Imagine machines so small that they can interact with life’s tiniest building blocks, from cells down to individual atoms. These are micro- and nanosystems: technologies engineered with extraordinary precision at scales invisible to the naked eye. With innovative designs that tap into universal rules of how materials behave at the nanoscale, such systems include solar cells that mimic living leaves, as well as entire ‘laboratories on chips’ that detect early signs of disease.
Fascinated by their potential, Charmaine Chia, a Senior Scientist at the A*STAR Bioinformatics Institute (A*STAR BII) and A*STAR Bioprocessing Technology Institute (A*STAR BTI) , not only designs these cutting-edge systems but also helps make sense of the complex data they collect. Combining expertise in biology, hardware engineering and data analytics, Chia’s current work taps on artificial intelligence (AI) to uncover intricate patterns in biological data, enhancing systems from industrial enzymes to microfluidic devices.
In this interview with A*STAR Research, Chia delves into her multifaceted projects, shares her advice for braving cross-disciplinary STEM endeavours, and reflects on fostering a supportive culture for women in engineering.
Q: Tell us about your journey in science.
My dad, an electrical engineer of train systems, was my earliest guide. He took us on nature walks, encouraging us to connect with the world and wonder how things work. Like him, I wanted to apply science and math to solve real-world problems.
I became intrigued by how precisely- engineered nanostructures can control how electrons flow, enabling digital computing, signal transmission, medical devices and other revolutionary technologies. As a fresh engineering graduate at the A*STAR Institute for Materials Research and Engineering (IMRE), I had the chance to peek through advanced microscopes and watch atoms and electrons move in materials.
Inspired, I pursued a PhD degree on a nanoscale electrochemical device for detecting biological molecules. My time at Stanford University, US, coincided with the AI boom; I was able to take machine learning (ML) classes and apply them to my thesis. It was exciting to see first-hand how the convergence of atoms and bits unlocked new possibilities.
After graduating, I joined a startup for engineering enzymes—nature’s ‘biomachines’—to produce new biological and chemical products. The COVID-19 pandemic shifted my focus to data analytics, putting me at the nexus of multiple teams that were producing, housing, crunching and telling stories through big data.
As it seemed like a great time to be a biotech engineer, I joined A*STAR BTI upon returning to Singapore in 2022 to explore the biotech ecosystem. Now, with a joint appointment at A*STAR BII, I delve deeper into the messy world of biological data to engineer solutions for synthetic biology and bioprocessing.
Q: As a multidisciplinary engineer keen on biology and sustainability, what are some memorable projects to you?
At the enzyme engineering startup I worked at, we built a high-throughput screening platform from scratch: a complex endeavour requiring tight teamwork and a holistic view of requirements and tradeoffs. Scientists and automation engineers optimised workflows to construct and screen hundreds of thousands of mutant enzymes. Software and data engineers developed an in-house information management system to process all that data. ML engineers then used that data to identify patterns from which to propose better enzyme designs, and so on.
Over tight three-to-four-week cycles, and a memorable amount of camaraderie between teams, we improved the activity of an industrially- relevant enzyme a hundredfold.
Another project was one I joined at A*STAR BTI, where we developed a clog-free filtration system for continuous drug manufacturing. The central piece was a microfluidic device with curved channels that separated cultured cells from their surrounding liquid even with ultrahigh cell densities, allowing a continuous perfusion of fresh media into the culture to maintain optimal growth.
Translating this device into an industry-ready product required integrating it with pumps, controls and a user interface, as well as scaling it for higher flow rates. Much of my work these last two years has been on such a prototype for lab-scale perfusion culture; again, made possible only through collaborative efforts with experts in mechanical, electrical and software engineering design, mammalian cell culture and industry product requirements.
Q: What are you currently working on?
At present, I’m more focused on biological data delivered by devices like those I’ve worked on. As part of a cross-institute project, I’m developing more efficient algorithms to optimise enzyme performance using less screening data.
In biopharma, enzymes often drive multi-step reaction pathways to produce drugs. This can be more sustainable and higher-yielding than purely chemical methods. Advanced computational methods can speed up the development of enhanced enzymes to bring drugs to market sooner, as well as unlock novel chemistries from sparse data. With the rapid growth of AI in protein design, I’m excited to explore this from a bioinformatics angle.
Another focus is applying ML methods to bioprocessing datasets to answer problems such as: how do we optimise the nutrients fed to a mammalian cell culture to improve the quality of drugs it produces, and therefore make medicines better and cheaper? I’m especially interested in understanding how these models work—how they learn and what features they discover.
Q: What would help more women thrive in engineering and the physical sciences?
Both real and perceived barriers often contribute to underrepresentation. In developed countries like Singapore, the direct institutional barriers to women entering engineering are fairly low, but perceptual ones—thoughts like “I don’t think I can do it”, or “it’s too boring”—still persist.
I feel that it’s powerful to normalise engineering as something women do and enjoy. Thoughtful science communication and public outreach efforts—whether through publications, panel discussions, exhibitions or children’s workshops—play an important role in highlighting the diverse individuals behind science.
I’ve experienced the impact of mentorship firsthand in nurturing a ‘can-do’ culture among women exploring less-trodden paths. Even a simple coffee chat—, where encouraging words, ideas or connections are shared—, can be a powerful catalyst, inspiring someone to step out of their comfort zone, ask a question, start a new habit or take on a career challenge. Everyone has a role to play in fostering a supportive culture that can constructively challenge barriers and make the unfamiliar a little less scary.
Q: What strategies have helped you bridge different STEM fields?
Tackling any complex problem with diverse experts involves sub-teams for separate areas such as experimental design, data acquisition or analysis to meet project goals within time, budget and risk constraints. Each area surfaces different issues. I try to understand the various perspectives—their data, scientific principles, engineering considerations—by asking questions and consulting multiple sources to learn quickly as I go.
It’s also important to identify interdependencies in advance, such as how measurement trade-offs could impact data for model-building, or how schedules and team dynamics drive collaboration. Bridging these different factors and communicating them clearly is key to decision-making and progress.
I’m still figuring out how to effectively invest time to build technical expertise across multiple fields, but I think it’s always easier when one follows their curiosity and works on day-to-day tasks they find joy or meaning in. The answer varies for everyone, so it’s good to keep reflecting on how to grow where you are, or to seek a better fit. The deep tech industry offers many ways for different skillsets to contribute, but overall—especially for cross-disciplinary STEM projects—technical competence, good communication and a collaborative spirit always help.
