A community approach to study metabolism in computational biology

We present a community approach to model reconstruction, open software development and scientific communication applied to the study of enzymes and their limitations across a wide variety of organisms, spanning industrial yeasts, bacteria and human metabolism.
A community approach to study metabolism in computational biology
Robustness and interconnectivity are key aspects of metabolic networks that can inspire our social relations at work and life. Image taken from Metabolic Atlas: https://metabolicatlas.org/

When I started my PhD, some years ago, I was initially assigned to an engineering-scientific project, joining different universities, research institutes and companies of different sizes. The goal was to develop microbial cell factories to produce interesting industrial chemicals in a sustainable and scalable way. My role was to use computational metabolic models to predict the best genetic engineering strategies to boost production, using diverse yeast species as host cells.

A fellow PhD with more experience in the lab had recently developed a computational method, with the purpose of studying enzyme and protein data in metabolic networks at a genome-scale. To me it sounded like rocket science, after coming from a master thesis project about a single reaction in a hypothetical reactor. I started my project by adapting the software of my colleague, I needed to make it able to deal with multiple biological organisms.

In our lab I was introduced to the practice of collaborative software development based on version-control systems. It made all my coding efforts more organized, focused and somehow useful to others. My first collaboration came from contributing to another software project, RAVEN 2.0, as a developer of auxiliary functions, nonetheless, it gave me confidence to keep on advancing my software skills, together with my background in biology and general metabolism. My insecurities came from my chemical and energy engineering degrees, with zero biology in their curricula. With time and an increasing amount of long nights coding, testing data structures, adjusting parameters in mathematical models and high coffee consumption (even for a Swedish context), my code started to deliver reproducible results. With this I was starting to contribute with predictions for my colleagues in the wet lab (bench) and solutions for peers in the dry lab (computational). I gained confidence and dared to expand my scope, explore statistics, data analysis, programming languages, genotypes and phenotypes.

A great satisfaction came when I was able to collaborate in the human metabolic atlas project, predicting tumor growth for multiple cancer cell-line types, using metabolic models and enzyme data. But, as uncle Ben said, "with great power comes great responsibility", my recently earned confidence translated into multiple spin-off projects and piled-up deadlines. Then COVID changed everything.

Labs all around the world closed their doors, scientists were pushed to sit and analyze their data or write their longly postponed manuscripts. Even though, we scientists are a privileged group in comparison to wide swaths of society, I managed to worsen in my mental state as my social interactions dropped. Being away from the family, mourning relatives and friends at the distance paid a heavy toll on me and I developed clear symptoms of depression. I tried to keep on working at irregular times and long sessions, but it became harder and harder as time passed.

We were developing a software, GECKO 2.0, already used by multiple users in Europe and the rest of the world, ambitions grew and, together with enthusiast colleagues, we expanded the scope of our tools, aiming to further accessibility and automation. It was time to wrap up years of multiple studies on enzymes and metabolic networks  and successful experiences of collaborative software development, even at the distance. The publication needed to be written. After a long postponement period, I made it, wrote a manuscript that I was satisfied with and sent it to my collaborators, these included people working in both wet and dry lab, also both in the making and supervising roles. Rounds of rich discussion, revision and, an always respectful, exchange of ideas gave us a manuscript that made us agree. We submitted to nature communications. 

It was also in this period that life hit at my door, multiple events made me collapse mentally and I was sent home for a sick-leave due to my depression and diagnosed burn-out syndrome. This was supposed to be a period of six weeks of progressive recovery,  but the snowball kept on rolling downhill, my dad being close to a COVID death, getting my right leg broken in a football match and my grandma passing away in Mexico made it impossible for me to reconnect with work. After some time I decided to accept my circumstances and took on a healing trip to my hometown. Mourning together with my family and allowing myself to be part of a caring community, were the missing elements in my life.

I went back to Sweden, feeling ashamed after such a long absence at work, I assumed that everybody were furious at me, but I was also convinced that I wanted to make it up. To my surprise, collaborators were happy to have me back to the team. I realized that my supervisor made sure that the the editor gave us time for our reviewed version of  the paper, my co-supervisor and colleagues took over the remaining tasks and a fellow PhD student stepped in on responding to reviewers' comments. I realized then that we had a real team, a community that went beyond the standard practices of collaborative software development and applied similar principles to their science and their relations. My shame started to fade out and I was able to reconnect and conduct the final steps of the revision process. Our paper got accepted, but the real reward was to feel reassured as an active and valuable member of a humane research community. 

With our publication in Nature Communications, and its associated web resources, we want to bring genome-scale metabolic models closer to biologists, metabolic engineers but even the broad public. In our lab we have been able to use the concept of enzyme-constrained metabolic modeling to understand biological processes such as long-term adaptation to industrial stress conditions, metabolic effects of caloric restriction, and even evolution of kinetic capabilities in yeast cells, but also shed some light on the potential use of these models for health and cancer research. We wish our resources to be leveraged and expanded by other groups of researchers, knowledge is a common good.

From a management perspective, construction of a robust project team, with different areas of expertise and with members at different stages in their career, together with combining project-based personnel (PhD candidates and PostDocs) with long-term members (research engineers, young researchers and professors) proved to be key points for success. Project-based members tend to work for intense sprint periods because of their clear need of publishable results, whilst long-term personnel, such as computational research engineers, can offer supervision and a sustainable planning of scientific software development that transcends PhD projects. The decision of maintaining computational scientific resources as open source under continuous development, may bring enormous benefits when external user communities are engaged and feel empowered enough to contribute, however, reasonable and realistic planning of human resource allocation for software and community maintenance is needed.

My experiences during my PhD have taught me that scientific practice is a long distance relay race, no matter how trapped we may feel in the exhausting 100 meters individual sprints, long-term objectives, collective planning and execution of projects and construction of robust trustworthy teams bring us closer to our goals. In particular, the journey of this publication taught me more than metabolism and enzyme capacity, it showed me an open face of the scientific enterprise, where us researchers are human beings that can acknowledge our capabilities, but also our needs and limitations, and conscious communication of these can lead to successful results. Personally, I can add that talking to my collaborators, colleagues and supervisors about my mental health and personal issues, has helped me to increase my self-confidence and feeling of belonging to a knowledge community.

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