Computational Life Science, Department of Biology, RWTH Aachen University, Aachen, Germany.
Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
PLoS Comput Biol. 2024 Sep 12;20(9):e1012445. doi: 10.1371/journal.pcbi.1012445. eCollection 2024 Sep.
Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions for ensemble of parameter sets and found no physiological benefit of a model assuming phycobilisome detachment. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. We suggest gene targets for overexpression under different illuminations to increase the yield. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.
蓝藻具有通过光驱动化学合成来彻底改变传统工业和农业实践的巨大潜力。为了充分利用其光合作用能力并提高产品产量,研究其与环境的复杂相互作用(包括光强和光谱)至关重要。数学模型为优化这方面的策略提供了有价值的见解。在这项研究中,我们提出了一个基于常微分方程的蓝藻 Synechocystis sp. PCC 6803 模型,以评估其在各种光源(包括单色光)下的性能。我们的模型可以再现多种生理上测量的数量,例如实验报告的通过四个主要途径分配电子、O2 释放以及环境和饱和 CO2 下碳固定的速率。通过捕获光合作用系统中不同组件之间的相互作用,我们的模型有助于理解驱动系统行为的基本机制。我们的模型定性地再现了各种光照条件下发出的荧光,复制了具有饱和脉冲的脉冲幅度调制(PAM)荧光测定实验。使用我们的模型,我们针对参数集的集合测试了蓝藻状态转变的四种假设机制,发现假设藻胆体脱落的模型没有生理益处。此外,我们在不同的光颜色和辐照度下评估了生物工艺生产的代谢控制。我们建议在不同光照下过表达基因靶标,以提高产量。通过提供蓝藻光合作用的全面计算模型,我们的工作增强了对光依赖性蓝藻行为的基本理解,并建立了第一个依赖波长的框架,以系统地测试它们用于生物催化的生产能力。