Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework

Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework’s success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.

The accumulation of rotavirus NSP3 dimers does not correlate with the extent of host cell translation inhibition

Aim: We aimed to determine the functionality of rotavirus NSP3 dimers. Materials & methods: We expressed rhesus rotavirus NSP3 and determined the kinetics of host cell translation inhibition and the levels of accumulated dimerization intermediates and dimers. Results: We observed a linear kinetics of host cell translation inhibition, which correlated well with the sum of the dimerization intermediates and dimers. Treatment with 17-dimethylaminoethylamino-17-demethoxygeldanamycin reduced the accumulation of NSP3 dimers and potentiated host cell translation inhibition. Conclusion: Our results show that NSP3 dimer formation does not correlate with host cell translation inhibition and suggest that both NSP3 dimers and dimerization intermediates are functional and inhibit host cell translation.