CHAPTER 6
AI in Biotechnology and Industrial Life Sciences
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6.1 AI APPLICATIONS IN BIOTECHNOLOGY AND BIOENGINEERING
Artificial Intelligence (AI) has emerged as an important enabling technology in biotechnology and bioengineering, where biological systems generate complex, high-dimensional, and often heterogeneous datasets. Conventional biological experimentation is frequently time-consuming, expensive, and dependent on repeated laboratory trials. AI and machine learning (ML) provide computational approaches for identifying patterns in genomic, proteomic, metabolomic, imaging, and bioprocess datasets and using these patterns to support prediction, design, optimization, and decision-making. Consequently, AI is increasingly being integrated into areas such as protein engineering, metabolic engineering, synthetic biology, drug discovery, fermentation, bioprocess optimization, and biomaterial development (Dunn et al., 2020; Peng et al., 2026). (Figure 6.1)
Figure 6.1: AI Applications in Biotechnology and Bioengineering
One of the most significant applications of AI in biotechnology is protein structure prediction and protein engineering. Proteins perform essential biological functions, and understanding their three-dimensional structures is
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critical for designing enzymes, antibodies, therapeutics, and industrial catalysts.
Traditional experimental techniques for determining protein structures can require substantial time and resources. Deep-learning systems such as AlphaFold demonstrated that AI can predict protein structures with remarkable accuracy from amino acid sequences, substantially improving computational structural biology (Jumper et al., 2021). More recent machine-learning approaches have expanded from structure prediction toward functional protein design, allowing researchers to explore protein sequences and identify candidate variants with desirable characteristics. Such approaches can support the development of enzymes with improved stability, catalytic activity, specificity, or resistance to harsh industrial conditions (Notin et al., 2024).
AI is also transforming metabolic engineering and synthetic biology. Metabolic engineering involves modifying microorganisms or cells so that they produce valuable compounds such as biofuels, pharmaceuticals, amino acids, organic acids, enzymes, and other industrial chemicals. Biological pathways can contain numerous interacting genes, enzymes, metabolites, and regulatory mechanisms, making it difficult to predict the consequences of individual genetic modifications. Machine-learning models can analyze omics data and experimental results to identify relationships between genetic modifications and production characteristics. These models can subsequently guide the selection of metabolic pathways, predict promising gene combinations, and prioritize experiments. Machine learning has therefore been applied to pathway construction, pathway optimization, strain engineering, and scale-up activities (Dunn et al., 2020).
Another important application is enzyme engineering. Enzymes are widely used in food processing, detergents, pharmaceuticals, agriculture, biofuels, and chemical manufacturing. Conventional enzyme engineering often relies on directed evolution, in which large numbers of variants are experimentally generated and screened. AI can reduce the number of experimental candidates by learning relationships between amino acid sequences, structures, and functional properties. Protein language models and generative AI can identify potentially useful sequences, while predictive models can estimate properties such as enzyme activity, thermostability, and substrate specificity before
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laboratory testing. Recent research indicates that AI-assisted protein design is moving toward integrated models that consider protein sequences, structures, metabolic pathways, and cellular phenotypes simultaneously (Notin et al.,
2024). In bioprocess engineering, AI can improve the production of biological products at laboratory, pilot, and industrial scales. Fermentation and cell-culture processes depend on numerous parameters, including temperature, pH, dissolved oxygen, nutrient concentration, agitation speed, feed rate, and biomass concentration. Small changes in these variables can significantly influence yield and product quality. Machine-learning models can learn from historical process data and predict production outcomes under different operating conditions. AI can consequently support process optimization, anomaly detection, predictive maintenance, and real-time control. This is particularly valuable in biopharmaceutical manufacturing, where upstream processes involving living cells can generate complex datasets while experimental data may remain relatively limited (Peng et al., 2026). AI additionally supports automated experimentation and closed-loop biotechnology. In conventional research, scientists design an experiment, perform laboratory work, analyze the results, and then design the next experiment. AI can partially automate this cycle by selecting experiments that are expected to provide the greatest amount of useful information. When combined with laboratory robotics, automated instruments, and high-throughput screening, AI systems can iteratively generate hypotheses, conduct experiments, analyze results, and recommend subsequent experiments. This approach has the potential to reduce experimental workload and accelerate optimization of biological systems (Duong-Trung et al., 2022). Drug discovery and biopharmaceutical development represent another major area in which biotechnology and AI intersect. AI can analyze molecular structures, biological targets, genomic information, and experimental data to support target identification, virtual screening, molecular property prediction, and candidate optimization. Generative models can also propose new molecular structures with selected characteristics. In protein and antibody engineering, machine-learning systems can help identify candidate binders and optimize molecular properties. These capabilities can reduce the number of 98
compounds that must be experimentally evaluated and help researchers prioritize promising candidates.
