Package 'rbcmodel' reference manual

Title:	Model Rubisco Carboxylation Rate Across Temperature, CO2, and O2
Description:	Collects published kinetics of ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) and uses these kinetics to model the carboxylation rate across CO2 and O2 concentrations and different temperatures. The carboxylation rate can be modeled as the gross rate or the net rate, which takes into account the oxygenase activity of the enzyme using one of the three known phosphoglycolate salvage pathways. Custom enzymes and temperature dependences can be created with user kinetics, or published kinetics can be accessed through a meta-analysis contained within the package. An expansion of methods from Harrison et al. (2025) <doi:10.1128/aem.00604-25>.
Authors:	Kaitlin Harrison [aut, cre, cph], Wing-Ho Ko [aut]
Maintainer:	Kaitlin Harrison <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.1
Built:	2026-10-04 09:45:38 UTC
Source:	https://github.com/keharr/rbcmodel

Validator for the S3 class 'DHScale'

Description

Throw error if any scaling constants are non-numeric, or if name is not a character string.

Usage

check_DHScale(the_DHScale)
check_DHScale(the_DHScale)

Arguments

the_DHScale

the 'DHScale' instance to be checked.

Value

the supplied DHScale instance, if all checks pass.

Examples

bad_DHScale <- new_DHScale(NA, 41, 26, -13.8, name="bad") # bad instance
try(check_DHScale(bad_DHScale)) # produces error

bad_DHScale <- new_DHScale(NA, 41, 26, -13.8, name="bad") # bad instance
try(check_DHScale(bad_DHScale)) # produces error

Validator for the S3 class 'enzyme'

Description

Throw error if any kinetic values or temperature are non-numeric, or if name is not a character string.

Usage

check_enzyme(the_enzyme)
check_enzyme(the_enzyme)

Arguments

the_enzyme

the 'enzyme' instance to be checked.

Value

the supplied enzyme instance, if all checks pass.

Examples

bad_enzyme <- new_enzyme(NA, 144, 394, 10.8, name="bad") # bad instance
try(check_enzyme(bad_enzyme)) # produces error
bad_enzyme <- new_enzyme(NA, 144, 394, 10.8, name="bad") # bad instance
try(check_enzyme(bad_enzyme)) # produces error

Cite data collected in the package.

Description

Cite data collected in the package.

Usage

cite_Rbc(identifier, type = "full")
cite_Rbc(identifier, type = "full")

Arguments

identifier

The identifier of the data. Takes a single identifier or a list of identifiers.

type

The type of citation desired. Can take "full", which will return the full citation, or "short", which returns the pmid or doi, if available.

Value

For data from a single study, this returns the citation for that study. For data averaged or compiled by this study, returns a note to cite this package.

Examples

cite_Rbc("average_1B_all")
cite_Rbc(c("japonica_Sage_2002b","japonica_orr_2016_dH","average_1B_C3_warm_dH"))
cite_Rbc("breve_banda_2020",type="short")
cite_Rbc("average_1B_all")
cite_Rbc(c("japonica_Sage_2002b","japonica_orr_2016_dH","average_1B_C3_warm_dH"))
cite_Rbc("breve_banda_2020",type="short")

Creates a combined model that compares two different enzymes

Description

Creates a combined model that compares two different enzymes. Requires the input of two models output from the CO2_dependence equation.

Usage

CO2_comparison(model1, model2)
CO2_comparison(model1, model2)

Arguments

model1

The first model to compare. Positive values from the output equation will mean that this modeled enzyme is faster.

model2

The second model to compare. Negative values from the output equation will mean that this modeled enzyme is faster.

Value

A function with the same inputs as the original models comparing the two models.

Examples

pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro")
bacIA_DHScale <- new_DHScale(47.2,40.8,26.7,-21.8,name="bacIA")
pro_model <- CO2_dependence(pro_enzyme, bacIA_DHScale, "canon") #first model
tmrIA_DHScale <- modify_DHScale(bacIA_DHScale,kcat_dH=32,name="tmr_IA")
tmrIA_model <- CO2_dependence(pro_enzyme, tmrIA_DHScale, "canon") #second model
tmr_vs_pro <- CO2_comparison(pro_model, tmrIA_model) #creating the comparison
print(tmr_vs_pro) # prints the comparison equation
# prints the amount for which the tmrIA_model is faster than the pro_model
tmr_vs_pro(126,54,15)

pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro")
bacIA_DHScale <- new_DHScale(47.2,40.8,26.7,-21.8,name="bacIA")
pro_model <- CO2_dependence(pro_enzyme, bacIA_DHScale, "canon") #first model
tmrIA_DHScale <- modify_DHScale(bacIA_DHScale,kcat_dH=32,name="tmr_IA")
tmrIA_model <- CO2_dependence(pro_enzyme, tmrIA_DHScale, "canon") #second model
tmr_vs_pro <- CO2_comparison(pro_model, tmrIA_model) #creating the comparison
print(tmr_vs_pro) # prints the comparison equation
# prints the amount for which the tmrIA_model is faster than the pro_model
tmr_vs_pro(126,54,15)

Function factory to model an enzyme's carbon output

Description

Function factory to model an enzyme's carbon output versus temperature and CO2/O2 concentration. Uses a 'DHScale' object to scale each kinetic parameter from an 'enzyme' object in regards to temperature. Based on the stoichiometry of known phosphoglycolate salvage pathways, it also takes into account the carbon lost due to the Rubisco oxygenation reaction based on user input.

