Table 1 Genetic Algorithm for finding mutation in apoptotic network.

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.