From cacf2adc38047e804ba57a888aba7df62174284c Mon Sep 17 00:00:00 2001 From: Eduard Kerkhoven Date: Fri, 30 Oct 2026 13:13:37 +0100 Subject: [PATCH] Remove the references to the retired Bayesian kcat tuning --- src/kcat_tuning/evotune/evotuneScore.m | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/kcat_tuning/evotune/evotuneScore.m b/src/kcat_tuning/evotune/evotuneScore.m index 71305957..189c4ac9 100644 --- a/src/kcat_tuning/evotune/evotuneScore.m +++ b/src/kcat_tuning/evotune/evotuneScore.m @@ -5,9 +5,7 @@ % tunePriorPenaltyWeight to score one ecModel (with a given set of kcats % already applied via applyKcatConstraints) against the experimental % growth and flux data. Fuses what geckopy splits into simulate.py (the -% FBA half) and distance.py (the RMSE half) into one function, matching -% this module's own predecessor abc_max.m -- there is no MATLAB test -% suite here to benefit from Python's testability-driven split. +% FBA half) and distance.py (the RMSE half) into one function. % % Parameters % ---------- @@ -114,7 +112,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [rmse, rmseList] = simulateCondition(ecModel,data,constrain,rxn2block,modelAdapter) % One condition's FBA simulation + carbon-weighted RMSE against measured -% data. Ported from abc_max.m's rmsecal. +% data. rmseList = zeros(length(data.conds),1);