Usage

CO2_dependence(enzyme, DHScale, PGS = NULL)
CO2_dependence(enzyme, DHScale, PGS = NULL)

Arguments

enzyme

An 'enzyme' object. (See enzyme class for more info.)

DHScale

A 'DHScale' object. (See DHScale class for more info.)

PGS

One of 'gross', 'canon', 'diatom', or 'alt'. This option allows you to pick a stoichiometry for the phosphoglycolate salvage (PGS) pathway. If NULL, the choice is inferred from the default PGS of the enzyme instance. If you do not want to incorporate PGS, use the 'gross' option, while 'canon' applies the C2 cycle/glycerate pathway stoichiometry (1 CO2 for every 2 2-phosphoglycolate created). 'alt' corresponds to the oxalyl-CoA or malate cycle pathways (2 CO2 for every 2-phosphoglycolate created). 'diatom' corresponds to the proposed diatom-specific pathways, which do not return 2-PG to the Calvin Cycle, but incorporate it directly into biomass with the exception of 1 CO2 for every 2-phosphoglycolate created.

Value

A new function that models carbon output with three variables: CO2 (in uM), O2 (in uM), and temperature (in celsius).

Examples

pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro")
bacIA_DHScale <- new_DHScale(47.2,40.8,26.7,-21.8,name="bacIA")
pro_model <- CO2_dependence(pro_enzyme, bacIA_DHScale, "canon") # sets up model
pro_model(126,54,15) # prints carbon fixation at 15 deg C, 126 uM CO2, 54 uM O2
pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro")
bacIA_DHScale <- new_DHScale(47.2,40.8,26.7,-21.8,name="bacIA")
pro_model <- CO2_dependence(pro_enzyme, bacIA_DHScale, "canon") # sets up model
pro_model(126,54,15) # prints carbon fixation at 15 deg C, 126 uM CO2, 54 uM O2

Create an DHScale instance by looking up the relevant data in database

Description

Create an DHScale instance by looking up the relevant data in database

Usage

DHScale(id_name, id_col = "identifier", scale_name = NULL, data = NULL)
DHScale(id_name, id_col = "identifier", scale_name = NULL, data = NULL)

Arguments

id_name

the name of identifier used to pull out a single record out of the database

id_col

the name of the column from which the id_name will be searched for within the database

scale_name

the name of the DHScale instance. If NULL the value is defaulted to id_name

data

the database to search from. Can be "averaged", NULL, or a data.frame. If NULL or "averaged", the averaged table is searched. NOTE: if a custom data.frame is supplied, it is assumed to have columns named "kcat_dH", "Kc_dH", "Ko_dH", and "S_dH".

Value

a new DHScale instance

Examples

cyano <- DHScale("average_1Ac_Cyanobacteria_dH") # retrieved from averaged database
cyano <- DHScale("average_1Ac_Cyanobacteria_dH") # retrieved from averaged database

Create an Enzyme instance by looking up the relevant data in database

Description

Create an Enzyme instance by looking up the relevant data in database

Usage

Enzyme(id_name, id_col = "identifier", enzyme_name = NULL, data = NULL)
Enzyme(id_name, id_col = "identifier", enzyme_name = NULL, data = NULL)

Arguments

id_name

the name of identifier used to pull out a single record out of the database

id_col

the name of the column from which the id_name will be searched for within the database

enzyme_name

the name of the enzyme instance. If NULL the value is defaulted to id_name

data

the database to search from. Can be "abridged", "comprehensive", NULL, or a data.frame. If NULL, the abridged table is first searched and if no entry is found the comprehensive table is searched. NOTE: if a custom data.frame is supplied, it is assumed to have columns named "kcat_val", "Kc_val", "Ko_val", and "S_val". In addition, it may have optional columns "temp", "kcat_T", "Kc_T", "Ko_T", "S_T", and "PGS"

Value

a new Enzyme instance

Examples

biuncialis <- Enzyme("biuncialis_Prins_2016") # retrieved from abridged database
# specify enzyme using species name
vavilovii <- Enzyme("vavilovii", "species", data="comprehensive")
biuncialis <- Enzyme("biuncialis_Prins_2016") # retrieved from abridged database
# specify enzyme using species name
vavilovii <- Enzyme("vavilovii", "species", data="comprehensive")

Function factory for calculating temperature-dependent kinetics

Description

Calculates the non-supplied constant (c) for an Arrhenius-type temperature scaling function (as seen in Galmés et al. 2016). Uses measured values and the temperature they were measured at, along with a supplied temperature scaling constant (also called the activation energy, $\Delta$ H (in kJ/mol)).

