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| Shortcutting biology leads back to physics |
| By Stilianos Louca. May 16, 2019 |
Introduction
A major goal of biological and environmental research is to predict the rates at which biochemical processes occur in natural and engineered ecosystems.
For example, biochemical reactions driven by microorganisms in the ocean water and sediments have a major impact on the ocean's nitrogen budget, which in turn influences the entire marine food web and even climate.
Ideally, we would like to accurately predict these processes while requiring as little a priori information as possible (since information is costly and may not always be available).
There are two major types of processes that must be mathematically accounted for when modeling the bio(geo)chemistry of ecosystems:
- The kinetics of biological growth and metabolism, i.e. the rates at which biological populations grow and perform various reactions depending on local substrate concentrations.
- The physical transport of living and non-living matter across space, for example through microscopic diffusion processes, through ocean currents, or the sinking of particles toward the bottom of the ocean due to gravity.
The biological kinetics are usually the most complex and the least understood, and therefore often constitute an obstacle to our ability to predict the biogeochemistry of ecosystems.
It is widely acknowledged, for example, that the accuracy of our climate models could be substantially improved if we had a better understanding of the biological processes involved.
Incidentally, the time scales of microbial kinetics, i.e. the typical time it takes for microorganisms to grow and deplete available substrates, are often much shorter than the time scales of physical transport across an ecosystem.
For example, in poorly mixed lakes or stagnant ocean basins, it can take months to years for chemical compounds to disperse from one end of the water column to the other, while microorganisms can grow, die and deplete local chemical resources within just a few days.
Perhaps, then, it may be possible to take advantage of this separation between microbial and physical time scales, and somehow "shortcut" the microbial kinetics in our mathematical models.
Microbial kinetics can be shortcutted
In our latest study, published in the journal Proceedings of the National Academy of Sciences, we show how one can indeed shortcut the microbial kinetics in a variety of ecosystems using a new mathematical approach.
The idea is simple: If microorganisms are efficient at utilizing substrates and performing chemical reactions to gain energy, then ultimately the rates of these reactions will become limited by the rate at which substrates can be transported across space (i.e., from their origin to where they are consumed).
Standard ecological theory in fact predicts that ultimately biological populations become very efficient at utilizing substrates for energy, because the most efficient organisms are selected for in the long run.
Under such a scenario, many metabolic reactions end up occurring in narrow chemical transition zones, i.e. where the two main substrates of the reaction overlap only within a very narrow zone.
The reason for this minimal overlap is that molecules of either substrate are quickly consumed before they have had time to penetrate deep into the zone of the other substrate.
In these transition zones, reaction rates are predicted to be substantially higher than elsewhere, which is why we called these zones "hotspots".
Such hotspots are in fact widely observed in nature and microbial abundances tend to be highest around these hotspots.
Based on the prediction that microbially driven reactions will ultimately concentrate around hotspots, and based on the fact that substrate fluxes into these hotspots must obey specific ratios depending on the reactions involved, we devised a mathematical method that can predict the location of these hotspots and the reaction rates within them purely based on the active physical transport processes and without any knowledge of the underlying microbial kinetics.
Our method, which we called "Spatial Metabolic Flux" analysis, works best for systems near steady state, i.e. where chemical concentrations and microbial abundances have stabilized and change only slowly or not at all over time.
Such systems include sediments in the deep ocean, subsurface soil, stagnant deep ocean basins, various poorly mixed lakes and a variety of biofilms.
Explaining the biogeochemistry in the Cariaco Basin
We tested the accuracy of our Spatial Metabolic Flux analysis method in the deeper waters of the Cariaco Basin in Venezuela, one of the largest permanently oxygen-depleted (and thus microbially dominated) marine basins in the world.
The Cariaco Basin has received considerable attention by scientists over the last few decades, because it constitutes a model ecosystem from which we can learn a lot about the ocean's nutrient cycles and the effects of climate change on ocean oxygen concentrations.
Using our method, we were indeed able to predict the distribution and flux rates for multiple major chemical compounds used by the microorganisms for energy, including oxygen, hydrogen sulfide (a potent toxin to most animals) and ammonium, across hundreds of meters and over multiple years.
Importantly, our predictions were based entirely on the physics of the system (i.e., the hydrodynamical mixing processes) and the chemical boundary conditions (i.e., the concentrations of the various substrates at the top and bottom of the considered water column), and did not require any knowledge of the resident microorganisms and their kinetics.
The hotspot locations predicted using our approach also coincided with the typical depths at which measured microbial abundances as well as productivity rates in Cariaco Basin tend to peak, thus providing a simple explanation for the biomass distribution seen in this ecosystem.
Closing remarks
Apart from the practical implications for biogeochemical and climate modeling, our results also have an important conceptual dimension.
Indeed, the realization that metabolic rates of entire ecosystems can be predicted regardless of the resident microorganisms, could explain why in many previous studies a high variability of microbial species seemed to have little effect on the overall biochemical nature of a system.
While the work described here applies mainly to stabilized systems, it may be possible to generalize our methods to more dynamic situations, such as ecosystems influenced heavily by seasonal changes.
This possibility is something our lab is looking into for the future.
Full scientific article:
Louca, S., Scranton, M. I., Taylor, G. T., Astor, Y. M., Crowe, S. A., Doebeli, M. (2019). Circumventing kinetics in biogeochemical modeling. PNAS 116:11329-11338 |
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Louca lab. Department of Biology, University of Oregon, Eugene, USA
© 2025 Stilianos Louca all rights reserved
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