Cell Fate, Differentiation & Attractor Landscapes — Hands-on

Understand how stable cell fates, differentiation trajectories and plasticity emerge from underlying gene regulatory and signaling networks. This module covers Boolean and ODE-based dynamical models of GRNs, attractors and basins of attraction, bifurcation analysis, stochasticity, and visualization of Waddington-like landscapes. You will implement in-silico perturbations, simulate differentiation/reprogramming scenarios, and generate publication-ready figures using Python, R and specialized tools.

Cell Fate, Differentiation & Attractor Landscapes

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Session Index

Session 1 — GRNs, Dynamical Systems & Cell Fate Concepts Session 2 — Boolean Dynamics, Attractors & Basins Session 3 — ODE/Stochastic Models & Waddington Landscapes Session 4 — Mini Capstone: In-Silico Differentiation & Reprogramming

Session 1

Fee: Rs 8800

GRNs, Dynamical Systems & Cell Fate Concepts

Cell fate & gene regulatory networks (GRNs)

cell types & lineage trees GRNs as dynamical systems feedback, multistability, hysteresis

Dynamical systems foundations

state space & trajectories fixed points, cycles, attractors basins of attraction (intuitive view)

Toolchain & model formats

SBML-qual / SBML ODE CellCollective / GINsim (overview) Python/R for GRN dynamics

Session 2

Fee: Rs 11800

Boolean Dynamics, Attractors & Basins

Logical/Boolean modeling of GRNs

Boolean rules & update schemes synchronous vs asynchronous state transition graphs

Attractor analysis & cell types

fixed-point attractors cyclic attractors linking attractors to phenotypes

Tools & practical workflows

PyBoolNet / BoolNet GINsim / CellCollective basin size & robustness metrics

Session 3

Fee: Rs 14800

ODE/Stochastic Models & Waddington Landscapes

Continuous dynamics & fate transitions

ODE GRN models bifurcations & saddle-node transitions noise-driven switching (overview)

Energy/Waddington landscape concepts

potential landscapes (intuitive) stable vs unstable manifolds visualizing cell state manifolds

Toolchain & visualization

Python (SciPy, PyDSTool style workflows) MATLAB / XPPAUT (bifurcation overview) 2D/3D landscape and phase-plot figures

Session 4

Fee: Rs 18800

Mini Capstone: In-Silico Differentiation & Reprogramming

Case study: modeling a simple differentiation system

Theory + Practical

In-silico perturbations & interventions

knockout/overexpression in GRNs changing landscape shape reprogramming & transdifferentiation scenarios

Deliverables

PDF/HTML report with attractor/landscape figures Python/R notebooks or model files environment.yml / requirements.txt

PDF

Cell Fate, Differentiation & Attractor Landscapes Training | GRNs, Boolean/ODE Dynamics & Waddington Landscapes

Cell Fate, Differentiation & Attractor Landscapes — Hands-on