get_energetics {ceas}

Calculate ATP Production from OXPHOS and Glycolysis

Description

Calculates ATP production from glycolysis and OXPHOS at points defined in patitioned_data

Usage

get_energetics(partitioned_data, ph, pka, buffer)

Arguments

partitioned_data

a data.table of organized Seahorse OCR and ECAR rates based on timepoints from the assay cycle. Returned by partition_data

ph

pH value for energetics calculation (for XF Media, 7.5)

pka

pKa value for energetics calculation (for XF Media, 6.063)

buffer

buffer for energetics calculation (for XF Media, 0.1 mpH/pmol H+)

Details

TODO: check that all symbols are defined

Proton production rate (PPR):

\text{PPR} = \frac{\text{ECAR value}}{\text{buffer}}

\text{PPR}_{\text{mito}} = \frac{10^{\text{pH}-\text{pK}_a}}{1+10^{\text{pH}-\text{pK}_a}} \cdot \frac{\text{H}^+}{\text{O}_2} \cdot \text{OCR}

calculates the proton production from glucose during its conversion to bicarbonate and \text{H}^+ assuming max \frac{\text{H}^+}{\text{O}_2} of 1

\text{PPR}_\text{glyc} = \text{PPR} - \text{PPR}_\text{resp}

calculates the proton production from glucose during its conversion to lactate + \text{H}^+

Joules of ATP (JATP) production:

\text{ATP}_{\text{glyc}} = \Bigl(\text{PPR}_\text{glyc} \cdot \frac{\text{ATP}}{\text{lactate}}\Bigl) + \Bigl(\text{MITO}_\text{resp} \cdot 2 \cdot \frac{\text{P}}{\text{O}_\text{glyc}}\Bigl)

\frac{\text{ATP}}{\text{lactate}} = 1

with \frac{\text{P}}{{\text{O}_\text{glyc}}} = 0.167 for glucose (0.242 for glycogen).

\text{ATP}_\text{resp} = \Bigl(\text{coupled MITO}_\text{resp} \cdot 2 \cdot \frac{\text{P}}{\text{O}_\text{oxphos}}\Bigl) + \Bigl(\text{MITO}_\text{resp} \cdot 2 \cdot \frac{\text{P}}{\text{O}_\text{TCA}}\Bigl)

with \frac{\text{P}}{{\text{O}_\text{oxphos}}} = 2.486 and \frac{\text{P}}{{\text{O}_\text{TCA}}} = 0.167.

Value

a data.table of glycolysis and OXPHOS rates

Examples

rep_list <- system.file("extdata", package = "ceas") |>
  list.files(pattern = "*.xlsx", full.names = TRUE)
seahorse_rates <- read_data(rep_list, sheet = 2)
partitioned_data <- partition_data(seahorse_rates)
energetics <- get_energetics(partitioned_data, ph = 7.4, pka = 6.093, buffer = 0.1)
head(energetics, n = 10)