Nevertheless, AI-generated candidates still require laboratory validation, preclinical studies, and clinical evaluation because computational predictions cannot completely reproduce the complexity of biological systems.
Beyond pharmaceuticals, AI has applications in industrial biotechnology, including biofuel production, sustainable chemical manufacturing, food biotechnology, agricultural biotechnology, and biomaterial development. AI can help identify microorganisms capable of producing useful compounds, optimize fermentation conditions, predict material properties, and improve biological production efficiency. In energy biotechnology, for example, AI-assisted enzyme and metabolic engineering can contribute to the development of microbial systems capable of producing fuels and other valuable products more efficiently (Dunn et al., 2020; 2026).
Despite these opportunities, AI adoption in biotechnology faces several challenges. Biological datasets can be incomplete, noisy, biased, or difficult to standardize. Many biotechnology experiments are expensive, resulting in small datasets that can limit model performance. Furthermore, biological systems are highly context-dependent, meaning that a model trained under one experimental condition may not perform equally well under another. Interpretability, reproducibility, data privacy, computational requirements, and experimental validation also remain important concerns. Therefore, AI should generally be viewed as a decision-support and discovery technology rather than a complete replacement for biological experimentation.
Overall, AI is creating a shift from predominantly trial-and-error biotechnology toward data-driven, predictive, and increasingly automated biological engineering. Its greatest value lies in combining computational prediction with laboratory experimentation. As protein-language models, generative AI, automated laboratories, multi-omics integration, and digital bioprocessing continue to develop, AI is likely to become increasingly important in designing biological systems, optimizing industrial processes, and accelerating the development of biotechnology products.
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6.2 SYNTHETIC BIOLOGY AND INTELLIGENT BIOPROCESSING
Synthetic biology and artificial intelligence (AI) are increasingly converging to transform the design, engineering, and production of biological systems. Synthetic biology applies engineering principles to biological components, enabling researchers to construct or redesign cells, genetic circuits, metabolic pathways, and enzymes for specific purposes. AI and machine learning (ML), in contrast, provide computational methods for identifying patterns in large biological datasets, predicting system behaviour, and selecting promising designs. Their integration is creating a more data-driven approach to biotechnology in which biological systems can be designed, tested, analysed, and optimized through iterative computational and experimental cycles (Goshisht, 2024). (Figure 6.2)
Figure 6.2: Synthetic Biology and Intelligent Bioprocessing
6.2.1 AI-Enabled Synthetic Biology
A central framework in synthetic biology is the Design–Build–Test–Learn (DBTL) cycle. In the design stage, researchers identify biological components or pathways that may achieve a desired function. The build stage involves constructing the selected genetic or cellular system, while the test stage evaluates its performance experimentally. Finally, the learn stage uses experimental data to identify successful designs and guide the next cycle. AI
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can strengthen each stage, particularly by helping researchers predict which designs are most likely to work before laboratory resources are committed.
