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Systems and Synthetic Biology's Projects

porter5 icon porter5

Fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes

prob icon prob

pseudotemporal progression-based Bayesian method for inferring GRNs from cross-sectional clinical transcriptomic data

proteinnet icon proteinnet

Standardized data set for machine learning of protein structure

provis icon provis

Official code repository of "BERTology Meets Biology: Interpreting Attention in Protein Language Models."

pyabc icon pyabc

distributed, likelihood-free inference

pybnf icon pybnf

An application for fitting systems biology models using metaheuristic algorithms

pyfba icon pyfba

A python implementation of flux balance analysis to model microbial metabolism

pyme icon pyme

Python Solver for The Chemical Master Equation

pypesto icon pypesto

python Parameter EStimation TOolbox

pysb icon pysb

Python framework for Systems Biology modeling

pysster icon pysster

pysster: Learning Sequence And Structure Motifs In Biological Sequences Using Convolutional Neural Networks

pyvipr icon pyvipr

Jupyter widget for the dynamic and static visualizations of systems biology models written in PySB, BNGL, and SBML

r4kappa icon r4kappa

Set of packages designed to bridge kappa language modelling and bioinformatics.

raven icon raven

The RAVEN Toolbox for genome scale model reconstruction, curation and analysis.

rc_e_coli icon rc_e_coli

MATLAB code for resource competition and growth modelling in E.coli

rebop icon rebop

Fast stochastic simulator for chemical reaction networks

recon3d icon recon3d

A 3-Dimensional View of Human Metabolism and Disease

redm icon redm

Applications of Empirical Dynamic Modeling from Time Series

resourceawarewholecellmodel icon resourceawarewholecellmodel

Here, we model a two-strain consortium, with one strain expressing an endohydrolase and a second strain expressing an exohydrolase, for cooperative degradation of a complex substrate.

rgn icon rgn

Recurrent Geometric Networks for end-to-end differentiable learning of protein structure

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