ClassX Video Lessons Subjects Artificial Intelligence Inside the Lab Where Robots Run Their Own Experiments
Imagine a laboratory where robots are the scientists, tirelessly conducting thousands of experiments to discover life-saving drugs and create synthetic organisms. This isn’t science fiction—it’s the cutting-edge reality of modern science. The push towards automation in laboratories is transforming how we develop new therapies and revolutionizing scientific discovery.
Traditionally, life sciences have relied heavily on manual labor. If you walk into a typical lab, you’ll see researchers performing repetitive and time-consuming tasks. There’s even a joke that PhD students are essentially free labor for professors. However, this is changing with the advent of robotic cloud labs. These labs allow scientists to design experiments online and have them executed by robots remotely. This innovation frees researchers to focus on creative tasks like generating hypotheses and interpreting data, rather than getting bogged down in manual work.
One of the significant advantages of using robots in labs is the precision and consistency they bring to experiments. Human-executed protocols often include ambiguous instructions like “incubate overnight” or “shake until cloudy,” which can lead to variability in results. In contrast, robotic labs define every step of an experiment with code, ensuring that experiments can be replicated exactly. This consistency not only saves time but also enhances the reliability of scientific findings.
To start an experiment in a robotic lab, you simply log onto a website, select the scientific processes you need, input your parameters, and click “Launch.” The system checks for any errors, such as excessive liquid amounts or hazardous materials, before dispatching the task to the robots. Inside the lab, robotic arms move samples and conduct experiments, notifying users via email once results are ready. Under optimal conditions, a single robotic work cell can execute up to 190,000 experiments in a day, with multiple work cells operating simultaneously.
The process of developing new drugs is notoriously challenging, often taking years and costing billions. Robotic labs can streamline this process by allowing researchers to quickly test numerous compounds against a target. Collaborations with companies like Eli Lilly have expanded these capabilities to include synthetic chemistry, enabling users to design, synthesize, and test molecules entirely via the cloud. This democratizes access to advanced equipment, making it available to startups and academic researchers.
The COVID-19 pandemic highlighted the need for remote scientific work. With many labs closed, researchers turned to robotic labs to continue their experiments. This shift underscores the importance of being able to conduct science without physical lab access, paving the way for more efficient and accessible discovery.
A key concept in this field is closed-loop experimentation, which involves executing experiments, building models from the data, and deciding on the next experiments to improve those models. This approach integrates robotics, machine learning, and artificial intelligence, potentially transforming how we discover drugs. By creating predictive models for drug interactions, we could gain a deeper understanding of complex biological systems.
Despite the promise of automated science, challenges remain. Implementing these technologies across different experimental spaces, such as various cells and tissues, requires significant effort. Additionally, there’s a need to encourage scientists to adopt these new approaches. Educational programs, like a Master’s in automated science, are being developed to train the next generation of scientists in these methods.
Automation doesn’t eliminate the need for human scientists; it changes their roles. With robots handling the grunt work, researchers can focus on designing ambitious experiments and exploring new hypotheses. This shift allows scientists to scale their work and achieve more significant breakthroughs, ultimately advancing our understanding of the world.
Engage in a virtual lab simulation where you can design and execute experiments using a robotic cloud lab interface. This activity will help you understand the process of setting up experiments remotely and the precision involved in automated science.
Analyze a case study on a successful drug discovery project that utilized robotic labs. Discuss the advantages and challenges faced during the process. This will enhance your understanding of how automation impacts drug development.
Participate in a group debate on the pros and cons of automation in scientific research. This activity will encourage you to critically evaluate the role of robots in labs and the evolving role of human scientists.
Attend a workshop that introduces you to closed-loop experimentation. Learn how to integrate robotics, machine learning, and AI to create predictive models for scientific research. This will provide insights into the future of automated science.
Develop a research proposal that incorporates the use of robotic labs for a specific scientific question. Present your proposal to peers for feedback. This activity will help you apply the concepts of automated science to real-world research scenarios.
This laboratory is run by robots. These silicon scientists are executing thousands of experiments, searching for life-saving drugs and building synthetic organisms—all with virtually no human intervention. It’s part of an industry-wide push to move away from time-intensive manual benchwork and towards automation. This has the potential to transform how we develop new therapies and could fundamentally reimagine scientific discovery.
The life sciences are really underserved by automation and technology in general. If you go into a lab, you’ll see humans doing a lot of labor-intensive work. There’s a common joke that PhD students are free labor for professors. When I was doing my PhD, I first started using Strateos’ robotic cloud lab. The concept was that you could log into a web application, design an experiment with code, and then have it executed for you by robots remotely via the internet. I got really excited and signed up, and then I actually started running experiments. I remember sitting on the couch in my apartment, watching an experiment execute while I relaxed, and I thought, “This is the future of life science.”
This is really about helping humans focus more on the creative aspects of hypothesis generation and scientific interpretation, rather than on the manual tasks. Offloading experimental work onto robots has the potential to save enormous amounts of time and could also lead to more reliable results. Often, when you look at a protocol executed by a human, there are ambiguous steps, like “incubate overnight,” which is not a set period of time, or “shake until the solution is cloudy.” There’s no real definition of “cloudy” or how much you should shake that sample. Every experiment that Strateos has executed is actually defined by code. So, when I want my colleagues to replicate an experiment I’ve performed, I can just give them access to that code, and they can click “Go,” and it runs exactly the same way.
