Biotechnology is the field of science that applies biological systems and processes to create products and services that benefit human health, agriculture, environment, and industry. Biotechnology has been advancing rapidly in recent years, thanks to the availability of large amounts of data, powerful computational tools, and innovative techniques. However, biotechnology also faces many challenges, such as the complexity of biological systems, the high cost and time of research and development, the ethical and regulatory issues, and the need for interdisciplinary collaboration.
Artificial intelligence (AI) is the branch of computer science that aims to create machines and systems that can perform tasks that normally require human intelligence, such as learning, reasoning, problem-solving, decision-making, and creativity. AI has been making remarkable progress in various domains, such as computer vision, natural language processing, speech recognition, robotics, and gaming. AI has also been applied to biotechnology, with the potential to revolutionize the field and overcome its limitations.
In this article, we will explore some of the applications and benefits of AI in biotechnology, as well as some of the challenges and risks that need to be addressed.
Applications and benefits of AI in biotechnology
AI can be used in biotechnology for various purposes, such as data analysis, drug discovery, disease diagnosis, personalized medicine, synthetic biology, and biomanufacturing. Here are some examples of how AI can enhance biotechnology in these areas:
Data Analysis.
Biotechnology generates massive amounts of data from various sources, such as genomics, proteomics, metabolomics, transcriptomics, microbiomics, and imaging. Analyzing and interpreting these data can be challenging and time-consuming for human experts. AI can help biotechnologists to process, integrate, visualize, and extract insights from these data, using techniques such as machine learning, deep learning, natural language processing, and computer vision. AI can also help biotechnologists to discover new patterns, relationships, and hypotheses from the data, and to validate and test them using simulations and experiments¹.
Drug discovery.
Drug discovery is the process of identifying and developing new compounds that can modulate the activity of a biological target, such as a protein, gene, or pathway, to treat or prevent a disease. Drug discovery is a complex, costly, and lengthy process, involving multiple steps, such as target identification, target validation, lead identification, lead optimization, preclinical testing, clinical trials, and regulatory approval. AI can help biotechnologists to accelerate and improve the drug discovery process, by using techniques such as machine learning, deep learning, natural language processing, and computer vision. AI can help biotechnologists to:
- Predict the structure and function of biological targets, using methods such as AlphaFold², which can accurately model the three-dimensional shape of proteins from their amino acid sequences.
- Generate and screen potential drug candidates, using methods such as Generative Adversarial Networks (GANs), which can create novel molecules with desired properties and activities.
- Optimize the drug candidates, using methods such as reinforcement learning, which can learn from feedback and trial-and-error to find the optimal solution.
- Evaluate the drug candidates, using methods such as deep learning, which can predict the pharmacokinetics, pharmacodynamics, toxicity, efficacy, and side effects of the drug candidates.
- Design and conduct the clinical trials, using methods such as natural language processing, which can analyze the medical records, literature, and social media to identify the best patient cohorts, endpoints, and outcomes for the trials.
Disease diagnosis.
Disease diagnosis is the process of identifying the cause and nature of a disease, based on the signs, symptoms, and tests of a patient. Disease diagnosis is a critical step for providing the appropriate treatment and care for the patient. However, disease diagnosis can be difficult and error-prone, especially for rare and complex diseases. AI can help biotechnologists to improve the accuracy and efficiency of disease diagnosis, by using techniques such as machine learning, deep learning, natural language processing, and computer vision. AI can help biotechnologists to:
- Analyze the biomarkers of the disease, such as genes, proteins, metabolites, and microbes, using methods such as machine learning, which can classify, cluster, and correlate the biomarkers with the disease.
- Interpret the images of the disease, such as X-rays, MRI, CT, PET, and ultrasound, using methods such as deep learning, which can detect, segment, and measure the anomalies and lesions in the images.
- Generate the diagnosis report, using methods such as natural language processing, which can summarize the findings, recommendations, and references from the analysis and interpretation of the data.
Personalized medicine.
Personalized medicine is the practice of tailoring the prevention, diagnosis, and treatment of a disease to the individual characteristics of a patient, such as their genes, environment, lifestyle, and preferences. Personalized medicine can improve the effectiveness and safety of the interventions, as well as the satisfaction and adherence of the patient. However, personalized medicine can be challenging and costly, due to the heterogeneity and variability of the patients and the diseases. AI can help biotechnologists to enable and enhance personalized medicine, by using techniques such as machine learning, deep learning, natural language processing, and computer vision. AI can help biotechnologists to:
- Stratify the patients, using methods such as machine learning, which can group the patients based on their similarities and differences in their features and outcomes.
- Predict the response and risk of the patients, using methods such as deep learning, which can forecast the probability and magnitude of the effects and adverse events of the interventions for each patient.
- Recommend the best intervention for the patients, using methods such as reinforcement learning, which can learn from the feedback and outcomes of the previous interventions to suggest the optimal action for each patient.
- Monitor and adjust the intervention for the patients, using methods such as natural language processing and computer vision, which can track and analyze the behavior and feedback of the patient, and modify the intervention accordingly.
Synthetic biology.
Synthetic biology is the engineering of biological systems and processes to create new functions and applications that are not found in nature. Synthetic biology can be used for various purposes, such as bioremediation, biosensing, biofuel, bioproduction, and biomedicine. However, synthetic biology can be complex and unpredictable, due to the nonlinear and emergent behavior of biological systems. AI can help biotechnologists to design and optimize synthetic biology, by using techniques such as machine learning, deep learning, natural language processing, and computer vision. AI can help biotechnologists to:
- Model and simulate the synthetic biological systems, using methods such as machine learning, which can learn from the data and rules of the biological systems, and generate and test the possible outcomes and scenarios of the synthetic systems.
