Due to sustainability concerns, bio-based production capitalizing on microbes as cell factories is in demand to synthesize valuable products. Nevertheless, the nonhomogenous variations of the extracellular environment in bioprocesses often challenge the biomass growth and the bioproduction yield. To enable a more rational bioprocess optimization, we have established a model-driven approach that systematically integrates experiments with modeling, executed from flask to bioreactor scale, and using ferulic acid to vanillin bioconversion as a case study. The impacts of mass transfer and aeration on the biomass growth and bioproduction performances were examined using minimal small-scale experiments. An integrated model coupling the cell factory kinetics with the three-dimensional computational hydrodynamics of bioreactor was developed to better capture the spatiotemporal distributions of bioproduction. Full-factorial predictions were then performed to identify the desired operating conditions. A bioconversion yield of 94% was achieved, which is one of the highest for recombinant Escherichia coli using ferulic acid as the precursor.
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http://dx.doi.org/10.1002/bit.27571 | DOI Listing |
Clin Transl Sci
January 2025
Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
View Article and Find Full Text PDFNeural Netw
December 2024
CAS Key Laboratory of GIPAS, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. Electronic address:
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhancing data efficiency is a core issue in MARL. However, in the context of MARL, agent partially observed information leads to a lack of consideration for agent interactions and coordination from an ego perspective under the world model, which becomes the main obstacle to improving the data efficiency of current proposed MARL methods.
View Article and Find Full Text PDFCarcinogenesis
January 2025
Instituto de Investigaciones en Ciencias de la Salud, INICSA (CONICET - FCM UNC), 5016 Córdoba, Argentina.
Pancreatic cancer is a devastating malignancy in great need of new and more effective treatment approaches. In recent years, studies have indicated that nutritional interventions, particularly nutraceuticals, may provide novel avenues to modulate cancer progression. Here, our study characterizes the impact of ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), as a nutraceutical intervention in pancreatic cancer using a genetically engineered mouse model driven by KrasG12D and Trp53R172H.
View Article and Find Full Text PDFPlant Physiol
December 2024
Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Potsdam 14476, Germany.
Collections of insertional mutants have been instrumental for characterizing the functional relevance of genes in different model organisms, including Arabidopsis (Arabidopsis thaliana). However, mutations may often result in subtle phenotypes, rendering it difficult to pinpoint the function of a knocked-out gene. Here, we present a data-integrative modeling approach that enables predicting the effects of mutations on metabolic traits and plant growth.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Design and Art, Shaanxi University of Science & Technology, Xi'an 710026, China.
With the development of smart technology and the increasing variety of everyday products, factors influencing product service touchpoint design have become more diverse and complex. Existing service touchpoint design methods and models often focus narrowly on user research, co-design, and risk analyses, lacking a systematic approach. Consequently, they struggle to deliver solutions that align with user needs.
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