Systems and Synthetic Biology's repositories
causalpath
A project for exploring differentially active signaling paths related to proteomics datasets
vivarium-cell
A collection of models for simulating cells with Vivarium.
scNET
jupyther notebook comparing reproducibility of single-cell network inference algorithms
AMICI
Advanced Multilanguage Interface to CVODES and IDAS
mechanismEncoder
Developing patient-specific phosphoproteomic models using mechanistic autoencoders
wc_lang
Language for describing whole-cell models as reaction networks
trDesign
trRosetta for protein design
MusiteDeep_web
This repository contains the stand-alone tool for MusiteDeep server
rustfilm
Biofilm simulation in rust
parPE
Parameter estimation for dynamical models using high-performance computing, batch and mini-batch optimizers, and dynamic load balancing.
metage2metabo
From annotated genomes to metabolic screening in large scale microbiotas
RAVEN
The RAVEN Toolbox for genome scale model reconstruction, curation and analysis.
micom
Python package to study microbial communities using metabolic modeling.
modelseed-escher
Escher Viewer for ModelSEED python library
RVAgene
Recurrent Variational Auto gene encoder
AutoCD
Automated synthetic microbial Community Design
songbird
Vanilla regression methods for microbiome differential abundance analysis
StoichiometricBalance
Given 1) a protein interaction network, with interfaces resolved and 2) Copy numbers for some of the proteins, Quantify degree of stoichiometric balance in protein copy numbers
EcoliKineticBenchmark
This repository contains code to replicate study-benchmark of published E. coli kinetic metabolic models
decagon
Graph convolutional neural network for multirelational link prediction
HuRI_paper
Code for the analyses in the human reference interactome paper.
pyvipr
Jupyter widget for the dynamic and static visualizations of systems biology models written in PySB, BNGL, and SBML
immune-svm
Using a machine-learning algorithm to understand adaptive immune system dynamics
MEP-SidersPond
Fortran code used for MEP model to describe microbial biogeochemistry in Siders Pond, MA
Zhang_and_Petersen_et_al_2019
Scripts connected to the scientific publication: Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models
MusiteDeep
MusiteDeep provides a deep-learning method for general and kinase-specific phosphorylation site prediction. It is implemented by deep learning library Keras and Theano backend (the Keras2.0 and Tensorflow backend implementation were also provided under folder MusiteDeep_Keras2.0). At present, MusiteDeep only provides prediction of human phosphorylation sites; however, it also provides customized model training that enables users to train other PTM prediction models by using their own training data sets based on either CPU or GPU.
WebGestaltR
R package for WebGestalt