Genomics, Proteomics, Bioinformatics

Modeling Genome-scale Knowledge In Earth’s Global Ocean

By Keith Cowing
Status Report
December 4, 2023
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Modeling Genome-scale Knowledge In Earth’s Global Ocean
Illustration of the modeling combination between a Genome-Scale Model (GSM), i.e., Prochlorococcus MED4 GSM, and an Earth Systems Model (ESM), i.e., NEMO-PISCES, and comparison between growth rates estimated from ESM and GSM. a |From a metabolic network, we defined a solution space embedding all possible fluxes that go through each network reaction. These fluxes satisfy the quasi-steady states assumption and other thermodynamic constraints defined in the GSM. This set of constraints (C1) is defined as biotic constraints, and they affect inner reactions, as well as exchange reactions responsible for the uptake or secretion of nutrients, and a biomass reaction simulating the growth of the organism (color scale similar to panel d., see Appendix 1 for details). b |Earth Systems Models predict global ocean biogeochemistry across space and time. Here the ESM provides uptake fluxes of nutrients for each grid point for each modeled organism. In our framework, these uptake values are used as a set of constraints (C2) on the exchange reactions of the GSM. These are defined as the set of abiotic constraints that are applied in the model. c |C1 and C2 are combined to constrain further exchanged metabolite fluxes at each grid point of the global ocean. As a result, we can estimate the organismal growth rate and all feasible inner fluxes corresponding to a given environment as proposed by the ESM. d |Description of growth rates (h−1) at 5 m depth estimated from NEMO-PISCES picophytoplankton (top) and Prochlorococcus MED4 GSM (bottom). The dashed line shows the transect described in the following panel. e |Distribution of respective growth rates across latitudes and depths at longitude -24o. Grey areas indicate latitudes that do not allow Prochlorococcus MED4 growth because of thermal limits; the GSM does not consider them. The relationship between growth rates across space (above 500 m) and time (i.e., without gray areas) shows R2:0.80 and slope: 0.787 (see Extended Fig. 11). —

Editor’s note: As we make plans to do orbital and surface sorties on other worlds searching for life it is certainly useful to use Earth and its diverse biota as an analog to figure out how to do this. If we do find life on a world we’ll want to catalog what we find based on habitat, physiology, and genomics. Using planet Earth as a testbed for planetary-level genomic modeling is a good way to learn how to do the same thing on other worlds – and we can start doing it right now.

Earth System Models (ESMs) highly simplify their representation of biological processes, leading to major uncertainty in climate change impacts.

Despite a growing understanding of molecular networks from genomic data, describing how changing phytoplankton physiology affects the production of key metabolites remains elusive.

Here we embed a genome-scale model within a state-of-the-art ESM to deliver an integrated understanding of how gradients of nutritional constraints modulate metabolic reactions and molecular physiology.

Applied to the prevalent marine cyanobacteria Prochlorococcus, we find that glycogen and lipid storage can be understood as a consequence of acclimation to environmental gradients.

Given the pressing need to assess how biological diversity influences biogeochemical functions, genome-enabled ESMs allow the quantification of the contribution of modeled organisms to the production of dissolved organic carbon and its molecular composition.

Investigation of Prochlorococcus MED4 genome-scale model fitness and acclimation strategies across the global ocean (more than 106 estimations). a |Description of Prochlorococcus MED4 genome-scale behavior across the Atlantic Ocean transect (longitude -24o). It describes growth rate according to light and temperature, associated nutrient constraints (light, nitrogen, and phosphorus), and acclimation consequences (glycogen and lipid productions per biomass) resumed by the glycogen storage index (see Material and Methods for details), with blue colors indicating consumption of potential glycogen stock and green colors showing increased storage. b |Distribution of lipid contribution to Prochlorococcus MED4 biomass over five distinct temperature ranges (see Materials and Methods for details). c |Distribution of glycogen production satisfying predicted Prochlorococcus MED4 growth rates under four categories of gradual light exposures. d |Distribution of glycogen storage indices computed with estimated Prochlorococcus MED4 growth rates under four similar categories of gradual light exposures. Indices between 0 and 0.46 index indicate a gradual decrease of glycogen stocks to support growth. Above 0.46, indices are associated with full phototrophic growth with increased glycogen storage. —

Towards modeling genome-scale knowledge in the global ocean (open access)

Astrobiology , Genomics,

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) 🖖🏻