When I first started university, I wanted to understand how cells in the human body actually work — how medications affect metabolism, why cells sometimes fail, and what leads to disease. I spent hours in the lab, pipetting, extracting DNA, and analyzing proteins. And then I realized two things:
- I am just too clumsy for lab work.
- Wet lab experiments take forever and require tons of patience, reagents, and space.
Luckily, technology came to the rescue. Nowadays, you can simulate biological experiments — at a reduced complexity, but still surprisingly powerful. That’s when I shifted my focus toward bioinformatics and software engineering, combining my love for biology with coding.
Once I started writing code, I couldn’t help but notice the striking parallels between a cell and a software system. Take metabolism — the network of chemical reactions that keeps a cell alive. Think of enzymes as modular functions: each one performs a specific, deterministic task. Some enzymes rely on the outputs of others, creating dependency chains, much like method calls in software. You can think of enzymes as processing nodes in a computational network.
Zooming out, the whole metabolic network can be represented as a directed graph:
- Nodes = enzymes or reactions
- Edges = metabolites flowing from one reaction to another
With enough data, we can build genome-scale metabolic models (GEMs) (O’Brien et al., 2015; Brunk et al., 2018). These are massive computational representations of an organism’s metabolism, incorporating thousands of reactions and metabolites. Using GEMs, we can simulate how a cell consumes nutrients, produces energy, or responds to drugs — essentially running virtual experiments without touching a pipette (Heirendt et al., 2019).
From a software engineering perspective, this is pure gold:
- Modular Architecture: Each enzyme or pathway can be treated as a component you can swap, extend, or test independently (GEMSiRV, 2012).
- Constraint Solving: The simulation determines which reactions can occur given resource limitations, similar to solving deterministic, rule-based problems in software (Ebrahim et al., 2013).
- Parallel Execution: Thousands of reactions and pathways can be computed simultaneously, so performance optimization and scalable computation matter.
- Reproducibility and Testing: Just like in software, you want results to be consistent, version-controlled, and maintainable (Machado et al., 2018).
What can we actually do with GEMs? A few examples:
- Predict how genetic mutations or drugs affect metabolism.
- Explore alternative growth strategies for microbes in biotechnology.
- Understand how different species in a microbial community interact.
It’s like building a simulation engine for life itself. Imagine a physics engine calculating the interactions of millions of objects — now replace the objects with metabolites and the forces with enzymes. The engine computes how the system evolves over time. The big difference? Nature’s engine has evolved over billions of years, while we get to design and optimize ours in code.
Working with these models made me realize something important: biology is full of rules and constraints, but it’s software that lets us explore and experiment at scale. By applying principles like modularity, clean architecture, and parallelization, software engineers can make simulations faster, more flexible, and reproducible. That’s how we can predict outcomes, design experiments, or even suggest new drug strategies — all without stepping into a lab.
In short, a cell’s metabolism is nature’s own software simulation, and genome-scale metabolic models let us peek inside, manipulate, and understand it. For software engineers, that means there’s a whole world of high-performance, modular, and scalable systems to explore — and it’s as fascinating as any tech problem out there.
References
- Brunk, E., et al. Current status and applications of genome-scale metabolic models. Genome Biology, 2019.
- O’Brien, E. J., Monk, J. M., Palsson, B. O. Applications of genome-scale metabolic models in biotechnology and systems medicine. Frontiers in Systems Biology, 2015.
- Heirendt, L., et al. Creation and analysis of genome-scale metabolic models. Nature Protocols, 2019.
- GEMSiRV: Software platform for GEM simulation and visualization. Bioinformatics, 2012.
- Ebrahim, A., et al. COBRApy: Constraints-based reconstruction and analysis for Python. BMC Systems Biology, 2013.
- Machado, D., et al. Addressing uncertainty in genome-scale metabolic model reconstruction. Genome Biology, 2021.
