Gene Regulatory Networks GRNs and Logic Models — Hands-on

Understand how gene regulatory networks encode decision making in cells and how to model them using logical and Boolean formalisms. This module covers GRN concepts, rule-based models, state space and attractor analysis with an emphasis on practical workflows for hypothesis generation and perturbation design.

Gene Regulatory Networks GRNs & Logic Models

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Session Index

Session 1 — GRN Concepts & Representations Session 2 — Boolean & Logical Rule Based Models Session 3 — State Space, Attractors & Perturbations Session 4 — Mini Capstone: Build & Analyze a GRN Model

Session 1

Fee: Rs 8800

GRN Concepts & Representations

Biological basis of gene regulatory networks

transcription factors and targets activation vs repression regulatory motifs

GRN representations and data sources

directed signed graphs TF target databases curated vs inferred networks

From wiring diagrams to model ready structure

nodes, regulators and regulators sets signs and thresholds mapping biology to structure

Session 2

Fee: Rs 11800

Boolean & Logical Rule Based Models

Boolean models of regulation

binary states and update rules AND / OR / NOT logic synchronous vs asynchronous updates

Multi valued and logical models

multi level activity logical rules vs truth tables influence graphs

Tools and formats for logic models

GINsim / BoolNet overview SBML qual basics export and interchange

Session 3

Fee: Rs 14800

State Space, Attractors & Perturbations

State transition graphs and trajectories

state space exploration transient vs steady behavior visualizing transitions

Attractors and cell fate interpretation

fixed points and cycles attractors as phenotypes basins of attraction

Perturbation and intervention analysis

knockouts and overexpression in silico drug targeting robustness of attractors

Session 4

Fee: Rs 18800

Mini Capstone: Build & Analyze a GRN Model

Select a small GRN and encode logical rules

Theory + guided practical

Simulate dynamics and identify attractors

use of GINsim / BoolNet style tools state transition plots compare perturbation scenarios

Deliverables: model file, notebook and brief report

SBML qual / tool native format Python or R notebook PDF or HTML summary

PDF

Gene Regulatory Networks GRNs and Logic Models Training | Boolean, Logical and Attractor Based Modeling

Gene Regulatory Networks GRNs and Logic Models — Hands-on