Package {rbcmodel}

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)

Arguments

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)

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)

Arguments

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

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)

Arguments

Value

a new DHScale instance

Examples

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)

Arguments

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")

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

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 (^\circC)

kcat_pH

kcat measurement pH

Kc_val

Value of Kc (\muM)

Kc_T

Kc measurement temperature (^\circC)

Kc_pH

Kc measurement pH

Ko_val

Value of Ko (\muM)

Ko_T

Ko measurement temperature (^\circC)

Ko_pH

Ko measurement pH

S_val

Value of Sc/o

S_T

Specificity measurement temperature (^\circC)

S_pH

Specificity measurement pH

temp

Kinetics measurement temperature (^\circC)

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

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 (^\circC)

Kc_val

Value of Kc (\muM)

Kc_T

kcat measurement temperature (^\circC)

Ko_val

Value of Ko (\muM)

Ko_T

kcat measurement temperature (^\circC)

S_val

Value of Sc/o

S_T

Specificity measurement temperature (^\circC)

temp

Kinetics measurement temperature (^\circC)

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

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

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)

Arguments

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

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)

Arguments

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

Cite data collected in the package.

Description

Cite data collected in the package.

Usage

cite_Rbc(identifier, type = "full")

Arguments

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")

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, \DeltaH (in kJ/mol)).

Usage

kinetics_T_dep(std, delta_H, temp = 25)

Arguments

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

Return the last non-NA value from a vector.

Description

Return the last non-NA value from a vector.

Usage

last_non_NA(x)

Arguments

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"))

Arguments

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

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)

Arguments

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)

Arguments

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
)

Arguments

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

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
)

Arguments

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

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
)

Arguments

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

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 \DeltaH. 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 = "")

Arguments

Value

A new 'DHScale' instance.

Examples

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 = ""
)

Arguments

Value

A new enzyme instance.

Examples

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)

Arguments

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

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,
  ...
)

Arguments

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))
}

Customized print for S3 class 'DHScale'

Description

Customized print for S3 class 'DHScale'

Usage

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

Arguments

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

Customized print for S3 class 'enzyme'

Description

Customized print for S3 class 'enzyme'

Usage

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

Arguments

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

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

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

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")

Arguments

Value

a dataframe containing matching entries.

Examples

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

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")

Arguments

Value

a dataframe containing matching entries.

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")

Arguments

Value

a dataframe containing matching entries.

Examples

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)

Arguments

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

Abridged Delta H temperature scaling data

Description

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

Usage

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

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)

Arguments

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