Usage

kinetics_T_dep(std, delta_H, temp = 25)
kinetics_T_dep(std, delta_H, temp = 25)

Arguments

std

Measured value

delta_H

$\Delta$ H value for this kinetic parameter for this organism type (in kJ/mol).

temp

Temperature at which param 'std' was measured. Defaults to 25 $^\circ$ C.

Value

A new function that scales the supplied kinetic parameter with temperature (in celsius).

Examples

kcat_proteo_Ia <- kinetics_T_dep(15, 44.7) # create new function
kcat_proteo_Ia(30) # evaluate the kinetic parameter at a different temperature

kcat_proteo_Ia <- kinetics_T_dep(15, 44.7) # create new function
kcat_proteo_Ia(30) # evaluate the kinetic parameter at a different temperature

Return the last non-NA value from a vector.

Description

Return the last non-NA value from a vector.

Usage

last_non_NA(x)
last_non_NA(x)

Arguments

x

the vector to extract values from.

Value

The last non-NA value, or NA if none exists.

Construct a 4D grid

Description

Construct a 4D grid, where the first 3 coordinates are independent variables, while the 4th coordinate is dependent variable arising from evaluating a function against the values of the first 3 coordinates.

Usage

make_4D_grid(func, x_seq, y_seq, z_seq, var_names = c("x", "y", "z", "f"))
make_4D_grid(func, x_seq, y_seq, z_seq, var_names = c("x", "y", "z", "f"))

Arguments

func

The function to apply on the grid points to obtain the value of the dependent variable.

x_seq

A vector of coordinates for the first independent variable.

y_seq

A vector of coordinates for the second independent variable.

z_seq

A vector of coordinates for the third independent variable.

var_names

Names for the 3 independent and 1 dependent variables, in a single character vector.

Details

The grid is represented by a 4-element list, where each member is a 3-dimensional array representing grid points. The first array is the value of first independent variable on each grid point, etc., and the fourth array is the value of function evaluated at each grid point.

Value

A 4D grid as a 4-element list of 3-dimensional arrays

Examples

f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
g1
f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
g1

Compute the mean of a vector if the vector is numerical or logical. Otherwise, return the common value if all entries take the same value, or NA in any other cases.

Description

Compute the mean of a vector if the vector is numerical or logical. Otherwise, return the common value if all entries take the same value, or NA in any other cases.

Usage

mean_if_num(x, na.rm = TRUE)
mean_if_num(x, na.rm = TRUE)

Arguments

x

the vector to compute average from.

na.rm

whether NA's are ignored for the purpose of computing average or for checking consistency between entries.

Value

The average or common value, if exists.

Compute the median of a vector if the vector is numerical or logical. Otherwise, return the common value if all entries take the same value, or NA in any other cases.

Description

Compute the median of a vector if the vector is numerical or logical. Otherwise, return the common value if all entries take the same value, or NA in any other cases.

Usage

median_if_num(x, na.rm = TRUE)
median_if_num(x, na.rm = TRUE)

Arguments

x

the vector to compute average from.

na.rm

whether NA's are ignored for the purpose of computing average or for checking consistency between entries.

Value

The average or common value, if exists.

Merge (combine) several entries from a data.frame.

Description

Merge (combine) several entries from a data.frame.

Usage

merge_entries(
  data,
  id_col,
  id_vals,
  method,
  new_id = NA,
  const_cols = NULL,
  keep_merged = TRUE,
  keep_unmerged = TRUE
)
merge_entries(
  data,
  id_col,
  id_vals,
  method,
  new_id = NA,
  const_cols = NULL,
  keep_merged = TRUE,
  keep_unmerged = TRUE
)

Arguments

data

The data.frame from which entries are pulled.

id_col

The column from which the relevant entries are identified.

id_vals

The values from id_col for which the corresponding entries will be pulled.

method

The method to combine entries. Can be "average", "median", or "last".

new_id

The new identifier (for id_col) for the merged entry.

const_cols

The columns to check for consistency. Error is thrown if the values from the pulled entries are not identical for any such columns.

keep_merged

Whether to keep the original entries that are pulled and merged.

keep_unmerged

Whether to keep the original entries that have NOT been touched.

Value

a data frame consisting of the merged entries, and optionally the unmerged and/or the untouched entries.

Examples

df <- data.frame(
  id = 1:4,
  x = c(1, 2, NA, 3),
  y = c("this", "this", NA, "that")
)

df2 <- merge_entries(df, "id", c(1,4), "average", new_id = "new")
df2

df3 <- merge_entries(df, "id", c(4, 3, 1), "last", keep_merged = FALSE)
df3

try(merge_entries(df, "id", c(4, 3, 1), "last", const_cols="y")) # expects error
df <- data.frame(
  id = 1:4,
  x = c(1, 2, NA, 3),
  y = c("this", "this", NA, "that")
)

df2 <- merge_entries(df, "id", c(1,4), "average", new_id = "new")
df2

df3 <- merge_entries(df, "id", c(4, 3, 1), "last", keep_merged = FALSE)
df3

try(merge_entries(df, "id", c(4, 3, 1), "last", const_cols="y")) # expects error

Create a new instance of S3 class 'DHScale', starting from a previous instance

Description

If any argument (other than the_DHScale, which is required) is NULL, the corresponding instance attribute will copy its value from the previous instance. Otherwise, the input value will become the value of the corresponding attribute in the new instance. For detailed explanation of each 'DHScale' attribute, see the documentation of new_DHScale()

