Hybrid semiparametric systems for quantitative sequence-activity modeling of synthetic biological parts

Towards in silico Process Modeling for Vaccines

Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report on the modeling competencies required to achieve it. By inspecting the most frequent vaccine process units, we identify fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments as the pillars for vaccine development. In addition, we craft a generic pathway for accommodating the modeling competencies into an in silico process development strategy.

Plasmid Design for Tunable Two-Enzyme Co-Expression Promotes Whole-Cell Production of Cellobiose

Catalyst development for biochemical cascade reactions often follows a "whole-cell-approach" in which a single microbial cell is made to express all required enzyme activities. Although attractive in principle, the approach can encounter limitations when efficient overall flux necessitates precise balancing between activities. This study shows an effective integration of major design strategies from synthetic biology to a coherent development of plasmid vectors, enabling tunable two-enzyme co-expression in E. coli, for whole-cell-production of cellobiose. An efficient transformation of sucrose and glucose into cellobiose by a parallel (countercurrent) cascade of disaccharide phosphorylases requires the enzyme co-expression to cope with large differences in specific activity of cellobiose phosphorylase (14 U mg^-1 ) and sucrose phosphorylase (122 U mg^-1 ). Mono- and bicistronic co-expression strategies controlling transcription, transcription-translation coupling or plasmid replication are analyzed for effect on activity and stable producibility of the whole-cell-catalyst. A key role of bom (basis of mobility) for plasmid stability dependent on the ori is reported and the importance of RBS (ribosome binding site) strength is demonstrated. Whole cell catalysts show high specific rates (460 µmol cellobiose min^-1  g^-1 dry cells) and performance metrics (30 g L^-1 ; ∼82% yield; 3.8 g L^-1 h^-1 overall productivity) promising for cellobiose production.

Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation

Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the correct model structure to quantify the complex biological mechanism poses a severe challenge for the construction of kinetic models, while the lack of data due to the time-consuming experiments greatly impedes applications of most data-driven models. This study proposes the use of an innovative hybrid modelling approach that consists of a simple kinetic model to govern the overall process dynamic trajectory and a data-driven model to estimate mismatch between the kinetic equations and the real process. An advanced automatic model structure identification strategy is adopted to simultaneously identify the most physically probable kinetic model structure and minimum number of data-driven model parameters that can accurately represent multiple data sets over a broad spectrum of process operating conditions. Through this hybrid modelling and automatic structure identification framework, a highly accurate mathematical model was constructed to simulate and optimise an algal lutein production process. Performance of this hybrid model for long-term predictive modelling, optimisation, and online self-calibration is demonstrated and thoroughly discussed, indicating its significant potential for future industrial application.