KinBiont.jl#

pykinbiont is a thin Python bridge over KinBiont.jl, a Julia package for model-based analysis of microbial kinetics data developed by the Pinheiro Group.

What KinBiont provides#

KinBiont implements the full analysis pipeline in Julia:

  • Preprocessing — smoothing (LOWESS, rolling average, Gaussian), blank subtraction, scattering correction, negative-value handling, stationary-phase trimming

  • Nonlinear fitting — closed-form growth models (logistic, Gompertz, Richards, Baranyi, …) via Optim.jl

  • ODE fitting — SciML-powered fitting for custom differential equation models

  • Data-Driven Differential Equations (DDDE) — sparse regression to discover ODEs from data

  • Model selection — AICc-based automatic selection across multiple candidate models

  • Clustering — k-means clustering of curve shapes with centroid extraction

Julia resources#

Resource

Link

KinBiont.jl source

https://github.com/pinheiroGroup/KinBiont.jl

Julia downloads

https://julialang.org/downloads/

PythonCall / juliacall

https://juliapy.github.io/PythonCall.jl/stable/

juliapkg

https://github.com/JuliaPy/juliapkg.py

How pykinbiont calls Julia#

pykinbiont uses juliacall to start a Julia process in the same Python session. The bridge is transparent:

  1. Python GrowthData / FitOptions / ModelSpec objects are converted to their Julia counterparts in pykinbiont._convert.

  2. Kinbiont.kinbiont_fit() or Kinbiont.preprocess() is called from Python.

  3. The Julia result structs are converted back to Python dataclasses.

For custom Python models (NLModel / ODEModel with a func argument), pykinbiont creates a Julia closure that calls back into Python via PythonCall’s pyconvert:

# Generated at runtime for each custom NLModel
(p, t) -> PythonCall.pyconvert(Vector{Float64}, pyfunc(p, t))

This means custom model functions are executed in Python on every optimizer call. For performance-critical work, define the model in Julia directly.

Accessing Julia directly#

If you need to call KinBiont.jl functions not yet exposed by pykinbiont, you can access the Julia module directly:

import pykinbiont
pykinbiont.init()          # ensure Julia is started

jl = pykinbiont.get_jl()   # returns the juliacall Main module
print(list(jl.Kinbiont.MODEL_REGISTRY.keys()))

All juliacall features (type conversions, seval, etc.) are available through get_jl().

Version compatibility#

pykinbiont

KinBiont.jl

0.2.x

≥ 1.2.0

KinBiont.jl 1.2 introduced the unified FitOptions struct and ModelSpec API that pykinbiont 0.2 relies on. Earlier KinBiont versions are not supported.