Usage

modify_DHScale(
  the_DHScale,
  kcat_dH = NULL,
  Kc_dH = NULL,
  Ko_dH = NULL,
  S_dH = NULL,
  name = NULL
)
modify_DHScale(
  the_DHScale,
  kcat_dH = NULL,
  Kc_dH = NULL,
  Ko_dH = NULL,
  S_dH = NULL,
  name = NULL
)

Arguments

the_DHScale

the previous 'DHScale' instance to base the new instance on.

kcat_dH

if not NULL, the kcat_dH value for the new instance

Kc_dH

if not NULL, the Kc_dH value for the new instance

Ko_dH

if not NULL, the Ko_dH value for the new instance

S_dH

if not NULL, the S_dH value for the new instance

name

if not NULL, the identifier ("name") for the new instance

Value

a new instance of the S3 class 'DHScale'.

Examples

bacIA_DHScale <- new_DHScale(47.2,40.8,26.7,-21.8,name="bacIA") # new instance
print(bacIA_DHScale) # printout DHScale info
tmrIA_DHScale <- modify_DHScale(bacIA_DHScale,kcat_dH=32,name="tmr_IA") # modify instance
print(tmrIA_DHScale) # printout modified DHScale info

bacIA_DHScale <- new_DHScale(47.2,40.8,26.7,-21.8,name="bacIA") # new instance
print(bacIA_DHScale) # printout DHScale info
tmrIA_DHScale <- modify_DHScale(bacIA_DHScale,kcat_dH=32,name="tmr_IA") # modify instance
print(tmrIA_DHScale) # printout modified DHScale info

Create a new instance of S3 class 'enzyme' starting from a previous instance

Description

If any argument (other than the_enzyme, which is required) is NULL, the corresponding instance attribute will copy its value from the previous instance. Otherwise, the input value will become the value of the corresponding attribute in the new instance. For detailed explanation of each 'enzyme' attribute, see the documentation of new_enzyme()

Usage

modify_enzyme(
  the_enzyme,
  kcat_val = NULL,
  Kc_val = NULL,
  Ko_val = NULL,
  S_val = NULL,
  temp = NULL,
  kcat_T = NULL,
  Kc_T = NULL,
  Ko_T = NULL,
  S_T = NULL,
  T_max = NULL,
  PGS = NULL,
  name = NULL
)
modify_enzyme(
  the_enzyme,
  kcat_val = NULL,
  Kc_val = NULL,
  Ko_val = NULL,
  S_val = NULL,
  temp = NULL,
  kcat_T = NULL,
  Kc_T = NULL,
  Ko_T = NULL,
  S_T = NULL,
  T_max = NULL,
  PGS = NULL,
  name = NULL
)

Arguments

the_enzyme

the previous 'enzyme' instance to base the new instance on.

kcat_val

if not NULL, the kcat_val value for the new instance

Kc_val

if not NULL, the Kc_val value for the new instance

Ko_val

if not NULL, the Ko_val value for the new instance

S_val

if not NULL, the S_val value for the new instance

temp

NOT USED. A warning is produced if this argument is used. Please set the temperature for each kinetic parameters separately

kcat_T

if not NULL, the kcat_T value for the new instance

Kc_T

if not NULL, the Kc_T value for the new instance

Ko_T

if not NULL, the Ko_T value for the new instance

S_T

if not NULL, the S_T value for the new instance

T_max

if not NULL, the upper thermal limit of the new instance

PGS

if not NULL, the default phosphoglycolate salvage (PGS) pathway for the new instance

name

if not NULL, the identifier ("name") for the new instance

Value

a new instance of the S3 class 'enzyme'.

Examples

pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro") # new instance
print(pro_enzyme) # printout enzyme info
pro_worse_enzyme <- modify_enzyme(pro_enzyme, kcat_val=2, Kc_val=310, name="pro_worse")
print(pro_worse_enzyme) # printout modified enzyme info
pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro") # new instance
print(pro_enzyme) # printout enzyme info
pro_worse_enzyme <- modify_enzyme(pro_enzyme, kcat_val=2, Kc_val=310, name="pro_worse")
print(pro_worse_enzyme) # printout modified enzyme info

Constructor for the S3 class 'DHScale'.

Description

This class collects temperature scaling constants for each of the four kinetic parameters. These scaling constants are also called the activation energy of that kinetic parameter, and are typically denoted with $\Delta$ H. In combination with an enzyme-class object, a DHScale-class object can be used to create a function that models an enzyme's carbon output with regards to temperature and CO2/O2 concentration.

Usage

new_DHScale(kcat_dH, Kc_dH, Ko_dH, S_dH, name = "")
new_DHScale(kcat_dH, Kc_dH, Ko_dH, S_dH, name = "")

Arguments

kcat_dH

The temperature scaling constant of kcat for the carboxylation reaction, also known as the activation energy (in kJ/mol).