- Generate and optimize the synthetic biological parts, using methods such as deep learning, which can create and modify the sequences and structures of the synthetic DNA, RNA, and proteins, to achieve the desired functions and properties.
- Assemble and integrate the synthetic biological parts, using methods such as natural language processing and computer vision, which can understand and follow the instructions and protocols for the construction and operation of the synthetic systems.
Biomanufacturing.
Biomanufacturing is the production of biological products and services, such as drugs, vaccines, diagnostics, enzymes, chemicals, and materials, using biological systems and processes, such as cells, tissues, organs, and bioreactors. Biomanufacturing can offer advantages over conventional manufacturing, such as higher specificity, diversity, and sustainability. However, biomanufacturing can also be challenging and costly, due to the variability and instability of biological systems and processes. AI can help biotechnologists to improve and scale up biomanufacturing, by using techniques such as machine learning, deep learning, natural language processing, and computer vision. AI can help biotechnologists to:
- Control and optimize the biomanufacturing processes, using methods such as machine learning, which can monitor, model, and regulate the parameters and variables of the biomanufacturing processes, such as temperature, pH, oxygen, nutrients, and growth factors, to maximize the yield and quality of the products and services.
- Detect and correct the biomanufacturing errors, using methods such as deep learning, which can identify and diagnose the faults and failures of the biomanufacturing processes, such as contamination, mutation, degradation, and aggregation, and suggest and implement the corrective actions and measures.
- Automate and standardize the biomanufacturing operations, using methods such as natural language processing and computer vision, which can execute and document the tasks and procedures of the biomanufacturing operations, such as sampling, testing, harvesting, and packaging.
Challenges and risks of AI in biotechnology
AI can also pose some challenges and risks for biotechnology, such as the quality, reliability, and explainability of the AI models and outputs, the ethical, legal, and social implications of the AI applications and outcomes, and the security, privacy, and ownership of the AI data and systems.
AI models and outputs can be affected by the quality, quantity, and diversity of the data and algorithms that are used to train and test them. AI models and outputs can also be influenced by the biases, errors, and uncertainties that are inherent or introduced in the data and algorithms. AI models and outputs can also be difficult to understand, interpret, and justify, especially for complex and nonlinear models, such as deep neural networks. These factors can affect the validity, accuracy, and robustness of the AI models and outputs, and the confidence and trust of the biotechnologists and the stakeholders in the AI models and outputs. Therefore, biotechnology need to ensure the quality, reliability, and explainability of the AI models and outputs, by using methods such as data cleaning, validation, augmentation, and annotation, algorithm selection, optimization, and evaluation, and model interpretation, visualization, and verification.
AI applications and outcomes can have significant impacts on the ethical, legal, and social aspects of biotechnology, such as the safety, efficacy, and accessibility of the products and services, the rights, responsibilities, and accountability of the developers and users, and the values, norms, and expectations of the society. AI applications and outcomes can also raise new and complex ethical, legal, and social issues, such as the moral status, agency, and autonomy of the AI systems, the consent, privacy, and ownership of the data and algorithms, and the fairness, equity, and diversity of the AI models and outputs. Therefore, biotechnology need to consider and address the ethical, legal, and social implications of the AI applications and outcomes, by using methods such as ethical principles, guidelines, and codes, legal frameworks, regulations, and standards, and social engagement, education, and awareness.
AI data and systems can be vulnerable to various threats and attacks, such as hacking, tampering, stealing, and sabotaging, which can compromise the security, privacy, and ownership of the data and systems, and cause harm and damage to the biotechnology and the society. AI data and systems can also be misused or abused, such as for malicious, fraudulent, or illegal purposes, which can violate the security, privacy, and ownership of the data and systems, and cause harm and damage to the biotechnology and the society. Therefore, biotechnology need to protect and secure the AI data and systems, by using methods such as encryption, authentication, authorization, and verification, and to prevent and deter the misuse and abuse of the AI data and systems, by using methods such as monitoring, auditing, reporting, and sanctioning.
In sum…
AI is transforming biotechnology, by enabling and enhancing various applications and benefits, such as data analysis, drug discovery, disease diagnosis, personalized medicine, synthetic biology, and biomanufacturing. However, AI also poses some challenges and risks for biotechnology, such as the quality, reliability, and explainability of the AI models and outputs, the ethical, legal, and social implications of the AI applications and outcomes, and the security, privacy, and ownership of the AI data and systems. Therefore, biotechnology need to balance and integrate the AI opportunities and challenges, by using methods such as data and algorithm quality, reliability, and explainability, ethical, legal, and social principles, frameworks, and engagement, and security, privacy, and ownership protection and prevention.
References.
- The future of biotech: AI-driven drug discovery | McKinsey & Company. https://www.mckinsey.com/featured-insights/the-next-normal/biotech.
- The Future of Artificial Intelligence and Biotechnology. https://biotechnology.georgetown.edu/general-page/the-future-of-artificial-intelligence-and-biotechnology/.
- AI In Biotechnology – Exploring Role And Applications – Qualetics. https://qualetics.com/role-applications-of-ai-in-biotechnology/.