amirhossein207 Goto Github PK

amirhossein207.github.io

My personal website.

autopacmen

Retrieves kcat data and adds protein allocation constraints to stoichiometric metabolic models according to the sMOMENT method

biofx_python

Code for Mastering Python for Bioinformatics (O'Reilly, 2021, ISBN 9781098100889)

brc_mh_bioinformatics

Collection of pipelines, workflows and scripts for processing SNP and gene expression arrays and NGS data

comets-toolbox

MATLAB scripts and utilities for the COMETS software for spatial DFBA

fastknock

fermentationsim

Fermentation simulator used in paper: "A study of a diauxic growth experiment using an expanded dynamic flux balance framework", Karlsen et al. 2022

gwastutorial

GWAS Tutorial for Beginners

lightmm

lightMM: a novel tool for calculating kinetic constants in the Michaelis-Menten equation from two substrate concentrations

linearalgebra

Linear Algebra for Data Science. A Textbook for Students and Practitioners

mattfa

A Matlab implementation of Thermodynamics-based Flux Analysis

mmabc

A code for the paper "Estimating kinetic constants in the Michaelis-Menten model from one enzymatic assay using Approximate Bayesian Computation"

modular_co_cultivation

This is the code accompanying my master's thesis

phenomapping

PhenoMapping is a computational framework that provides some workflows and methodologies for the understanding of mechanisms underlying phenotypes using genome-scale models (GEMs). PhenoMapping classifies the information in a GEM as organism-specific information and context-specific information. Organism-specific information includes the (i) biochemistry/metabolic functions annotated to the genes, (ii) the localization of enzymes, (iii) the intracellular transportability of metabolites, and (iv) the enzymatic irreversibilities defined/ad hoc pre-assigned directionalities. Context-specific information involves (i) the medium composition, (ii) the reaction directionalities given a set of metabolomics data, (iii) the reaction flux levels given a set of gene expression data, and (iv) the possibility of regulation of gene expression between isoenzymes given a set of gene expression data. PhenoMapping is modular and allows the independent study of these mechanisms. The PhenoMapping workflow suggests a sequence that one can follow for the study of these mechanisms and analysis and interpretation of the results. PhenoMapping was developed for the analysis of high-throughput fitness phenotypic data throughout the life cycle of the malaria parasite P. berghei, and served to curate the genome-scale model of this organism (iPbe) and generate context-specific models for the blood (iPbe-blood) and liver (iPbe-liver) stages - both of which show approximately 80% accuracy and 0.5 Matthew Correlation Coefficient (MCC) with the phenotypic data.

plinkr

Work with PLINK from R

pytfa

A Python 3 implementation of Thermodynamics-based Flux Analysis

sequentialscanner

A sequential gene scanner for both gene deletions and upregulations, based on flux balance analysis

systems_biology_projects

Codes from various Systems Biology course written in Matalb

texfba

Integration of gene expression data with TFA constraints