News - Overview | Empowering Biological Manufacturing with Artificial Intelligence

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Overview | Empowering Biological Manufacturing with Artificial Intelligence

time2024/11/11

: Biomanufacturing is an advanced technology with great potential for constructing highly biomimetic three-dimensional human organs in vitro.

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This technology will help solve the problems of immune rejection and organ donor shortage in organ transplantation, assist doctors in developing personalized treatment plans for clinical patients, and replace animal experiments. Biomanufacturing typically involves interdisciplinary applications of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis.

Artificial intelligence (AI), with its outstanding capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships, as well as better integrating and applying them in biomanufacturing. In recent years, the development of the semiconductor and integrated circuit industries has driven rapid progress in computer processing capabilities. Artificial intelligence programs can learn and iterate multiple times in a short period of time, thereby gaining powerful automation capabilities for specific research content or problems.

So far, many artificial intelligence programs have been applied to various processes of biomanufacturing, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation models, accelerating the transformation and development of these technologies, and even changing traditional research models.

Sun Wei, Pang Yuan and others from Tsinghua University reviewed the significant changes and progress brought by artificial intelligence in the field of biomanufacturing, and explored its future application value and direction. The related paper titled AI for Biofabrication was published in Biofabrication (IF 8.2) Pub on November 4, 2024.

Schematic diagram of artificial intelligence integrated biomanufacturing. (A) A simple biomanufacturing flowchart consisting of three steps: preparation process, manufacturing process, evaluation and application. Growth factors (GFs) are a class of bioactive proteins or peptides that can regulate cell growth, proliferation, differentiation and survival. They are common ingredients in bio ink formulations. (B) Medical image preprocessing and feature extraction. (C) Model design and optimization. (D) Cell sorting and quality control.

(E) Optimization of bio ink and bio printing parameters. (F) Real time monitoring and compensation of the manufacturing process. (G) Cell state monitoring, analysis, and prediction. (H) Organizational model structure evaluation, taking vascular network formation as an example. (I) Biological testing of organizational models, taking organoids as an example. (J) Future prospects: drug development, screening, and administration. (K) Future prospects: Toxicity testing and evaluation of drugs and new compounds. (L) The development diagram of artificial intelligence and deep neural network models, in figures (B) to (K), the yellow arrows indicate that artificial intelligence is involved in the processing steps.

Medical image processing based on artificial intelligence. (A) Convolutional neural networks for whole tumor segmentation of neuroendocrine tumors aim to depict regions of interest with specific meanings in medical images and extract relevant features. This provides a reliable basis for clinical diagnosis and pathological research, helping doctors make more accurate diagnoses. The typical process of AI algorithm recognition and detection of medical diagnostic images is shown in the figure.

(B) Artificial intelligence algorithms can extract features from regions of interest in images. This image shows the conversion from scanning to feature maps. (C) AI can achieve tumor grading through the recognition of medical images. (D) AI can further output the overall malignancy prediction, risk bucket score, and prediction of cancer nodule localization of cancer cases through the overall analysis of images.

Structural design and cell sorting based on artificial intelligence. (A) Applying machine learning models that do not require prior data to bone scaffold design has improved the performance and repair capability of the scaffold. (B) Study on the influence of microfluidic chip structure on fluid dynamics. Three different flow carving devices alter the same inlet fluid flow schematic diagram (left), and research on artificial intelligence based micro column sequence prediction in the flow channel (right).

(C) Based on artificial intelligence, unlabeled imaging flow cytometry can predict DNA content and cell cycle stages using only bright and dark field features.

Optimization of biological links and manufacturing parameters based on artificial intelligence. (A) The flowchart of artificial intelligence in ink research; Artificial intelligence can evaluate the printability of ink or predict ink culture by analyzing ink performance tests and identifying and analyzing printing structures.

(B) Analysis of printability of bioink based on Bayesian optimization algorithm. (C) Artificial intelligence based bio ink formulation and printability prediction.

Real time monitoring of manufacturing processes based on artificial intelligence. (A) Squeeze bioprinting and EHDP real-time monitoring system, integrating AI for real-time monitoring of printed structures, including structural defects, abnormalities, and printability indicators.

(B) Non contact real-time monitoring of cell status, artificial intelligence can analyze patterns from indirectly measured electrical signals to monitor cell status. (C) A common application of microfluidic chips is the production of uniform microspheres. Integrate artificial intelligence into the microsphere power generation monitoring system, analyze power generation modes, and monitor microsphere production status.

Detection of biomanufacturing models based on artificial intelligence. (A) The trained AI model can predict the fluorescence labeling of unlabeled cells and analyze gene expression and other states. (B) AI can help analyze key indicators of tumor spheroid models and evaluate their migration and invasion abilities.

(C) Analysis and evaluation of artificial intelligence generating vascular networks on chips. (D) Using artificial intelligence algorithms to evaluate the growth and development status of organoids. (E) Evaluate and analyze cell states on different manufacturing surfaces through artificial intelligence.