The introduction of heterologous pathways into microbial hosts often imposes a metabolic burden on the cell, arising from three major physiological constraint layers: competition for gene expression resources, limited precursor availability and flux distribution, and insufficient energy and redox supply. Although Pseudomonas putida KT2440 is considered a robust and metabolically versatile production host, it remains unclear which of these constraint layers primarily limits heterologous terpenoid production in this organism. Here, lycopene biosynthesis was used as a model system to systematically dissect these three potential sources of metabolic burden. A capacity-monitoring system revealed no clear reduction in transcriptional or translational capacity across the tested strains and cultivation conditions, indicating that general gene expression capacity was not the primary limiting factor. Instead, lycopene production depended strongly on promoter architecture and plasmid backbone, showing that regulatory design shaped pathway performance. Enhancing precursor supply by introducing a heterologous mevalonate (MVA) pathway substantially increased product titres, identifying precursor availability from the native MEP pathway as the dominant bottleneck. This conclusion was independently supported by exogenous mevalonate supplementation, which further increased lycopene accumulation but also revealed saturation at higher concentrations, suggesting that downstream pathway balance or enzyme capacity became limiting once precursor supply was relieved. Under controlled bioreactor conditions, lycopene titres increased from approximately 1 mg/L to nearly 25 mg/L, indicating that process conditions further modulate production performance, suggesting an additional contribution of process-dependent energy and redox constraints. Metabolic burden during heterologous lycopene production in P. putida is governed primarily by precursor availability rather than by limitations in general gene expression capacity. Regulatory properties of the vector system strongly influence pathway performance, while controlled cultivation conditions can further improve production by alleviating additional process-dependent constraints. Together, these findings provide a systematic framework for distinguishing constraint layers and guiding the optimisation of heterologous terpenoid production systems.
Carina Meiners, Lucas Hermann, Mishela Stoja et al.· Microbial Cell Factories· 0 citations
Integrating sensor measurements with complex numerical models for structural health monitoring holds great promise because both sources provide complementary and potentially high-fidelity sources of information. However, effectively assimilating data into computationally intensive models presents substantial challenges. Among these are noisy and potentially sparse data, simplifications and uncertainties in the computational model and strong nonlinearities that pose challenges to traditional assimilation approaches. While Bayesian updating offers a principled approach to data assimilation under uncertainty, it requires considerable numerical effort that is often not acceptable, in particular, if frequent and fast updates are required.
The present work takes [1] as a starting point, where a statistical version of the Finite Element method (statFEM) has been introduced. The main ingredients of statFEM are a propagation of uncertainty through a Finite Element model and (empirical) Bayesian updating that explicitly accounts for model misspecification. Subsequent work also considers Ensemble Kalman filtering as an alternative to linear Bayesian updating [2] and demonstrated its applicability in structural health monitoring contexts [3].
Here, we investigate the limits of updating methods, as presented in [1] or [4], and present a comparative computational analysis against alternative strategies such as Ensemble Kalman filtering for standard benchmark problems. In order to accelerate the prior computations, we employ surrogate models and further integrate adaptive sampling techniques. As a case study, we model aging material properties as spatially correlated random fields and assimilate noisy simulated measurements of structural responses. This allows us to demonstrate the efficient computation of stochastic priors and their assimilation via both the original statFEM and ensemble-based methods. Finally, we discuss the propagation of cracks and its implications for load-bearing capacity assessment within the data assimilation framework.
[1] Girolami, M., Febrianto, E., Yin, G., & Cirak, F. (2021). The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions. Computer Methods in Applied Mechanics and Engineering, 375, 113533.
[2] Duffin, C., Cripps, E., Stemler, T., & Girolami, M. (2021). Statistical finite elements for misspecified models. Proceedings of the National Academy of Sciences, 118(2), e2015006118.
[3] Muralidhar, N. K., Gräßle, C., Rauter, N., Mikhaylenko, A., Lammering, R. & Lorenz, D. A. (2023). Damage identification in fiber metal laminates using Bayesian analysis with model order reduction, Computer Methods in Applied Mechanics and Engineering, 403, 115737.
[4] Narouie, V., Wessels, H., Cirak, F., & Römer, U. (2025). Mechanical state estimation with a Polynomial-Chaos-Based Statistical Finite Element Method. Computer Methods in Applied Mechanics and Engineering, 441, 117970.
Lucas Hermann, Saddam Hijazi, C. Gräßle et al.· e-Journal of Nondestructive...· 0 citations
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