Modeling With Uncertainty Quantification Identifies Essential Features of a Non-Canonical Algal Carbon-Concentrating Mechanism
The thermoacidophilic red alga Cyanidioschyzon merolae survives its challenging environment likely in part by operating a carbon-concentrating mechanism (CCM). Here, we demonstrated that C. merolaes cellular affinity for CO2 is stronger than its rubisco affinity for CO2. This provided further evidence that C. merolae operates a CCM while lacking structures and functions characteristic of CCMs in other organisms. To test how such a CCM could function, we created a mathematical compartmental model of a simple CCM distinct from those previously described in detail. The results supported the feasibility of this proposed minimal and non-canonical CCM in C. merolae. To facilitate robust modeling of this process, we incorporated new physiological and enzymatic data into the model, and we additionally trained a surrogate machine-learning model to emulate the mechanistic model and characterized the effects of model parameters on key outputs. This parameter exploration enabled us to identify model features that influenced whether the model met experimentally-derived criteria for functional carbon-concentration and efficient energy usage. Such parameters included cytosolic pH, bicarbonate pumping cost and kinetics, cell radius, carboxylation velocity, number of thylakoid membranes, and CO2 membrane permeability. Our exploration thus suggested that a novel CCM could exist in C. merolae and illuminated essential features necessary for CCMs to function. SignificanceCarbon-concentrating mechanisms (CCMs) are processes which boost photosynthetic efficiency. By developing modeling approaches to robustly describe CCMs in organisms where biochemical data is limited, such as extremophile algae, we can better understand how organisms survive environmental challenges. We demonstrate an interdisciplinary modeling approach which efficiently sampled from large parameter spaces and identified features (e.g., compartment permeability, pH, enzyme characteristics) which determine the function and energy cost of a simple CCM. This approach is new to compartmental photosynthetic modeling, and could facilitate effective use of models to inform experiments and rational engineering. For example, engineering CCMs into crops may improve agricultural productivity, and could benefit from models defining the structural and biochemical features necessary for CCM function.