Machine learning can analyse genomic, transcriptomic, proteomic, metabolomic, and experimental datasets to identify relationships between genotype, phenotype, and environmental conditions. For example, ML models can predict gene expression, enzyme activity, protein function, pathway performance, and cellular responses. Such predictions can reduce the number of experimental combinations that need to be tested. In protein engineering, recent advances in machine learning have enabled computational design and optimization of proteins with desired structural or functional properties, expanding the search space beyond what can practically be explored through conventional laboratory evolution (Notin et al., 2024).
AI is also being used for the design of genetic regulatory elements and metabolic pathways. Instead of relying exclusively on trial-and-error experimentation, researchers can use predictive models to estimate how changes in promoters, enzymes, genes, or pathway structures may influence cellular performance. This is particularly valuable because biological systems contain complex interactions and nonlinear relationships that are difficult to model using conventional approaches alone. Resource competition and cellular burden, for instance, can cause engineered genetic systems to behave differently from their theoretical designs, making quantitative and data-driven approaches increasingly important (Di Blasi et al., 2024).
6.2.2 Intelligent Bioprocessing
Synthetic biology produces engineered biological systems, but successful industrial biotechnology also requires efficient processes for growing cells and converting biological substrates into useful products. This is the domain of intelligent bioprocessing, where AI, sensors, automation, and advanced process models are combined to monitor and optimize biological production.
Industrial bioprocesses such as fermentation and cell culture involve numerous interacting variables, including temperature, pH, dissolved oxygen, nutrient concentration, agitation, aeration, biomass concentration, metabolite levels, and feeding rates. These variables can change dynamically during production. Traditional control systems may struggle to capture all these nonlinear
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interactions. ML models can instead learn relationships between process variables and important outcomes such as product yield, productivity, biomass growth, and product quality (Mondal et al., 2023).
One important application is the development of soft sensors. Many important biological variables cannot be measured continuously because direct measurement is expensive, technically difficult, or requires laboratory sampling. AI models can estimate such variables indirectly by combining available sensor measurements. This allows operators to obtain near-real-time information about the biological state of a process and make faster control decisions. AI can also support fault detection, process monitoring, parameter optimization, and predictive control.
6.2.3 AI for Metabolic Pathway and Strain Optimization
An important connection between synthetic biology and intelligent bioprocessing is the development of microbial cell factories. Microorganisms such as Escherichia coli, yeast, and other industrial hosts can be genetically engineered to produce pharmaceuticals, enzymes, biofuels, organic acids, specialty chemicals, and other valuable products.
AI can assist in identifying metabolic bottlenecks and predicting how modifications to genes or enzymes may affect product formation. ML-based pathway optimization can support genome-scale metabolic modelling, enzyme engineering, regulatory-element design, and selection of promising pathway configurations. Data-driven approaches are particularly useful when experimental datasets are sufficiently large to capture complex biological relationships (Jin et al., 2023).
Another emerging approach is autonomous metabolic regulation, in which engineered cells dynamically adjust metabolic activity according to environmental or intracellular signals. Such systems can redirect cellular resources between growth and product formation, potentially improving production efficiency while reducing metabolic burden (Ream & Prather,
2024).
6.2.3 Digital Twins and Autonomous Bioprocesses
The next stage of intelligent bioprocessing involves digital twins-virtual representations of biological production systems that combine process data,
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mathematical models, sensors, and AI. A digital twin can continuously receive information from a physical bioreactor and use computational models to predict future process behaviour. The system can then support optimization or, in advanced applications, automated process control.
Recent research indicates that hybrid digital-twin approaches combining mechanistic models with ML can provide advantages over relying exclusively on either approach. Mechanistic models incorporate known biological and engineering principles, whereas ML models can capture complex patterns that are difficult to describe mathematically. Their combination can therefore improve monitoring, optimization, and control in bioprocessing (On digital twins in bioprocessing, 2025).
