A mechanistic and flexible modeling framework for microalgal biomass production

Microalgae such as Chlamydomonas reinhardtii and Nannochloropsis oculata are promising micro-organisms for the sustainable production of biofuels, pigments, and proteins. Their ability to convert light and CO₂ into biomass makes them attractive for industrial applications, yet scaling up their cultivation remains challenging due to the complex interplay of environmental factors (e.g., light, nutrients, CO₂), especially in more realistic community contexts such as algal-bacterial consortia. We present a multiscale modeling framework based on Bio-Chemical Reaction Networks (BCRN), where microalgal behavior is described through elementary biochemical reactions (e.g., light capture, nutrient uptake, biomass production). These networks are translated into systems of ODEs using mass-action kinetics for population-level transformations (e.g., nutrient uptake, cell division), while light effects are incorporated through nonlinear algebraic terms. By leveraging timescale separation and applying model reduction techniques such as Tikhonov’s theorem, this formalism unifies and generalizes classical growth models; recovering logistic, Monod-like behaviors under appropriate assumptions. The modular structure of BCRN allows new interactions or regulations (e.g., microbial cross-feeding or competition) to be added simply by introducing reactions, without altering the overall mathematical framework. This flexibility makes our approach well-suited for studying microalgal dynamics in both monocultures and coculture systems.