Young PIs in action: an interview with Pablo Carbonell
Our next interviewee for our ‘Young PIs in action’ series is Dr Pablo Carbonell, Senior Reader in Computational Synthetic Biology at Universitat Politècnica de València, Spain.
With a PhD in machine learning-based control of complex systems, Dr. Pablo Carbonell has been a research fellow in Spain, Japan, France, and the UK, as well as a visiting scientist in the USA, Germany and Switzerland. His last position was as senior scientist at the SYNBIOCHEM center at University of Manchester, working on computational metabolic pathway design and automation in metabolic engineering. Since spring 2020, he is a senior reader in computational biology at Universitat Politècnica de València (UPV), and leads his own research group.
Could you please tell us briefly about your field of study and your path?
My field of study is automated design for metabolic engineering and synthetic biology. My career path is somewhat atypical, as I earned my PhD in machine learning-based control of complex systems and eventually became so much interested in engineering biology that I decided to focus my research on that field. During my career, I had the opportunity to work as a research associate at both a biotech company in Tokyo as well as at several academic labs. Among them I worked at New York University Polytechnic School of Engineering (NYU-Poly), and at the Institute of Systems and Synthetic Biology (iSSB) with Prof. Jean-Loup Faulon (currently at INRA). More recently, I was at the Synbiochem Centre in Manchester working as a staff scientist on metabolic pathway design.
Can you explain a bit more about your work and challenges you had?
My work is focused on automating the steps that are typically involved in metabolic engineering in order to help its transition into a truly manufacturing technology. When I arrived at the field from a control engineering background, I was really surprised to see how much of the process was still done based on trial-and-error, especially at the early stages of the project. Luckily, the situation at that moment was starting to become more and more challenged by the vision of synthetic biology. I clearly saw an opportunity to bridge that gap by bringing my own perspective on automation and machine learning. My work has contributed towards such integration by formalizing the problem of linking pathway discovery to genetic circuit selection in the biochemical and design spaces.
Where is synbio regarding computational biology tools and approaches and what is needed in the coming years?
I think that it is useful to see synbio projects through the lens of the Design-Build-Test-Learn engineering cycle. Our approach is to develop computational tools or apps that serve to address specific challenges related to synbio. For instance, we have spent a good deal of time developing robust tools to select production pathways through retrosynthesis and select the right combination of genetic parts to engineer in the host chassis. Such design involves multiple decisions and we are providing apps for each task. I expect in the coming years more inter-lab collaboration, such as the Global Biofundry Alliance, in order to standardize and share the computational tools and resources, and an increasing focus on lab automation and automated learning.
Please tell us more about UPV Valencia, where you got your current position.
The Universitat Politècnica de València (UPV Valencia) is a technological university with a strong focus on research and engineering in tight collaboration with the industry. Research is organized into institutes such as the ai2 Institute focused on automation and artificial intelligence, where our lab is currently affiliated. Biotechnology is one of the major strategic research axes for the institution. Notably, the UPV Valencia iGEM team won several awards in its most recent participation, including the Grand Prize winner award with their project Printeria.
What are your future plans and what is your feeling about that?
My plan is to work on the integration of artificial intelligence and control engineering as an essential part of the Design-Build-Test-Learn cycle of synthetic biology. I am mainly interested in biomanufacturing systems as automated platforms for rapid prototyping and assembly of microbial producers for materials, drugs, food, cosmetics and others, providing solutions to our current global environmental, socioeconomic and health challenges. I believe that a technology-oriented academic institution such as UPV is the right environment where a synbio team can deliver innovative solutions for both local and global challenges.
What is your advice for young generations in synbio?
My main advice is to avoid preconceived notions about the field, like those that consider synbio as plainly molecular biology or those that see it just as an engineering technology. Successful stories in synbio have been always rooted on interdisciplinary approaches and the best that you can do as an aspiring researcher is to set up your own synbio research agenda. However, I have also seen too often young researchers trying to focus their research on a subject way too far from their area of expertise just to cope with their supervisors’ interests. As challenging as it might seem within the academic environment, my advice is to always keep your research in tune with your own interests and expertise. Add your own label to synbio; the advantage of working on this relatively young area of research is that it is largely open to novel views and approaches.