Kc_dH

The temperature scaling constant of Kc (in kJ/mol).

Ko_dH

The temperature scaling constant of Ko (in kJ/mol).

S_dH

The temperature scaling constant of Sc/o (in kJ/mol).

name

An identifier ("name") of the scale instance.

Value

A new 'DHScale' instance.

Examples

new_DHScale(40.1, 38.8, 26.7, -27.4, name = "Form Ia, cyanobacteria")
new_DHScale(40.1, 38.8, 26.7, -27.4, name = "Form Ia, cyanobacteria")

Constructor for the S3 class 'enzyme'.

Description

The class 'enzyme' is designed to encapsulate the information necessary to describe a particular measured enzyme's response to CO2, O2, and temperature. All of Rubisco's key kinetic parameters are used as inputs, along with the temperature at which they were measured. In combination with a DHScale-class object, an enzyme-class object can be used to create a function that models a Rubisco enzyme's carbon output with regards to temperature and CO2/O2 concentration.

Usage

new_enzyme(
  kcat_val,
  Kc_val,
  Ko_val,
  S_val,
  temp = 25,
  kcat_T = NULL,
  Kc_T = NULL,
  Ko_T = NULL,
  S_T = NULL,
  T_max = 55,
  PGS = NA,
  name = ""
)
new_enzyme(
  kcat_val,
  Kc_val,
  Ko_val,
  S_val,
  temp = 25,
  kcat_T = NULL,
  Kc_T = NULL,
  Ko_T = NULL,
  S_T = NULL,
  T_max = 55,
  PGS = NA,
  name = ""
)

Arguments

kcat_val

The measured value of kcat for the Rubisco carboxylase reaction (in s^-1). kcat represents the number of reactions per second a single active site can perform. The temperature at which it was measured can be found in kcat_T (if present) or temp.

Kc_val

The measured value of Kc (in uM). Kc is the Michaelis constant of Rubisco for CO2, or its affinity for CO2. The temperature at which it was measured can be found in Kc_T (if present) or temp.

Ko_val

The measured value of Ko (in uM). Ko is the Michaelis constant of Rubisco for O2, or its affinity for O2. The temperature at which it was measured can be found in Ko_T (if present) or temp.

S_val

The measured value of Sc/o (unitless). Sc/o is the specificity of Rubisco for CO2 versus O2. The temperature at which it was measured can be found in S_T (if present) or temp.

temp

The common temperature (in celsius) at which the kinetic constants are measured. Can be overridden by the more specialized arguments kcat_T, Kc_T, Ko_T, and S_T. If NULL these 4 arguments are used to specify temperatures.

kcat_T

The temperature (in celsius) at which kcat_val is measured.

Kc_T

The temperature (in celsius) at which Kc_val is measured.

Ko_T

The temperature (in celsius) at which Ko_val is measured.

S_T

The temperature (in celsius) at which S_val is measured.

T_max

The upper thermal limit (in celsius) of the enzyme.

PGS

The default phosphoglycolate salvage (PGS) pathway of the enzyme. Can be NA.

name

An identifier ("name") of the enzyme instance.

Value

A new enzyme instance.

Examples

new_enzyme(6.6, 144, 394, 10.8, name="pro")
new_enzyme(6.6, 144, 394, 10.8, name="pro")

Permute the independent variables in a 4D grid created by make_4D_grid().

Description

In the new grid, every element in the list is a permuted array. Moreover, the order of the independent variables in the list is also permuted.

Usage

permute_4D_grid(grid, perm)
permute_4D_grid(grid, perm)

Arguments

grid

The original grid to be permuted.

perm

the subscript permutation vector, a permutation of the integers c(1, 2, 3).

Value

A permuted 4D grid

Examples

f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
g2 <- permute_4D_grid(g1, c(2,3,1))
g2
f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
g2 <- permute_4D_grid(g1, c(2,3,1))
g2

Create a plot of the dependent variable in a 3D slice

Description

Create a plot of the dependent variable (4-th index) in a 3D slice (created using, e.g., slice_4D_grid()) against its two non-constant independent dimensions. In the plot, the dependent variable is represented by contours and false color, while the two independent variables form the horizontal and the vertical axes.

Usage

plot_slice_3D(
  grid_slice,
  contours = NULL,
  cmin = NULL,
  cmax = NULL,
  dims = NULL,
  colors = "default",
  NA_color = "grey",
  contour_col = "black",
  xlabel = "",
  ylabel = "",
  clabel = c("Carbon", "(C/s)"),
  lwd = 2,
  ...
)
plot_slice_3D(
  grid_slice,
  contours = NULL,
  cmin = NULL,
  cmax = NULL,
  dims = NULL,
  colors = "default",
  NA_color = "grey",
  contour_col = "black",
  xlabel = "",
  ylabel = "",
  clabel = c("Carbon", "(C/s)"),
  lwd = 2,
  ...
)

Arguments

grid_slice

The 3D slice to be plotted

contours

The values of dependent variable at which the contours are drawn, as a single numeric vector. If NULL no contours are produced.