6.2.4 Challenges and Future Perspectives
Despite its potential, AI-enabled synthetic biology faces several challenges. High-quality biological datasets are often limited, heterogeneous, and difficult to standardize. Models trained on one organism, laboratory, or process may not generalize effectively to another. Interpretability is another concern because highly complex models may produce accurate predictions without providing clear biological explanations. In industrial environments, scalability, sensor integration, cybersecurity, regulatory compliance, and validation are also important considerations.
Future systems are likely to combine AI, synthetic biology, robotics, high-throughput experimentation, multi-omics, IoT sensors, and digital twins into integrated autonomous platforms. Such systems could continuously design biological variants, conduct experiments, analyse results, and update predictive models. This would move biotechnology from predominantly human-guided experimentation toward increasingly automated and adaptive biological engineering. AI-enabled programming of biology is already extending from nucleic acids and proteins toward increasingly complex cellular systems (Abudayyeh & Gootenberg, 2024).
Overall, the integration of synthetic biology and intelligent bioprocessing represents a major transition in biotechnology. Synthetic biology provides the ability to engineer biological systems, while AI provides the computational intelligence required to explore, predict, and optimize those systems. Together,
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they can accelerate the development of engineered organisms, improve industrial production efficiency, reduce experimental costs, and support more sustainable biomanufacturing. The long-term objective is not simply to automate individual laboratory tasks, but to establish intelligent biological production systems capable of learning from data and continuously improving their performance.
6.3 AI IN FOOD BIOTECHNOLOGY AND SAFETY ANALYSIS
Artificial intelligence (AI) is increasingly becoming an important enabling technology in food biotechnology, food quality assessment, and food safety management. The food sector generates large and complex datasets from spectroscopy, chromatography, mass spectrometry, biosensors, genomic analysis, imaging systems, production processes, and supply-chain monitoring. Conventional analytical approaches often require substantial laboratory time, specialized equipment, and expert interpretation. AI and machine learning (ML) can complement these approaches by identifying patterns in multidimensional datasets, predicting quality and safety outcomes, and supporting faster decision-making (Yi et al., 2024). Recent research indicates that AI is moving beyond experimental applications toward integrated food-safety systems involving sensing, prediction, traceability, and risk management. (Figure 6.3)
Figure 6.3: AI in Food Biotechnology and Safety Analysis
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6.3.1 AI-Based Food Quality and Safety Assessment
One of the major applications of AI in food biotechnology is the rapid assessment of food quality and safety indicators. Machine-learning algorithms can process data generated through near-infrared (NIR), hyperspectral imaging, Raman spectroscopy, nuclear magnetic resonance (NMR), gas chromatography, and mass spectrometry. These analytical techniques produce complex chemical and spectral profiles that may be difficult to interpret using conventional statistical methods alone. AI models, including artificial neural networks, support vector machines, random forests, and deep-learning architectures, can learn relationships between analytical signals and food characteristics such as freshness, moisture, composition, oxidation, contamination, and authenticity (Yi et al., 2024).
Computer vision and deep learning also provide important tools for automated inspection. Cameras and hyperspectral imaging systems can capture information about colour, texture, surface defects, foreign materials, and physical deterioration. Convolutional neural networks (CNNs) can subsequently classify food products or identify defects with limited manual intervention. Such systems are particularly useful in high-throughput processing environments where continuous human inspection can be expensive, inconsistent, and difficult to scale.
6.3.2 Detection of Foodborne Pathogens
Foodborne microorganisms, including Salmonella, Listeria monocytogenes, Escherichia coli, and other pathogens, represent a major food-safety concern. Traditional microbiological testing can require enrichment, culturing, biochemical identification, and laboratory confirmation, which may delay corrective action. AI-assisted detection combines machine learning with biosensors, microscopy, spectroscopy, molecular diagnostics, and other rapid analytical technologies to accelerate pathogen identification.
ML-powered biosensors can process signals generated by biological recognition elements and improve classification of microorganisms and other contaminants.
