## 3 VFA as Bioplastic Precursors
Among the available approaches, energy-based metabolic models are currently the most effective for capturing the intricate dynamics of these processes. These models operate on the assumption that microorganisms prioritize metabolic pathways yielding the highest net ATP gain, thereby determining which VFAs dominate under specific conditions. This makes energy-based models particularly valuable for predicting outcomes across varying operational parameters, including pH and substrate type [95]. Earlier models were based on fixed stoichiometry, which assumed constant reaction pathways without accounting for changes in operational conditions [96]. Variable stoichiometry models were later introduced, offering greater flexibility by incorporating operational factors such as pH and substrate composition. However, these models still lacked the precision needed for complex anaerobic processes, as they were more aligned with kinetics-driven aerobic systems like those described by ASM-family models [97]. In contrast, energy-based metabolic models consider thermodynamic constraints, which are critical in anaerobic environments where energy supply is low, and metabolic conversions are limited by equilibrium conditions. Energy-based metabolic models have also been applied to protein substrates [95]. One notable example is the model developed by [PERSON] et al. [98], which was among the first to predict VFA profiles from glucose under varying pH conditions. This model demonstrated that butyric acid dominates at low pH, while acetic acid and ethanol prevail at higher pH levels. However, initial predictions did not fully align with experimental observations, where acetic acid often co-dominates with butyric acid at a low pH. Subsequent refinements by [PERSON] et al. [99] included electron bifurcation in butyrate synthesis pathways and homoacetogenesis processes, successfully improving predictions of acetic and butyric acid yields at low pH. Later, [PERSON] et al. [100] developed a framework to predict VFA derived from protein fermentation, accounting for amino acid interactions and NADH competition. This model highlighted how external pH influences metabolic pathways, enabling accurate predictions of VFA composition for defined amino acid profiles. Furthermore, this approach has been extended to co-fermentation systems, where mixtures of carbohydrates and proteins, such as glucose and gelatin, were modeled. These studies revealed that glucose supplementation alters the NADH balance, favoring pathways that maximize ATP yields. However, the model's effectiveness depends heavily on detailed substrate characterization, particularly the amino acid profile of proteins used as feedstock [101]. Building on these advancements, [PERSON] et al. [102] proposed a novel workflow that integrates mathematical modeling with experimental validation to further refine the bioprocess design. This approach was applied to a defined co-culture of _Pediococcus pentosaceus_ and _Megasphaen cerevisiae_ to optimize the production of both odd- and even-carbon VFAs from food waste. An unstructured kinetic model was developed and calibrated to predict metabolic pathways, enabling the design of experiments aimed at increasing the selectivity for specific VFAs. Experimental validation demonstrated the model's capacity to enhance process outcomes. For even-carbon VFAs, the caproate yield increased by 38%, while experiments targeting odd-carbon VFAs resulted in a significant shift in product distribution. Butyrate and caproate yields decreased by 62% and 94%, respectively, whereas propionate emerged as a dominant product, and valerate yield increased from 0.007 to 0.085 g of valerate per gram of consumed sugar. This workflow effectively utilized in silico modeling to identify optimal operational conditions, reducing the need for extensive trial-and-error experimentation.