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Table 1 Genetic Algorithm for finding mutation in apoptotic network.

From: Computational cell fate modelling for discovery of rewiring in apoptotic network for enhanced cancer drug sensitivity

INPUT: Objective function, Network Topology(NT) in the form of Adjacency vector of size n × n, reaction rate constants vector of size n × n, maximum number of generations Max num gen for the algorithm, biological data measurements
OUTPUT: A vector consisting topology of best uncovered network, score vector S, for such uncovered networks
1. NT0 topology
2. RC0 rate constants
3. Max_num_itr total number of iteration allowed
4. S0 0
5. Derive Population P (randomly generated networks)
6. For max_ num_gen times do:
7. Derive rate equations for each network in P
8. Solve each of the Rate equations
9. Derive numerical solutions (time series data)
10. Compare the simulation results with Yaffe's data using DTW Objective function and calculate the Score
11. Select 50 best score
12. Perform crossover among best selected networks to formulate next generation, total of 100 networks again. The crossover points are selected based on random numbers. The networks to be crossed over are selected randomly.
13. Perform mutation for each bit with the probability of 0.01, for each of the hundred networks.
14. Set new population P = mutated network from step 13
15. Check for the convergence.
16. If network not converged, go to step 5
7. Output solution set.