CellCollective

Import the relevant cancer signaling network model.

Set growth factor inputs to high to simulate a tumor environment.

Run a systematic single-node knockout simulation of all kinases.

Analyze the output to find nodes that result in a 'Cell Death' state.

Verify results against the integrated Knowledge Base.

Construct a model of cytokine interaction networks.

Initialize the model with healthy state parameters.

Simulate chronic inflammation by locking pro-inflammatory nodes to 'on'.

Observe the emergence of stable cycles in the network state.

Export the dynamical data for clinical comparison.

Model the synthetic circuit's transcription factors and promoters.

Define the inhibitory relationships between the repressors.

Test the circuit stability under various environmental 'inputs'.

Refine the logic gates to ensure robustness to noise.

Download the optimized SBML for sequence assembly design.

Load the microbial metabolic regulatory network.

Identify the rate-limiting steps in the fermentation pathway.

Perform virtual overexpression of key enzymes.

Simulate the effect on non-target biomass production.

Determine the optimal genetic modifications for production efficiency.

Assign a pre-built model (e.g., T-cell differentiation) to a student group.

Instruct students to visually predict the outcome of a specific perturbation.

Have students run the simulation in CellCollective to verify their hypothesis.

Use the platform's visualization tools to show state transitions.

Grade students based on their ability to explain the resulting attractors.