A new JSTAT study shows how to compute the minimum energy cells use to sustain certain metabolic pathways while suppressing others, revealing a “cost” invisible to mechanical physics.
There are “costs of life” that mechanical physics cannot capture. One example is the energy needed to keep particular biochemical processes running, including those involved in photosynthesis, while also preventing other possible reactions from taking over. In basic mechanics, if nothing moves then no work is done, so stopping something from happening seems as if it should require no energy. But detailed calculations in stochastic thermodynamics show that these hidden costs are real, and they can be large.
A new paper in the Journal of Statistical Mechanics: Theory and Experiment (JSTAT) outlines a thermodynamic approach for estimating these costs. The authors present it as a new way to study how metabolic pathways are chosen and how they evolve at the foundation of life.
Life Begins With Boundaries and Choices
In an ancient ocean, some organic molecules formed an outer boundary that became the first cell membrane. For the first time, there was a clear separation between an inside and an outside. From then on, this early system had to spend energy to preserve that separation and to narrow down the many reactions that could occur into only a small number of metabolic pathways.
Those pathways could take useful substances from the “outside” and convert them into new products. Life emerged alongside this continuing work of maintaining compartments and making chemical choices.
Metabolic reactions have an obvious energy demand, but they also come with an “extra cost.” This added burden reflects the effort required to keep chemical activity flowing along a favored route instead of spreading into every physically possible alternative.
Classical mechanics, however, treats compartmentalization and reaction selection as “constraints” set at a system’s boundaries, and in that view they should cost nothing because they are handled as fixed external conditions that do not add to entropy production.
A New Way to Rank Metabolic Pathways
Praful Gagrani, a researcher at the University of Tokyo and the study’s first author, worked with Nino Lauber (University of Vienna), Eric Smith (Georgia Institute of Technology and Earth-Life Science Institute), and Christoph Flamm (University of Vienna) to develop a way to calculate these overlooked costs and use them to rank pathways. The method gives researchers a way to compare biological efficiency, which can help evolutionary studies that investigate how life emerged on our planet.
“What inspired the new work is that Eric Smith, one of the co-authors, used MØD, a software developed by Flamm and co-workers, to enumerate all the possible pathways that can ‘build’ organic molecules starting from CO₂.”
Gagrani refers to one of Smith et al.’s earlier studies on the Calvin cycle, a cycle of chemical reactions in photosynthesis that converts carbon dioxide into glucose.
“Eric used the algorithm to enumerate all the pathways that can make the same conversion that the Calvin cycle does, and then he used what we now call the maintenance cost in our paper to rank them.”
In this way, Smith et al. showed that the cycle used by nature lies among the least dissipative pathways — those with the lowest energetic cost. “Awesome, isn’t it?” comments Gagrani.
Measuring Improbability Instead of Energy
Inspired by Smith’s work, Gagrani and colleagues devised a general method to estimate the thermodynamic costs of metabolic processes systematically. In their framework, the cell is imagined as a system crossed by a constant flow, where, for instance, one molecule (a nutrient) enters and another (a product or waste) exits.
Given the underlying chemistry, one can generate all chemically possible pathways that convert the input into the output. Each pathway has its own “thermodynamic cost.” Instead of calculating energy in the classical sense, the method estimates how improbable it would be — in a world driven solely by spontaneous chemistry — to see the network (the set of molecules and reactions that convert input to output) behave in exactly that way.
This improbability has two components. The first is the maintenance cost, meaning how unlikely it is to sustain a constant flow through a certain pathway. The second is the restriction cost, which measures how unlikely it is to block all the alternative reactions in the network while keeping only the pathway of interest active.
The calculated improbability represents the cost of that process, which can then be used to classify metabolic pathways according to how “expensive” it is for the cell to keep one pathway active and silence the others.
Why Nature Chooses What It Chooses
“We saw things we didn’t expect, but that make sense once you think about them,” Gagrani explains. “For example, that using multiple pathways at the same time is less costly than using just one. Here’s an analogy: imagine four people who need to go from A to B through narrow tunnels. If each person has their own tunnel — four tunnels — they arrive more quickly than if there are only three or fewer, because two or more people would obstruct each other in the same narrow passage.”
In nature, however, we usually see that one process is favored over many. How is that explained? “It’s true, but in biological systems, catalysis often intervenes — the action of facilitating molecules, enzymes — which accelerate reactions and make them less costly, achieving the same effect as having multiple pathways in parallel. This evolutionary choice happens because maintaining many pathways can have other drawbacks, such as producing many potentially toxic molecules.”
“Our method,” concludes Gagrani, “is a useful tool for studying the origin and evolution of life because it allows us to evaluate the costs of choosing and maintaining specific metabolic processes. It helps us understand how certain pathways arise — but explaining why those particular ones were selected requires a truly multidisciplinary effort.”
Reference: “Thermodynamic ranking of pathways in reaction networks” by Praful Gagrani, Nino Lauber, Eric Smith and Christoph Flamm, 6 January 2026, Journal of Statistical Mechanics: Theory and Experiment.
DOI: 10.1088/1742-5468/ae22eb
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