Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.

As a strategic emerging industry, biomanufacturing faces core challenges in achieving precise optimization and efficient scale-up of fermentation processes. This review focuses on two critical aspects of fermentation-real-time sensing and intelligent control-and systematically summarizes the advancements in online monitoring technologies, artificial intelligence (AI)-driven optimization strategies, and digital twin applications. First, online monitoring technologies, ranging from conventional parameters (e.g., temperature, pH, and dissolved oxygen) to advanced sensing systems (e.g., online viable cell sensors, spectroscopy, and exhaust gas analysis), provide a data foundation for real-time microbial metabolic state characterization. Second, conventional static control relying on expert experience is evolving toward AI-driven dynamic optimization. The integration of machine learning technologies (e.g., artificial neural networks and support vector machines) and genetic algorithms significantly enhances the regulation efficiency of feeding strategies and process parameters. Finally, digital twin technology, integrating real-time sensing data with multi-scale models (e.g., cellular metabolic kinetics and reactor hydrodynamics), offers a novel paradigm for lifecycle optimization and rational scale-up of fermentation. Future advancements in closed-loop control systems based on intelligent sensing and digital twin are expected to accelerate the industrialization of innovative achievements in synthetic biology and drive biomanufacturing toward higher efficiency, intelligence, and sustainability.
Artificial Intelligence ; Fermentation ; Bioreactors/microbiology* ; Neural Networks, Computer ; Algorithms ; Biotechnology/methods*

As green, sustainable, and environmentally friendly material processing processes using biological cells or enzymes to achieve substance conversion, bioprocesses play an increasingly important role in biomanufacturing. It is difficult to optimize bioprocesses because of the complex relationship at multiple levels and multiple scales. The knowledge of mesoscale behaviors is the key to understanding the dynamics of bioprocesses and to sort out the complex relationships of parameter variations in the spatial-temporal domain. Mesoscale numerical simulation paves a way for understanding these phenomena, and the integration of artificial intelligence (AI) and mesoscale simulation offers new vitality into the optimization of bioprocesses. This article reviews the progress in mesoscale simulation and AI optimization of bioprocesses and discusses the possible development directions, aiming to promote the development of this field.
Artificial Intelligence ; Biotechnology/trends* ; Computer Simulation

Stable isotope ¹³C labeling is an important tool to analyze cellular metabolic flux. The ¹³C distribution in intracellular metabolites can be detected via mass spectrometry and used as a constraint in intracellular metabolic flux calculations. Then, metabolic flux analysis algorithms can be employed to obtain the flux distribution in the corresponding metabolic reaction network. However, in addition to carbon, other elements such as oxygen in the nature also have natural stable isotopes (e.g., ¹⁷O, ¹⁸O). This makes the isotopic information of elements other than the ¹³C marker interspersed in the isotopic distribution measured by the mass spectrometry, especially that of the molecules containing many other elements, which leads to large errors. Therefore, it is essential to correct the mass spectrometry data before performing metabolic flux calculations. In this paper, we proposed a method for construction of correction matrix based on Python language for correcting the measurement errors due to natural isotope distribution. The method employed a basic power method for constructing the correction matrix with simple structure and easy coding implementation, which can be directly applied to data pre-processing in ¹³C metabolic flux analysis. The correction method was then applied to the intracellular metabolic flux analysis of ¹³C-labeled Aspergillus niger. The results showed that the proposed method was accurate and effective, which can serve as a reliable data correction method for accurate microbial intracellular metabolic flux analysis.
Metabolic Flux Analysis ; Isotope Labeling/methods* ; Carbon Isotopes/metabolism* ; Mass Spectrometry/methods* ; Metabolic Networks and Pathways

Currently, biomanufacturing technology and industry are receiving worldwide attention. However, there are still great challenges on bioprocess optimization and scale-up, including: lacing the process detection methods, which makes it difficult to meet the requirement of monitoring of key indicators and parameters; poor understanding of cell metabolism, which arouses problems to rationally achieve process optimization and regulation; the reactor environment is very different across the scales, resulting in low efficiency of stepwise scale-up. Considering the above key issues that need to be resolved, here we summarize the key technological innovations of the whole chain of fermentation process, i.e., real-time detection-dynamic regulation-rational scale-up, through case analysis. In the future, bioprocess design will be guided by a full lifecycle in-silico model integrating cellular physiology (spatiotemporal multiscale metabolic models) and fluid dynamics (CFD models). This will promote computer-aided design and development, accelerate the realization of large-scale intelligent production and serve to open a new era of green biomanufacturing.
Bioreactors ; Computer Simulation ; Fermentation ; Hydrodynamics

Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.
Genome/genetics* ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Models, Biological ; Proteomics

Isotope Dilution Mass Spectrometry (IDMS) is the most accurate method for high-throughput detection of intracellular metabolite concentrations, and the key is getting the corresponding fully uniformly(U) ¹³C-labeled metabolites to be measured. The conventional procedure for getting fully U ¹³C-labeled metabolites is through batch cultivation, but intracellular metabolites concentrations by this method are generally low. By applying U ¹³C-labeled glucose pulse, combined with fast sampling and quenching, mixture of fully U ¹³C-labeled intracellular metabolites was successfully extracted with higher concentration from Pichia pastoris G/DSEL fed with fully U ¹³C-labeled glucose as only carbon source. Quantitative results from liquid chromatography tandem mass spectrometry (LC-MS) and gas chromatography tandem mass spectrometry (GC-MS) show that concentrations of organic acids, sugar phosphates, amino acids and nucleotides were 2-10 folds higher than those without glucose pulse. Therefore, the glucose pulse method can efficiently improve the usage of fully U ¹³C-labeled glucose converting to ¹³C-labeled metabolites, and achieve the detection of intracellular metabolites with lower concentrate than the instrument detection limit.

The advances of industrial biotechnology highly depend on the development of industrial bioprocess researches. In China, we are facing several challenges because of a huge national industrial fermentation capacity. The industrial bioprocess development experienced several main stages. This work mainly reviews the development of the industrial bioprocess in China during the past 30 or 40 years: including the early stage kinetics model study derived from classical chemical engineering, researching method based on control theory, multiple-parameter analysis techniques of on-line measuring instruments and techniques, and multi-scale analysis theory, and also solid state fermentation techniques and fermenters. In addition, the cutting edge of bioprocess engineering was also addressed.
Bioengineering ; history ; Bioreactors ; Biotechnology ; Chemical Engineering ; China ; Fermentation ; History, 20th Century ; History, 21st Century

Carbon-limited continuous culture was used to study the relationship between the growth of Aspergillus niger and the production of glucoamylase. The result showed that when the specific growth rate was lower than 0.068 h(-1), the production of glucoamylase was growth-associated, when the specific growth rate was higher than 0.068 h(-1), the production of glucoamylase was not growth-associated. Based on the result of continuous culture, the Monod dynamics model of glucose consumption of A. niger was constructed, Combining Herbert-Pirt equation of glucose and oxygen consumption with Luedeking-Piret equation of enzyme production, the black-box model of Aspergillus niger for enzyme production was established. The exponential fed-batch culture was designed to control the specific growth rate at 0.05 h(-1) by using this model and the highest yield for glucoamylase production by A. niger reached 0.127 g glucoamylase/g glucose. The black-box model constructed in this study successfully described the glucoamylase production by A. niger and the result of the model fitted the measured value well. The black-box model could guide the design and optimization of glucoamylase production by A. niger.
Aspergillus niger ; metabolism ; Batch Cell Culture Techniques ; Carbon ; Culture Media ; Glucan 1,4-alpha-Glucosidase ; biosynthesis ; Glucose ; Industrial Microbiology ; methods ; Oxygen

Filamentous fungi are widely used in industrial fermentation. Particular fungal morphology acts as a critical index for a successful fermentation. To break the bottleneck of morphological analysis, we have developed a reliable method for fungal morphological analysis. By this method, we can prepare hundreds of pellet samples simultaneously and obtain quantitative morphological information at large scale quickly. This method can largely increase the accuracy and reliability of morphological analysis result. Based on that, the studies of Aspergillus niger morphology under different oxygen supply conditions and shear rate conditions were carried out. As a result, the morphological responding patterns of A. niger morphology to these conditions were quantitatively demonstrated, which laid a solid foundation for the further scale-up.
Aspergillus niger ; cytology ; Fermentation ; Industrial Microbiology ; Reproducibility of Results