Variance-Aware Michaelis-Menten Estimation and Inference

Variance-aware Michaelis-Menten estimation, model screening, grouped enzyme-kinetic analyses, and clustered repeated-measurement workflows. The package implements profile-score estimators under working variance functions, together with a lightweight cluster-aware working-covariance extension, Wald and bootstrap confidence intervals, prediction utilities, and simulation helpers. Related methodology is discussed by Kim and Ma (2012) , Kim (2023) , and Ma and Genton (2010) .

inferMM

inferMM provides variance-aware Michaelis-Menten estimation and inference for enzyme-kinetic data with concentration-dependent heteroscedasticity.

The package is designed around a compact workflow:

• fit one curve with fit_mm()
• compare working variance models with screen_mm()
• repeat that workflow across many enzymes with group_mm()
• fit repeated or clustered assays with cluster_mm()
• summarize and plot fitted objects with standard S3 methods plus report_mm()

Installation

# install.packages("remotes")
remotes::install_github("mijeong-kim/inferMM")

Bundled data

The package ships with two demo datasets.

• sdl_demo: self-driving laboratory Michaelis-Menten panel
• alves_demo: filtered soil exoenzyme kinetics panel from Alves et al. (2021)

library(inferMM)
data(sdl_demo)
data(alves_demo)

Minimal example

library(inferMM)

one_curve <- subset(sdl_demo, enzyme == "1111")

fit <- fit_mm(
  x = one_curve$s_uM,
  y = one_curve$v_uM_per_min,
  variance = "sqrt"
)

summary(fit)
confint(fit)

Screening working variance models

screen <- screen_mm(
  x = one_curve$s_uM,
  y = one_curve$v_uM_per_min,
  quiet = TRUE
)

screen$table[, c("model", "selected_model", "quasi_aic", "quasi_bic", "rmse")]

Grouped analyses

grouped <- group_mm(
  data = sdl_demo,
  s = "s_uM",
  v = "v_uM_per_min",
  groups = "enzyme",
  variance_models = c("constant", "log", "sqrt", "cuberoot"),
  quiet = TRUE
)

grouped$comparison$best_by_group[
  , c("group_label", "model", "selected_model", "quasi_aic", "quasi_bic", "rmse")
]

Clustered analyses

cluster_fit <- cluster_mm(
  data = subset(alves_demo, enzyme == "BG"),
  s = "substrate_conc",
  v = "activity",
  cluster = "core",
  variance = "sqrt"
)

summary(cluster_fit)
confint(cluster_fit)

For sparse clustered fits, default interval reporting is intentionally cautious: printed summaries may suppress intervals, and bootstrap intervals should be read as sensitivity analyses rather than routine default inference.

Reporting and plotting

report_mm(fit, interval_type = "confidence")
plot(grouped, interval_type = "confidence")
predict(fit, newdata = seq(0, 80, length.out = 6), interval = "prediction")

Repository contents

• R/: package source code
• man/: function documentation
• data/: bundled .rda demo data
• inst/extdata/: raw CSV mirrors of the demo data
• vignettes/: end-to-end workflow vignette
• tests/: unit tests

For manuscript-oriented simulation code and saved paper outputs, see the separate repository inferMM-cils-repro.