The first step in getting robots to do your scientific bidding? Log on to a website. You can see a whole menu of different scientific processes to choose from. After you’ve put in all your parameters and chosen your samples, you click “Launch,” and our system automatically checks that you’re not trying to pipette an excessive amount of liquid or use something dangerous. If everything is good, our system dispatches the work to the robots.
We’re inside one of our work cells here. This is the robotic arm; you can see it moving inventory on a plate. There are experiments all on this plate, and once that comes out, it will go to an analytical device. Meanwhile, the robot will go off and conduct other experiments for different users. Once it’s done, the user gets a notification via email and can fetch their results. Under optimal conditions, a single work cell could execute 190,000 experiments in a day, and Strateos currently has 23 work cells in operation.
We believe this will increasingly resemble the scale that cloud computing has reached. You could picture a large facility packed full of robotics, inventory, and storage equipment for samples, with thousands of scientists using that equipment and infrastructure simultaneously and remotely via the internet. Faster, easier, and more reliable experimental results would be a game changer across industries, particularly in drug discovery.
The process of developing drugs has become extremely difficult. We start by identifying a target for drug development. We design an assay to determine whether the activity of that target has been inhibited, and then screen that over many possible compounds. It can take years and cost billions of dollars to develop a single drug, and often, it could fail before reaching the market. Using a cloud lab could help drug developers streamline that process.
We’re excited to have worked with Eli Lilly to add synthetic chemistry to the platform. This means that entirely via the cloud, users will be able to design molecules, have them made and purified, and then run through biological assays to get data from their ideas. This platform offers state-of-the-art equipment that has typically only been accessible to large companies, making it easier for startups or academics to access.
COVID-19 has been an interesting time for Strateos. Many people reached out to us saying, “My lab is suddenly closed; I need to keep this work going.” People have seen the need to work remotely. Science should be able to continue without physical access to a lab. Automating the execution of experiments is a huge step toward more efficient and accessible scientific discovery, but some want to go even further to develop robots that can design their own experiments.
A key concept in automated science is closed-loop experimentation. This starts with executing a set of experiments, then building a model from that data, and finally deciding what experiments to conduct next to optimally improve that model. This loop relies on the integration of robotics, machine learning, and artificial intelligence. Getting it right could completely change how we find life-saving drugs.
You can think of this like playing a game. We need to explore the board and build a model as we do that to make informed choices. Automated science aims to create a predictive model for the experimental space of drugs and targets. In the future, this method could be expanded to build predictive models for complex interactions within our bodies, giving us a clearer understanding of how they work and what to do when they don’t.
However, there are still challenges ahead. One technical challenge is how to implement this for various experimental spaces, including different cells, tissues, and organisms. This will require significant effort. There is also a bottleneck in the adoption of automated science approaches by scientists. I thought a good starting point would be to build a Master’s program in automated science. The first class just completed their first year. These students will be some of the most productive scientists because they’ll be able to scale their experiments through code and automation, as well as the data analysis.
Many people ask, “What’s the role for humans if you’ve eliminated them from the loop?” The answer is that automation doesn’t replace the need for people; it changes the jobs that people do. Now, a PhD student can be their own principal investigator, using robots to conduct experiments. This allows them to have grander aspirations for the hypotheses they want to evaluate and the scale of experimentation they want to accomplish.
Robots – Machines capable of carrying out complex actions automatically, often used in biological research for tasks such as sample handling and data collection. – In the laboratory, robots have significantly increased the efficiency of high-throughput screening processes.
Experiments – Controlled procedures carried out to discover, test, or demonstrate a hypothesis in scientific research. – The experiments conducted with neural networks provided new insights into protein folding mechanisms.
Automation – The use of technology to perform tasks with minimal human intervention, often applied in biological data analysis and laboratory processes. – Automation in sequencing technologies has drastically reduced the time required to decode the human genome.
Drug – A chemical substance used in the treatment, cure, prevention, or diagnosis of disease, often discovered through biological and AI-driven research. – The development of a new drug for Alzheimer’s disease was accelerated by AI algorithms that predicted molecular interactions.
Discovery – The process of finding or learning something for the first time, often referring to new insights or innovations in biology and AI. – The discovery of CRISPR-Cas9 revolutionized genetic engineering and opened new avenues for AI applications in gene editing.
Biology – The scientific study of life and living organisms, encompassing various fields such as genetics, ecology, and molecular biology. – Advances in computational biology have enabled researchers to simulate complex biological systems using AI models.
Artificial – Created by humans rather than occurring naturally, often referring to systems or processes designed to mimic natural phenomena. – Artificial neural networks have been inspired by the structure and function of the human brain to solve complex problems.
Intelligence – The ability to acquire and apply knowledge and skills, often enhanced by artificial systems to perform tasks that require human-like understanding. – Artificial intelligence has transformed the field of genomics by providing tools for analyzing vast amounts of genetic data.
Synthetic – Relating to substances or materials made by chemical synthesis, often used in biology to refer to artificially created compounds or organisms. – Synthetic biology combines engineering principles with biological research to design and construct new biological parts and systems.
Research – The systematic investigation into and study of materials and sources to establish facts and reach new conclusions, often involving interdisciplinary approaches in biology and AI. – Research in bioinformatics utilizes AI to interpret complex biological data, leading to breakthroughs in personalized medicine.
