Multiscale modeling: A mathematical-based tool for bioprocess design, control and scale up/down
This paper presents a multiscale mathematical modeling tool that couples macro-scale compartment-based bioreactor hydrodynamics with micro-scale kinetic metabolic models to simulate the dynamic two-way interactions between environmental gradients and cellular responses, thereby improving bioprocess design, control, and scale-up.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
Inside a massive industrial tank, a single-celled organism is trying to do its job. It is a tiny factory, consuming sugar and turning it into something useful, but it does not live in a perfect, uniform world. In the real world of a large bioreactor, the environment is messy. The liquid does not mix instantly; pockets of high sugar concentration sit next to pockets of low sugar. The oxygen levels fluctuate as the liquid swirls. For decades, scientists have tried to predict how these microbes behave by assuming the tank is a perfectly mixed soup, where every cell sees the exact same conditions. But this assumption often fails. When a microbe is suddenly flooded with sugar in one corner of a giant tank, it reacts differently than it would in a small, perfectly stirred lab beaker. Understanding this gap between the small lab and the giant factory is the key to making bioprocesses more efficient, cheaper, and more reliable.
This is the challenge tackled by a team of researchers from the Universidad Nacional de Colombia. They set out to build a mathematical tool that could see inside the invisible world of a large bioreactor, connecting the movement of the liquid to the internal chemistry of the cells. Instead of treating the tank as a single, uniform blob, they broke it down into many smaller sections, like a grid of tiny rooms, to track how sugar and oxygen move through the liquid. They then linked this map of the tank to a detailed model of the cell itself. This allowed them to simulate what happens when a pulse of sugar is dumped into the system, watching how the liquid carries that sugar to different parts of the tank and how the cells in those specific spots react in real time.
The researchers focused on a 22 cubic meter industrial bioreactor, a vessel large enough to hold a small swimming pool. They simulated a scenario where a concentrated stream of glucose, a type of sugar, was injected into the tank. In a small lab reactor, this sugar would spread out almost instantly, and every cell would get a tiny, uniform dose. In their simulation of the large tank, the story was very different. Because the liquid takes time to mix—sometimes up to nearly three minutes to become uniform—the sugar created a temporary wave. Cells near the injection point were suddenly swimming in a sea of sugar, while cells at the far end of the tank saw almost nothing. The time it took for the sugar to disperse was faster than the time it took for the cells to eat it, leading to a chaotic environment where some cells gorged themselves while others starved.
The team discovered that where you put the sugar matters immensely. When they simulated adding the sugar at the top of the tank, a common practice in industry, the mixing took the longest. The cells at the top experienced a massive spike in sugar, with their internal sugar levels rising to more than one hundred times their normal amount in a matter of seconds. Meanwhile, cells at the bottom of the tank saw a much smaller change. This created a situation where the entire culture was not acting as one unit, but as a collection of different groups, each with its own internal chemistry. The cells at the top were forced to adjust their internal machinery to handle a sudden flood, while the cells at the bottom continued on as if nothing had happened. This unevenness, the researchers found, could lead to a culture that is less efficient and produces less of the desired product.
To test if they could fix this, the team ran simulations with different settings. They found that simply spinning the mixing blades faster helped. When they increased the speed of the agitation, the sugar spread out more quickly, smoothing out the dangerous spikes and dips. However, this came with a cost. To move the mixing speed from a slow 60 rotations per minute to a fast 240 rotations per minute, the energy required to power the machine would have to jump by a factor of sixty-four. This is a massive increase in cost, suggesting that while faster mixing solves the biological problem, it might not be the most economical solution for a factory.
The study also looked at how the cells responded internally. The researchers modeled the complex network of reactions inside the yeast cells, tracking how they stored and used the sugar. They found that the cells near the sugar injection point built up huge reserves of internal sugar, far more than they could use immediately. This internal buildup was a direct result of the external environment being too rich for the cells to handle quickly. In contrast, cells in areas where the sugar had already mixed were able to maintain a steady, calm pace. The simulation showed that these different environments could push the cells into different metabolic states, effectively creating different "personalities" within the same tank.
Ultimately, the work demonstrates that the old way of thinking about bioreactors as perfectly mixed containers is insufficient for large-scale operations. The researchers showed that the time it takes for the liquid to mix and the time it takes for the cells to react are two competing forces. When the liquid moves slowly, it creates gradients that force cells to behave differently depending on their location. By using their new tool to map these interactions, engineers can now see exactly where these problems occur. They can test different designs, such as moving the sugar injection point to the middle of the tank, which the simulations showed would create much gentler conditions for the cells. This approach offers a way to design better bioreactors and control them more precisely, ensuring that every cell in the tank, from top to bottom, is working in harmony rather than struggling against a chaotic environment.
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