strikingly, many were present at higher concentrations than their substrates, explaining how rising metabolite levels stimulate CBC flux. Rubisco, fructose-1,6-biosphosphatase, and seduheptulose-1,7-bisphosphatase were close to substrate saturation in vivo, and flux was increased by posttranslational activation. In the third phase, changes in abundance of particular proteins, including increases in plastidial ATP synthase and some CBC enzymes, relieved potential bottlenecks and readjusted protein allocation between different processes. Despite reasonable overall agreement between changes in transcript and protein abundance (R-2 = 0.24), many proteins, including those in photosynthesis, changed independently of transcript abundance.