hillCurve: 4-Parameter Hill Equation for Stimuli-Response Curves

In bhklab/PharmacoGx: Analysis of Large-Scale Pharmacogenomic Data

4-Parameter Hill Equation for Stimuli-Response Curves

Description

Sigmoidal function which fits well to many stimuli-response associations observed in biology and pharmacology. In the context of PharmacoGx we are using it to model treatment-response assocations in cancer cell lines.

Usage

hillCurve(dose, HS, EC50, E_inf, E_ninf)

Arguments

dose	numeric() A vector of log10(dose) values (or equivalent for the stimuli being modelleled).
HS	numeric(1) Hill coefficient (n) which defines the slope of the dose-response curve at the mid-point. This parameter describes the degree of sigmoidicity of the Hill curve. HS = 1 corresponds to the rectangular hyperbola in dose-response space.
EC50	numeric(1) The dose required to produce 50% of the theoretically maximal response in the system, E_inf. Should be in the same units as dose!
E_inf	numeric(1) Theoretical maximal response (minimal viability) in the system as a proportion in the range \[0, 1\]. Note that since we are predicting viability (percent of cells alive after treatment) instead of response, this value should be low (i.e., more cell killing).
E_ninf	numeric(1) Theoretical minimum response (basal response). Defaults to 1, which should be the case for most viability experiments since we expect no cell killing to occur prior to applying a treatment.

Value

numeric() Vector of predicted viabilities for the Hill curve defined by EC50, E_inf, E_ninf and HS for each supplied value of dose.

Author(s)

Feifei Li Petr Smirnov Christopher Eeles

References

Gesztelyi, R., Zsuga, J., Kemeny-Beke, A., Varga, B., Juhasz, B., & Tosaki, A. (2012). The Hill equation and the origin of quantitative pharmacology. Archive for History of Exact Sciences, 66(4), 427–438. https://doi.org/10.1007/s00407-012-0098-5

Motulsky, H., & Christopoulos, A. (2004). Fitting models to biological data using linear and nonlinear regression: A practical guide to curve fitting. Oxford University Press. See Chapter 41.

Examples

(viability <- hillCurve(
  dose=c(0.1, 0.01, 0.001),
  HS=1.1,
  EC50=0.01,
  E_ninf=1,
  E_inf=0
))

bhklab/PharmacoGx documentation built on April 18, 2024, 3:13 a.m.