DOI: 10.5061/dryad.s4mw6m9h2
title: Data for: Linking cell size, Vmax, and Km in phototrophs and chemotrophs: Insights from Bayesian inference

description: Microbial growth is often described in terms of resource uptake rates,
 making the understanding and parameterization of these rate-limiting
 processes critical for microbial modeling. In phototrophic plankton,
 theoretical studies suggest that nutrient uptake is limited by mechanistic
 processes involving membrane transporters, and it has been observed that
 the cell-specific maximum resource uptake rate (Vmax) follows a power-law
 relationship with cell size, as well as a trade-off between Vmax and the
 half-saturation constant (Km). These constraints may also apply
 to chemotrophic microorganisms; however, many datasets lack direct
 cell-size measurements. We therefore leveraged the assumption that
 prokaryotic cell sizes, Vmax, and Km each
 follow log-normal distributions, drawing parallels with established
 phytoplankton scaling laws. Our analysis suggests that chemotrophic
 organisms generally exhibit higher maximum uptake rate per dry
 weight (VmaxDW) and Km values than phototrophs, and
 that VmaxDW and Km are not strongly correlated
 when all chemotroph data are combined. Furthermore, the Bayesian-derived
 exponents for VmaxDW and Km exceeded those expected from
 allometric scaling relationships based on the membrane-transport capacity
 observed for phototrophs, implying that a range of additional factors
 likely affect observed kinetic parameters.
description: # Data for: Linking cell size, Vmax, and Km in phototrophs and
 chemotrophs: Insights from Bayesian inference This repository contains all
 data and scripts required to reproduce the analyses and figures presented
 in the study. ## Directory Structure Data_and_Programs ├── README.md ├──
 Original_data │ ├── README.md │ ├──
 Microbial_Parameter_database_kinetics.csv │ └── Phytoplankton_Data │ ├──
 Brandenburg_et_al_2018_Ecol_Lett_Aost15.xlsx │ ├──
 Brandenburg_et_al_2018_Ecol_Lett_Aost16.xlsx │ ├──
 Brandenburg_et_al_2018_Ecol_Lett_Baltic.xlsx │ ├──
 Marañón_et_al_2012_Ecol_Lett.xlsx │ ├──
 Perrin_et_al_2015_Biogeosciences.xlsx │ └──
 Rees_2014_Mar_Ecol_Prog_Ser.xls ├── Derived_data │ ├── README.md │ ├──
 chain.csv │ ├── dataAll.json │ ├── KmDB.csv │ ├── logLikes.csv │ ├──
 VmaxDB.csv │ └── vol_weight.json └── Scripts ├── README.md ├──
 DataProcessingAndPDFs.wl ├── Generate_Figure1.nb ├──
 Generate_Other_Figures.nb ├── Hartigans_dip_test.R └── PlotSettings.wl ##
 Directory Overview * `Original_data/`\ Contains the original datasets used
 in this study, including: * A compiled microbial kinetics database
 (`Microbial_Parameter_database_kinetics.csv`) * Published phytoplankton
 datasets (Excel files) * `Derived_data/`\ Contains processed datasets
 generated through our analysis pipeline: * Data used for MCMC sampling and
 statistical fitting * Summary statistics and transformed data files *
 `Scripts/`\ Includes all analysis scripts and plotting notebooks: *
 Wolfram Language scripts for data processing and figure generation * An R
 script used for Hartigan’s dip test ## Reproducibility All scripts and
 data are organized to ensure reproducibility. To regenerate figures and
 tables: 1. Load the `.nb` notebooks in `Scripts/` (e.g.,
 `Generate_Figure1.nb`) in Wolfram Mathematica. 2. Ensure relative paths
 remain consistent with this directory structure. 3. Required packages are
 included via `Get[]` commands in the notebooks. All computations and
 figure generations have been tested using **Wolfram Mathematica version
 12.0.0**. Functionality with other versions has not been verified and may
 result in unexpected behavior. ## Contact For questions regarding the
 dataset or code, please contact: Mayumi Seto
 ([seto@ics.nara-wu.ac.jp](mailto:seto@ics.nara-wu.ac.jp) or
 [setomayumi@gmail.com](mailto:setomayumi@gmail.com)) Department of
 Chemistry, Biology, and Environmental Science Nara Women’s University,
 Japan