Skip to content

Remove the references to the retired Bayesian kcat tuning #16

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
edkerk merged 1 commit into from
Oct 30, 2026
Merged

Remove the references to the retired Bayesian kcat tuning #16

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 2 additions & 4 deletions src/kcat_tuning/evotune/evotuneScore.m
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
% ----------
Expand Down Expand Up @@ -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);

Expand Down
Loading