Raman Lab, IIT Madras (RamanLab)

Raman Lab, IIT Madras

RamanLab

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Location:India

Home Page:https://home.iitm.ac.in/kraman/lab/

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Raman Lab, IIT Madras's repositories

metquest

Enumerating biosynthetic pathways in metabolic networks

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metquest2

Enumerating biosynthetic pathways in metabolic networks

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minMicrobiome

Code for generation of Minimal Microbiomes using a constraint-based approach

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fbc_curation_matlab

fbc_curation contains MATLAB/COBRA helpers for reproducibility of fbc models.

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SysBioBook-codes

This repository contains companion codes, developed for use with the CRC Press Book "An Introduction to Systems Biology" by Karthik Raman

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minRerouting

Package to understand the compensatory mechanisms performing similar metabolic functions through analysis of Synthetic Lethals

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NBDriver

Codes required to reproduce the results from the NBDriver manuscript

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picCASO

picCASO is a MATLAB package to simulate the dynamics of spatial patterning of a microbial consortium on a 2-dimensional surface.

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cobratoolbox

The COnstraint-Based Reconstruction and Analysis Toolbox

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FastSL-py

Python implementation of FastSL

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COWAVE

COVID-19 Wave dataset, based on WHO dataset and labelled as waves based on a new definition.

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iCOMIC_doc

iCOMIC is an open source genomic data analysis tool characterized by a user friendly GUI. It integrates many independent core workflows for Whole Genome/Exome Sequencing and RNA-Seq data analysis.

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Metabolic-Modelling-of-the-ISS-Microbiome

Created on 2020-11-04 to maintain versions of the project from this time point onwards.

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NetGenes

dummy repo for redirects

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NonLinearAdptation

This repository contains the necessary MATLAB and SIMULINK files for the simulation studies done in the manuscript titled, 'Design Principles for Global, Perfect Adaptation]{Design Principles for Perfect Adaptation in Biological Networks with Nonlinear Dynamics'

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Panera

MATLAB repository to construct pan-genus metabolic models (PGMM)

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structure-based-drug-design

Structure based Drug Design using Distance prediction with Graph Neural Networks - Dual Degree Project

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