Recent research has demonstrated applications of ML-enabled biosensing for microorganisms, antibiotics, pesticides, mycotoxins, heavy metals, anions, and
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persistent organic pollutants (Hassan et al., 2025). More recent systematic research has expanded this perspective by examining AI-assisted microscopy, spectroscopy, biosensors, and sensor-based systems as components of broader food-safety frameworks (Schirone et al., 2026).
AI can also contribute to foodborne outbreak investigation. Machine-learning models can analyse pathogen genomes, epidemiological information, geographic patterns, food-trade data, inspection records, and other heterogeneous datasets to identify relationships that may not be readily apparent through conventional analysis. Earlier research has highlighted applications of ML in pathogen source attribution, antibiotic-resistance prediction, outbreak detection, and food-safety risk assessment (Larkin et al.,
2021).
6.3.3 Detection of Adulteration and Contaminants
Food adulteration is another area in which AI offers considerable potential. Adulteration may involve substitution of expensive ingredients, addition of unauthorized substances, dilution, mislabelling, or contamination. AI can compare the chemical, spectral, visual, or molecular fingerprint of a sample with reference datasets to identify deviations from expected characteristics.
For example, AI-assisted spectroscopy can be used to distinguish authentic and adulterated food products, while metabolomic and mass-spectrometric datasets can be analysed using ML algorithms to identify characteristic molecular patterns. This approach is particularly valuable for complex products such as spices, oils, dairy products, herbal materials, beverages, and processed foods. Reviews of AI and ML applications in food integrity indicate that these technologies can strengthen authenticity verification, quality control, and supply-chain resilience (Gbashi & Njobeh, 2024).
Mycotoxin detection is another promising application. Mycotoxins are toxic compounds produced by certain fungi and may contaminate cereals, nuts, spices, and other agricultural commodities. ML models can analyse spectral or imaging data to predict contamination and support rapid screening before products enter the food chain.
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However, the reliability of these systems depends strongly on representative datasets, appropriate model validation, and transparent reporting of model parameters.
6.3.4 Predictive Food Safety and Risk Management
AI is increasingly being applied not only to detect existing contamination but also to predict potential food-safety risks. Predictive models can integrate environmental conditions, temperature, humidity, processing parameters, storage duration, microbial growth characteristics, and historical contamination data. These models can estimate the probability of spoilage or microbial growth and support preventive interventions.
Integration with the Internet of Things (IoT) can further improve this capability. Sensors installed throughout production and storage environments can continuously generate data, while AI models analyse these data in real time. Abnormal temperature patterns, unexpected changes in microbial indicators, or deviations from processing conditions can trigger early warnings. Consequently, food-safety management can become more proactive rather than relying exclusively on end-product testing.
6.3.5 AI in Food Biotechnology and Bioprocess Optimization
Beyond safety analysis, AI has applications in food biotechnology itself. Fermentation processes, enzyme production, microbial cultures, alternative proteins, and cultured-food technologies involve numerous interacting biological and environmental variables. ML can help identify relationships among parameters such as pH, temperature, nutrient composition, oxygen availability, inoculum concentration, and fermentation time.
In emerging cultured-meat biotechnology, for example, ML has been investigated for cell-line development, culture-media optimization, microscopy and image analysis, and bioprocess optimization. Such approaches may reduce the number of experimental iterations required to identify suitable conditions and thereby accelerate research and development.
6.3.6 Challenges and Future Perspectives
Despite its potential, AI should not be viewed as a replacement for laboratory science, food microbiologists, or regulatory expertise. The quality of an AI model is highly dependent on the quality, quantity, diversity, and
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representativeness of its training data. Models trained on limited datasets may perform well under laboratory conditions but fail when applied to different food matrices, geographic regions, processing environments, or contamination levels. Issues of data standardization, model interpretability, reproducibility, cybersecurity, privacy, and regulatory acceptance therefore remain important.
