Model Code

We developed a proteome allocation model for phytoplankton that optimizes for cell size, the relative investment into different protein pools, and the concentration of different macromolecules in order to maximize growth rate according to temperature, light, and external inorganic nutrient availability.

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CarveWe is a comprehensive workflow that integrates other open source tools, chiefly the CarveMe model annotation [software](https://github.com/cdanielmachado/carveme), and offers several distinct applications for metabolic modeling. Principally the complete workflow was designed to apply SOM neural networks to feature data of metabolic sensitivities based on in silico nutrient limitation tests in order to group marine heterotrophic organisms into functional cohorts.

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The model represents molecular-level transport and cellular proteome allocation specifically designed to compare the performance of ABC transport and PTS.

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This model captures key micro-scale dynamics occurring on particulate organic carbon (POC) in a manner scalable to the water column. We represent 18 groups of particle-associated heterotrophic microbes, encounter rates, enzymatic production and degradation of POC, and dynamic sinking rates.

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This model extracts a low-dimensional representation of binary data into groups of features that are tightly linked and co-occur with high specificity. The model pipeline is designed for use with binary presence/absence data of genomic features (e.g., presence/absence of genes, KO pathways, metabolic reaction components, etc.) to identify marine microbial functional guilds as an alternative to taxonomic community structure.

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The TRAit Correlation Evolution (TRACE) model framework simulates the adaptive walk of a microbial population across a trait landscape (trait-scape) towards a high-fitness area.

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An individual-based model of adaptation modified from Fisher’s model in which the simulated population moved between the “new” and “ancestral” environments. Adaptation is driven by both fast variation, low transmission and slow variation, high transmission modifications.

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This model combines a quota-based phytoplankton model with a stochastic evolution step for certain traits (similar to the TRACE model). The goal of this model is to explore the possible evolutionary trajectories within phytoplankton and their effect on intra- and interspecific population dynamics.

This model competes multiple populations of phytoplankton grown on a single nutrient and allows for specific nutrient response strategies to be toggled on or off for each population.