Gene Regulatory Networks & Transcriptional Control — Hands-on

Learn how to construct, infer, and interpret gene regulatory networks (GRNs) that explain how transcription factors, enhancers, and regulatory modules control gene expression programs. You will work with omics and ChIP style data, apply GRN inference algorithms, and explore dynamical behaviour and perturbation effects using practical workflows in R, Python, and Cytoscape.

Gene Regulatory Networks & Transcriptional Control

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Session Index

Session 1 — GRN Concepts & Data Sources Session 2 — GRN Inference Algorithms Session 3 — Dynamical GRN Models & Control Session 4 — Mini Capstone: Build & Analyse a GRN

Session 1

Fee: Rs 8800

GRN Concepts & Data Sources

Concepts of gene regulatory networks and transcriptional control

transcription factors and targets enhancers and promoters motifs and modules

Data types for GRN construction and validation

bulk and single cell expression ChIP like binding datasets TF motif databases (JASPAR)

Toolchain and resources for GRNs

Cytoscape for GRN visualisation R / Bioconductor basics Python / pandas data handling

Session 2

Fee: Rs 11800

GRN Inference Algorithms

Correlation and information based GRN methods

co expression networks mutual information approaches ARACNe style workflows

Regression, tree and Bayesian based GRNs

LASSO and Elastic Net based inference tree based methods (GENIE3 style) Bayesian network preview

Practical GRN inference toolchain

R packages for GRN inference Python workflows for GRNs Cytoscape import and styling

Session 3

Fee: Rs 14800

Dynamical GRN Models & Control

Boolean and logical models of gene regulation

on off regulatory logic state transition graphs attractors and cell states

ODE style regulatory dynamics and perturbations

simple ODE GRN examples feedback loops and oscillations knockout and overexpression scenarios

Toolchain for GRN dynamics exploration

Python / SciPy for simulations R based simulation workflows visualisation of trajectories

Session 4

Fee: Rs 18800

Mini Capstone: Build & Analyse a GRN

Construct a GRN for a pathway or phenotype of interest

Theory + Practical

Infer edges, identify key regulators and simulate perturbations

regulator ranking tables subnetwork visualisation perturbation scenario summaries

Deliverables: notebooks, GRN files & report

R / Python notebook network files (SIF / GraphML) PDF/HTML GRN summary

PDF

Gene Regulatory Networks & Transcriptional Control Training | GRN Inference, Motifs, Dynamics

Gene Regulatory Networks & Transcriptional Control — Hands-on