Another challenge is the “black-box” nature of some deep-learning systems. In food-safety decisions, stakeholders may need to understand why a system classified a sample as contaminated or unsafe. Explainable AI can therefore become increasingly important for regulatory and industrial applications. Recent research also emphasizes the need for appropriate governance, open and high-quality datasets, validation across real-world conditions, and meaningful human oversight.
Overall, AI is transforming food biotechnology from primarily reactive testing toward intelligent, predictive, and data-driven management. Its integration with biosensors, spectroscopy, genomics, computer vision, IoT, and automated processing systems could enable faster detection of hazards, improved product authenticity, optimized bioprocesses, and more responsive food-safety systems. The future of AI in food biotechnology will therefore depend not only on increasingly sophisticated algorithms but also on reliable data, interdisciplinary collaboration, validation, regulatory frameworks, and responsible human supervision.
6.4 INDUSTRIAL AUTOMATION AND COMPUTATIONAL BIOTECHNOLOGY
Industrial biotechnology is increasingly shifting from manually controlled, experience-driven production toward data-driven, automated, and computationally optimized systems. The integration of artificial intelligence (AI), machine learning (ML), robotics, process sensors, computational biology, and digital twins is creating a new generation of intelligent biomanufacturing systems. These technologies can support the design of biological systems, optimize fermentation and cell culture, improve process control, reduce production variability, and accelerate the development of industrially valuable products. This convergence is often associated with Bioprocessing 4.0, in which industrial biotechnology adopts technologies
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similar to those of Industry 4.0, including industrial Internet of Things (IIoT), AI, automation, and digital twins (Isoko et al., 2024). (Figure 6.4)
Figure 6.4: Industrial Automation and Computational Biotechnology
6.4.1 Role of AI in Industrial Biotechnology
Industrial biotechnology involves complex biological processes in which small changes in temperature, pH, oxygen concentration, nutrient availability, agitation, or cell physiology can substantially influence productivity. Conventional optimization often depends on repeated experiments and expert knowledge. Machine learning provides an alternative by identifying relationships between process variables and biological outcomes from historical and experimental data. Applications include strain selection, metabolic pathway optimization, fermentation optimization, scale-up, process monitoring, and quality prediction (Lawson et al., 2021).
For example, ML models can analyze fermentation datasets containing variables such as dissolved oxygen, glucose concentration, biomass, temperature, pH, and metabolite levels. Once trained, predictive models can estimate product yield or identify process conditions likely to maximize productivity. This allows researchers to reduce the number of physical
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experiments required and focus experimental resources on the most promising conditions.
AI is also becoming important in metabolic and enzyme engineering. Computational models can predict how genetic modifications may influence metabolic pathways and cellular phenotypes. Recent developments include protein language models, generative AI approaches for enzyme design, and integrated models connecting protein function, metabolic pathways, and cellular states (Zhao et al., 2026).
6.4.2 Automation of Bioprocesses
Automation provides the physical infrastructure through which computational predictions can be translated into biological experiments and industrial operations. Automated bioreactors, liquid-handling systems, robotic sampling, online sensors, automated analytical instruments, and process-control systems can continuously collect and respond to process information.
A particularly important development is the emergence of closed-loop bioprocess automation. In such systems, sensors collect real-time information, AI models interpret the data, algorithms determine an appropriate action, and automated equipment implements the decision. The resulting biological response is measured again, creating a continuous feedback loop.
This approach can be extended from laboratory-scale experimentation to industrial fermentation and biopharmaceutical manufacturing. Instead of maintaining fixed process conditions throughout production, intelligent control systems can potentially adjust operating parameters according to the physiological state of the culture. Such adaptive control can improve consistency and reduce deviations from desired process conditions.