cmin

The lower bound for the color mapping. If NULL defaults to the minimum value of the contour/color variable.

cmax

The upper bound for the color mapping. If NULL defaults to the maximum value of the contour/color variable.

dims

Length 3 integer vector, e.g., c(1,2,4), that specify the horizontal, vertical, and contour/color variables of the plot. If NULL, the function will attempt to automatically determine horizontal coordinate and the vertical coordinate, while the last element of the slice is treated as the dependent variable. Note that the horizontal coordinate should ALWAYS correspond the first index of the two-dimension arrays, and the vertical coordinates should ALWAYS be the second index. (Use transpose_3D_slice() to modify which variables are horizontal/vertical)

colors

The vector of colors used for rendering the false color tiles of the plot. If the string "default" is supplied instead, the default blue-white-red color scale is used, where the color is bluer the more negative the dependent variable, redder the more positive the dependent variable, and which white is anchored at 0.

NA_color

The color used to represent undefined or out-of-range values

contour_col

The color for the contour lines

xlabel

The label for the horizontal axis

ylabel

The label for the vertical axis

clabel

The label for the color bar. Defaults to c("Carbon", "(C/s)")

lwd

The linewidth of contour lines

...

Addtional arguments are passed to plot3D::image2D(), which is the "bottom" plot created by this function (to be overlaid by the contour plot)

Value

No explicit return (plot generated as side effect)

Examples

# create 3D slices
f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, seq(1, 3, 0.1), seq(-2, 2, 0.2), seq(4, 10, 0.2))
s1 <- slice_4D_grid(g1, 2, 2)
# plot the 3D slice (if tcltk is available)
has_tk <- capabilities("tcltk") && (
  !grepl("darwin", R.version$os, ignore.case = TRUE) ||
  capabilities("X11")
)
if (has_tk) {
  plot_slice_3D(s1, contours=seq(5, 25, 2.5), dims=c(1, 3, 4))
}
# create 3D slices
f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, seq(1, 3, 0.1), seq(-2, 2, 0.2), seq(4, 10, 0.2))
s1 <- slice_4D_grid(g1, 2, 2)
# plot the 3D slice (if tcltk is available)
has_tk <- capabilities("tcltk") && (
  !grepl("darwin", R.version$os, ignore.case = TRUE) ||
  capabilities("X11")
)
if (has_tk) {
  plot_slice_3D(s1, contours=seq(5, 25, 2.5), dims=c(1, 3, 4))
}

Customized print for S3 class 'DHScale'

Description

Customized print for S3 class 'DHScale'

Usage

## S3 method for class 'DHScale'
print(x, unicode = TRUE, ...)
## S3 method for class 'DHScale'
print(x, unicode = TRUE, ...)

Arguments

x

the 'DHScale' instance to be printed.

unicode

whether to print using unicode characters.

...

the remaining arguments are ignored.

Value

No explicit return. Information of the 'DHScale' instance is printed to stdout.

Examples

ia_cyano_DHScale <- new_DHScale(40.1, 38.8, 26.7, -27.4, name = "Form Ia, cyanobacteria")
print(ia_cyano_DHScale) # printout DHScale info

ia_cyano_DHScale <- new_DHScale(40.1, 38.8, 26.7, -27.4, name = "Form Ia, cyanobacteria")
print(ia_cyano_DHScale) # printout DHScale info

Customized print for S3 class 'enzyme'

Description

Customized print for S3 class 'enzyme'

Usage

## S3 method for class 'enzyme'
print(x, unicode = TRUE, ...)
## S3 method for class 'enzyme'
print(x, unicode = TRUE, ...)

Arguments

x

the 'enzyme' instance to be printed.

unicode

whether to print using unicode characters.

...

the remaining arguments are ignored.

Value

No explicit return. Information of the enzyme instance is printed to stdout.

Examples

pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro") # new instance
print(pro_enzyme) # print out enzyme info
pro_enzyme <- new_enzyme(6.6, 144, 394, 10.8, name="pro") # new instance
print(pro_enzyme) # print out enzyme info

References table

Description

A table containing a list of the citation information and pmid/doi numbers to help with citing data from the package.

Usage

refs_to_citation
refs_to_citation

Format

'refs_to_citation'

A data frame with 150 rows and 3 columns:

short_ref: Short reference
pmid_or_doi: The pmid number or the doi
citation: Full citation

Source

compiled by the authors

Comprehensive Rubisco kinetics data

Description

A dataset of Rubisco kinetics data compiled from the literature. Includes all four major kinetic parameters (kcat, Kc, Ko, and Sc/o), along with ancillary information about the measurements and the studies.

