Biopharmaceutical products, particularly monoclonal antibodies (mAbs), have gained prominence in the pharmaceutical market due to their high specificity and efficacy. As these products are projected to constitute a substantial portion of global pharmaceutical sales, the application of machine learning models in mAb development and manufacturing is gaining momentum. This paper addresses the critical need for uncertainty quantification in machine learning predictions, particularly in scenarios with limited training data. Leveraging ensemble learning and Monte Carlo simulations, our proposed method generates additional input samples to enhance the robustness of the model in small training datasets. We evaluate the efficacy of our approach through two case studies: predicting antibody concentrations in advance and real-time monitoring of glucose concentrations during bioreactor runs using Raman spectra data. Our findings demonstrate the effectiveness of the proposed method in estimating the uncertainty levels associated with process performance predictions and facilitating real-time decision-making in biopharmaceutical manufacturing. This contribution not only introduces a novel approach for uncertainty quantification but also provides insights into overcoming challenges posed by small training datasets in bioprocess development. The evaluation demonstrates the effectiveness of our method in addressing key challenges related to uncertainty estimation within upstream cell cultivation, illustrating its potential impact on enhancing process control and product quality in the dynamic field of biopharmaceuticals.

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time- and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

A key problem toward the use of microorganisms as bio-factories is reaching and maintaining cellular communities at a desired density and composition so that they can efficiently convert their biomass into useful compounds. Promising technological platforms for the real time, scalable control of cellular density are bioreactors. In this work, we developed a learning-based strategy to expand the toolbox of available control algorithms capable of regulating the density of a \textit{single} bacterial population in bioreactors. Specifically, we used a sim-to-real paradigm, where a simple mathematical model, calibrated using a few data, was adopted to generate synthetic data for the training of the controller. The resulting policy was then exhaustively tested in vivo using a low-cost bioreactor known as Chi.Bio, assessing performance and robustness. In addition, we compared the performance with more traditional controllers (namely, a PI and an MPC), confirming that the learning-based controller exhibits similar performance in vivo. Our work showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia.

In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty associated with bioprocesses, high data acquisition cost, and lack of data availability to learn complex relations in bioprocesses. To deal with these challenges, we propose to use a statistical machine learning approach, multi-fidelity Gaussian process, for process modelling in biomanufacturing. Gaussian process regression is a well-established technique based on probability theory which can naturally consider uncertainty in a dataset via Gaussian noise, and multi-fidelity techniques can make use of multiple sources of information with different levels of fidelity, thus suitable for bioprocess modeling with small data. We apply the multi-fidelity Gaussian process to solve two significant problems in biomanufacturing, bioreactor scale-up and knowledge transfer across cell lines, and demonstrate its efficacy on real-world datasets.

Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community.

Pharmaceutical manufacturers are increasingly interested in the tenets of Industry 4.0, including the use of digital twins to simulate, test and optimize manufacturing processes on a computer before using them in production, according to technology advisory firm ABI Research. It projects spending by pharmaceutical manufacturers on data analytics tools--including the digital twin -- to grow by 27% over the next seven years, to reach $1.2 billion in 2030. As with other manufacturers, pharmaceutical makers plan to use the digital tools to boost productivity and to track their operations. Toronto-based Basetwo recently moved into this market with its software-as-a-service (SaaS) artificial intelligence (AI) platform. Today, the year-old company announced an upcoming $3.8 million seed financing round led by Glasswing Ventures and Argon Ventures.

But growing usable human tendon cells--which need to stretch and twist--has proved trickier. Over the past two decades, scientists have encouraged engineered tendon cells and tissue to grow and mature by repeatedly stretching them in one direction. However, this approach has so far failed to produce fully functional tissue grafts that could be used clinically, in human bodies. A new study, published in Nature Communications Engineering today, shows how humanoid robots could be used to make engineered tendon tissue that is more like the real thing. "The clinical need is clearly there," says Pierre-Alexis Mouthuy from the University of Oxford, who led the team.

A new artificial intelligence invention by Hypergiant Industries could prove to be the solution to the world's carbon dioxide problem. The company is launching the second generation Eos Bioreactor, currently still a prototype, that can be used to absorb excess carbon dioxide from the atmosphere and give out oxygen. Besides its ability to reduce environmental pollution, the new AI-based bioreactor also improves health. The excessive presence of carbon dioxide in the atmosphere has led to a steady rise in the average global temperatures over the years. A National Geographic report states that ocean levels will rise by up to 2.3 feet by 2050 due to melting glaciers.

Algae, that green scum often seen on the surface of ponds, and credited with harmful ocean algal blooms that kill ocean life might just hold an important key to addressing climate change. Algae, much like trees, uses carbon dioxide to conduct photosynthesis, sequestering CO2 as it grows. Hypergiant, an AI products and solutions company, is harnessing this unique power of algae in its latest technology, the EOS bio-reactor which uses AI to optimize algae growth and carbon sequestration. Its bio-reactor is built to hook up to HVAC systems found in large industrial buildings, skyscrapers and apartment buildings which are some of the biggest contributors to global warming from the CO2 emitted through their energy usage and air conditioning systems. The science is clear that we must not only cut our carbon emissions as a means to stop the irreversible harm of climate change and limit global warming but that we also need to take carbon out of the atmosphere to stay within the stated target 1.5 C of the Paris Climate Agreement.