6.4.3 Digital Twins in Biomanufacturing
Digital twins represent another important component of computational biotechnology. A digital twin is a computational representation of a physical process or production system that is continuously informed by data from its real-world counterpart. In biopharmaceutical manufacturing, digital twins can be used to model process behavior, simulate operating conditions, predict deviations, and support process optimization. Their potential benefits include
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improved productivity, reduced costs, consistent product quality, and support for Quality-by-Design principles (Ding et al., 2024).
For example, a digital twin of a fermentation process can integrate historical process data, real-time sensor readings, mechanistic models, and ML predictions. Engineers can use the virtual model to evaluate the likely consequences of changing agitation speed, feeding rate, temperature, or oxygen availability before implementing the change in the physical bioreactor. Consequently, digital twins can function as decision-support systems and reduce the risks associated with trial-and-error optimization.
6.4.4 Computational Biotechnology and Self-Driving Laboratories
The combination of AI with robotics has led to the development of self-driving laboratories (SDLs). These systems integrate automated laboratory equipment with AI algorithms capable of selecting and prioritizing experiments. Rather than researchers manually designing every experimental iteration, the system can analyze previous results and select subsequent experiments according to a defined objective.
A 2025 study demonstrated an autonomous laboratory for biotechnology that combined robotic equipment with Bayesian optimization to perform a closed experimental loop involving cell cultivation, sample preparation, measurement, analysis, and hypothesis formulation. The system was successfully used to optimize culture conditions for recombinant Escherichia coli producing glutamic acid (Fushimi et al., 2025).
Similarly, self-driving laboratory platforms have been applied to protein engineering. The SAMPLE platform used an AI agent to learn relationships between protein sequences and functions, design new protein variants, and send them to an automated robotic system for experimental testing. Experimental results were then returned to the computational model, enabling further optimization (Rapp et al., 2024).
Such systems demonstrate the transition from a traditional design–build–test– learn workflow toward an automated design–build–test–learn–redesign cycle. This can substantially increase experimental throughput while allowing researchers to investigate complex biological design spaces more efficiently.
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6.4.5 Applications across Industrial Life Sciences
Industrial automation and computational biotechnology have applications across several sectors. In pharmaceutical biotechnology, AI-assisted process control can support the manufacture of monoclonal antibodies, vaccines, recombinant proteins, and other biologics. In industrial fermentation, computational models can optimize microbial growth and metabolite production. In enzyme biotechnology, AI can identify or redesign enzymes with desirable properties such as improved stability, catalytic activity, or temperature tolerance.
The technology is also relevant to biofuels, food biotechnology, agricultural biotechnology, biomaterials, and sustainable chemical production. ML-based metabolic engineering has already been investigated for pathway construction, genetic-editing optimization, cell-factory development, and production-scale optimization (Lawson et al., 2021).
6.4.6 Challenges and Future Directions
Despite its potential, intelligent automation in biotechnology faces important challenges. Biological systems generate highly variable and context-dependent data, while industrial datasets may be limited, noisy, heterogeneous, or poorly standardized.
ML models trained on insufficient or biased datasets may produce unreliable predictions. The complexity of biological processes also means that purely data-driven models may not always provide adequate mechanistic understanding.
Another challenge is integration and validation. AI models must interact reliably with sensors, robotic systems, laboratory information systems, and industrial control infrastructure. In regulated environments, traceability, data integrity, model validation, cybersecurity, and human oversight are particularly important. The increasing use of self-driving laboratories has therefore created a parallel need for safety frameworks and clearly defined human–machine responsibilities (Leong et al., 2025).
Future industrial biotechnology is likely to move toward hybrid intelligent systems that combine mechanistic models, machine learning, digital twins, robotics, and human expertise. Rather than completely replacing
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biotechnologists, these systems are expected to automate repetitive activities and support complex decision-making. The result could be more adaptive, efficient, reproducible, and sustainable biomanufacturing. As AI models become capable of integrating multi-omics, protein, metabolic, process, and environmental data, computational biotechnology may increasingly become a central component of industrial life-science innovation.
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