Usage

Rubisco_25C
Rubisco_25C

Format

'Rubisco_25C'

A data frame with 1454 rows and 30 columns:

identifier: Unique entry ID
genus: Genus
species: Species
subspecies: Subspecies, variant, or cultivar
mutant: Species
mutant_details: Species
primary: Species
heterologous_expression: Species
kcat_val: Value of kcat (/sec)
kcat_T: kcat measurement temperature ( $^\circ$ C)
kcat_pH: kcat measurement pH
Kc_val: Value of Kc ( $\mu$ M)
Kc_T: Kc measurement temperature ( $^\circ$ C)
Kc_pH: Kc measurement pH
Ko_val: Value of Ko ( $\mu$ M)
Ko_T: Ko measurement temperature ( $^\circ$ C)
Ko_pH: Ko measurement pH
S_val: Value of Sc/o
S_T: Specificity measurement temperature ( $^\circ$ C)
S_pH: Specificity measurement pH
temp: Kinetics measurement temperature ( $^\circ$ C)
pH: Kinetics measurement pH
pKa: pKa used to determine CO2 concentration for Kc, Ko, and specificity calculations
form: Form of Rubisco
PGS: Recommended phosphoglycolate salvage pathway
taxonomy: Taxonomy
group: Broader phylogenetic group
note: Note
short_ref: Short reference
Year: Study year

Source

compiled by the authors from a literature search

Abridged Rubisco kinetics data

Description

A subset of data from the comprehensive Rubisco kinetics table also included in the package, plus some composite data and median Rubisco kinetics as described in sections 5.1 and 5.2 of the Creating Custom Enzymes vignette.

Usage

Rubisco_abridged
Rubisco_abridged

Format

'Rubisco_abridged'

A data frame with 294 rows and 19 columns:

identifier: Unique entry ID
genus: Genus
species: Species
subspecies: Subspecies, variant, or cultivar
kcat_val: Value of kcat (/sec)
kcat_T: kcat measurement temperature ( $^\circ$ C)
Kc_val: Value of Kc ( $\mu$ M)
Kc_T: kcat measurement temperature ( $^\circ$ C)
Ko_val: Value of Ko ( $\mu$ M)
Ko_T: kcat measurement temperature ( $^\circ$ C)
S_val: Value of Sc/o
S_T: Specificity measurement temperature ( $^\circ$ C)
temp: Kinetics measurement temperature ( $^\circ$ C)
form: Form of Rubisco
PGS: Recommended phosphoglycolate salvage pathway
taxonomy: Taxonomy
group: Broader phylogenetic group
note: Note
short_ref: Short reference

Source

compiled by the authors from a literature search

Rubisco alias table

Description

A table containing a list of some of the alternative names for species contained within the datasets. This includes both common names (i.e., "wheat") and former names that are no longer in use (i.e., "Chenopodium rubra", which has become Oxybasis rubra).

Usage

Rubisco_aliases
Rubisco_aliases

Format

'Rubisco_aliases'

A data frame with 79 rows and 4 columns:

alternate_name: Alternate, user-input name
genus_database: Genus name in database
species_database: Species name in database
subspecies_database: Subspecies name in database

Source

compiled by the authors

Search for data of Rubisco enzyme alias from the database

Description

Data is pulled either from the abridged table or the comprehensive table, both of which are included in this package

Usage

search_alias(string, data = NULL, match = "complete")
search_alias(string, data = NULL, match = "complete")

Arguments

string

a string to search for in the database

data

the source data to search from. Can be "abridged", "comprehensive", or NULL. If NULL, the search will proceed from abridged to comprehensive until a match is found

match

whether to require complete match. Can be "complete" or "partial"

Value

a dataframe containing matching entries.

Search for temperature dependence DHScale from the database

Description

Data is pulled either from the averaged table or the abridged table, both of which are included in this package

Usage

search_DHScale(string, level = NULL, data = NULL, match = "complete")
search_DHScale(string, level = NULL, data = NULL, match = "complete")

Arguments

string

a string to search for in the database

level

the level at which to search. Can be "species", "genus", "taxonomy", "form", "group", or NULL. If NULL, the search will proceed from genus to species to taxonomy to form to group until a match is found. Note that on the averaged table the genus is always "Average," while the species generally agrees with "form," except for the overall average for which the specie is defined to be "Rubisco"

data

the source data to search from. Can be "averaged", "abridged", or NULL. If NULL, the search will proceed from abridged to averaged until a match is found

match

whether to require complete match. Can be "complete" or "partial"

Value

a dataframe containing matching entries.

Examples

Aegilops <- search_DHScale("Aegilops", level="genus") # complete genus search
print(Aegilops) # printout search results
Aegilops <- search_DHScale("Aegilops", level="genus") # complete genus search
print(Aegilops) # printout search results

Search for data of Rubisco enzyme from the database

Description

Data is pulled either from the abridged table or the comprehensive table, both of which are included in this package

Usage

search_enzyme(string, level = NULL, data = NULL, match = "complete")
search_enzyme(string, level = NULL, data = NULL, match = "complete")

Arguments

string

a string to search for in the database (case insensitive)

level

the taxonomic level to search. Can be "alias", "genus", "species", "form", "taxonomy", "group" or NULL. If NULL, the search will proceed from alias to genus to species to taxonomy to form to group until a match is found

data

the source data to search from. Can be "abridged", "comprehensive", or NULL. If NULL, the search will proceed from abridged to comprehensive until a match is found

match

whether to require complete match. Can be "complete" or "partial"

Value

a dataframe containing matching entries.

Examples

Aegilops <- search_enzyme("Aegilops", level="genus") # complete genus search
print(Aegilops) # printout search results
Aegilops <- search_enzyme("Aegilops", level="genus") # complete genus search
print(Aegilops) # printout search results

Create a 3D slice of 4D grid created by make_4D_grid().

Description

The resulting slice remains a list of 4 elements, but each element except the dimension being sliced will be a 2-dimensional array. For the dimension being sliced, the element will be a scalar showing the constant value of the sliced coordinate.

Usage

slice_4D_grid(grid, dim, val, approx = TRUE, tol = 0.001)
slice_4D_grid(grid, dim, val, approx = TRUE, tol = 0.001)

Arguments

grid

The original grid to be sliced.

dim

The dimension to be sliced. Its corresponding coordinates will take constant values in the resulting slice.

val

The constant value that the dim-th coordinates will take in the resulting slice.

approx

Whether approximate value matching is allowed.

tol

the tolerance for approximate value matching.

Value

A 3D slice of the supplied 3D grid.

Examples

f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
s1 <- slice_4D_grid(g1, 2, 2)
s1
f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
s1 <- slice_4D_grid(g1, 2, 2)
s1

Abridged Delta H temperature scaling data

Description

A dataset of Rubisco temperature dependence data compiled from a literature search.

Usage

temp_dep_abridged
temp_dep_abridged

Format

'temp_dep_abridged'

A data frame with 182 rows and 13 columns:

identifier: Unique entry ID
genus: Genus
species: Species
subspecies: Subspecies, variant, or cultivar
kcat_dH: kcat temperature dependence parameter (kJ/mol)
Kc_dH: Kc temperature dependence parameter (kJ/mol)
Ko_dH: Ko temperature dependence parameter (kJ/mol)
S_dH: S temperature dependence parameter (kJ/mol)
form: Form of Rubisco
taxonomy: Taxonomy
group: Broader phylogenetic group
note: Note
short_ref: Short reference

Source

compiled by the authors from a literature search

Averaged Delta H temperature scaling data

Description

A dataset of average Rubisco temperature scaling data per taxonomic group and form, as described in Section 5.3 of the "Designing Custom Enzymes" vignette.

Usage

temp_dep_averaged
temp_dep_averaged

Format

'temp_dep_averaged'

A data frame with 9 rows and 13 columns:

identifier: Unique entry ID
genus: Genus
species: Species
subspecies: Subspecies, variant, or cultivar
kcat_dH: kcat temperature dependence parameter (kJ/mol)
Kc_dH: Kc temperature dependence parameter (kJ/mol)
Ko_dH: Ko temperature dependence parameter (kJ/mol)
S_dH: S temperature dependence parameter (kJ/mol)
form: Form of Rubisco
taxonomy: Taxonomy
group: Broader phylogenetic group
note: Note
short_ref: Short reference

Source

compiled by the authors, mostly from Galmés et al. 2016

Transpose the non-constant independent variables in a 3D grid created by slice_4D_grid().

Description

In the new slice, every non-constant element in the list is transposed. Moreover, the order of the 2 non-constant independent variables in the list is also swapped.

Usage

transpose_3D_slice(slice)
transpose_3D_slice(slice)

Arguments

slice

The original slice to be transposed.

Value

A transposed 3D slice.

Examples

f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
s1 <- slice_4D_grid(g1, 2, 2)
s2 <- transpose_3D_slice(s1)
s2
f <- function(x, y, z) { x + y * z }
g1 <- make_4D_grid(f, 1:3, c(-2,2), seq(4, 10, 2))
s1 <- slice_4D_grid(g1, 2, 2)
s2 <- transpose_3D_slice(s1)
s2

Package 'rbcmodel'

Help Index

Validator for the S3 class 'DHScale'

Description

Usage

Arguments

Value

Examples

Validator for the S3 class 'enzyme'

Description

Usage

Arguments

Value

Examples

Cite data collected in the package.

Description

Usage

Arguments

Value

Examples

Creates a combined model that compares two different enzymes

Description

Usage

Arguments

Value

Examples

Function factory to model an enzyme's carbon output

Description

Usage

Arguments

Value

Examples

Create an DHScale instance by looking up the relevant data in database

Description

Usage

Arguments

Value

Examples

Create an Enzyme instance by looking up the relevant data in database

Description

Usage

Arguments

Value

Examples

Function factory for calculating temperature-dependent kinetics

Description

Usage

Arguments

Value

Examples

Return the last non-NA value from a vector.

Description

Usage

Arguments

Value

Construct a 4D grid

Description

Usage

Arguments

Details

Value

Examples

Compute the mean of a vector if the vector is numerical or logical. Otherwise, return the common value if all entries take the same value, or NA in any other cases.

Description

Usage

Arguments

Value

Compute the median of a vector if the vector is numerical or logical. Otherwise, return the common value if all entries take the same value, or NA in any other cases.

Description

Usage

Arguments

Value

Merge (combine) several entries from a data.frame.

Description

Usage

Arguments

Value

Examples

Create a new instance of S3 class 'DHScale', starting from a previous instance

Description