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1901 lines (1732 sloc) 57.072 kB
index of A is 0
index of B is 1
top : A * B
ODE Model Statistics:
Number of ODEs: 2
Number of Assignments: 2
Number of Constants: 1
____
Total number of values: 5
ODEs:
d[S1]/dt = (R2 - R1) / c
d[S2]/dt = (R1 - R2) / c
Assigned Variables:
R1 = 1 * S1
R2 = 0 * S2
Constants:
c
### RESULTS
### Printing All Results
## Printing Variable Compartment time courses
## No Values.
## Printing Species time courses
#time MKKK MKKK_P MKK MKK_P MKK_PP MAPK MAPK_P MAPK_PP
0 90 10 280 10 10 280 10 10
10 80.7166 19.2834 279.659 11.6577 8.68326 279.877 11.1066 9.01599
20 71.2643 28.7357 277.407 13.8808 8.71222 280.008 11.8598 8.13187
30 61.6258 38.3742 273.231 16.6905 10.0787 280.046 12.489 7.4653
40 51.9807 48.0193 267.12 20.0767 12.8032 279.663 13.1975 7.13937
50 42.6668 57.3332 259.106 23.9648 16.9288 278.547 14.1718 7.28154
60 34.1329 65.8671 249.303 28.219 22.4779 276.387 15.5759 8.03726
70 26.8282 73.1718 237.925 32.673 29.4015 272.883 17.5329 9.58446
80 21.0639 78.9361 225.281 37.1632 37.5556 267.753 20.1067 12.1399
90 16.912 83.088 211.728 41.5524 46.7198 260.755 23.2934 15.9518
100 14.209 85.791 197.615 45.741 56.6437 251.691 27.028 21.2812
110 12.6627 87.3373 183.242 49.6669 67.0916 240.418 31.2039 28.3778
120 11.9752 88.0248 168.838 53.2952 77.8669 226.847 35.6941 37.4593
130 11.9025 88.0975 154.575 56.6081 88.8166 210.935 40.3657 48.6997
140 12.2722 87.7278 140.581 59.5964 99.8229 192.687 45.0875 62.225
150 12.9693 87.0307 126.954 62.2535 110.793 172.159 49.7275 78.1134
160 13.9136 86.0864 113.776 64.5696 121.655 149.463 54.137 96.4002
170 15.0464 84.9536 101.119 66.531 132.35 124.794 58.1242 117.082
180 16.327 83.673 89.048 68.1221 142.83 98.4838 61.4021 140.114
190 17.7272 82.2728 77.6283 69.3216 153.05 71.135 63.4644 165.401
200 19.2249 80.7751 66.927 70.0978 162.975 43.9906 63.2524 192.757
210 20.8022 79.1978 57.0153 70.4125 172.572 19.948 58.2596 221.792
220 22.4438 77.5562 47.9675 70.2228 181.81 5.26601 43.3359 251.398
230 24.1335 75.8665 39.8601 69.4809 190.659 1.45787 20.5235 278.019
240 25.8504 74.1496 32.7647 68.142 199.093 0.60959 5.4476 293.943
250 27.5686 72.4314 26.7356 66.176 207.088 0.221778 1.94841 297.83
260 29.2754 70.7246 21.7929 63.5845 214.623 0.138429 1.52105 298.341
270 30.9691 69.0309 17.907 60.42 221.673 0.121637 1.43216 298.446
280 32.6499 67.3501 14.9869 56.7967 228.216 0.113548 1.38174 298.505
290 34.3181 65.6819 12.8878 52.8814 234.231 0.1073 1.34087 298.552
300 35.9741 64.0259 11.4363 48.8658 239.698 0.1021 1.30576 298.592
310 37.6181 62.3819 10.4627 44.9319 244.605 0.0977342 1.27525 298.627
320 39.2502 60.7498 9.82348 41.2277 248.949 0.0940957 1.24981 298.656
330 40.8706 59.1294 9.41055 37.8569 252.733 0.0910575 1.22835 298.681
340 42.4793 57.5207 9.14917 34.8809 255.97 0.088575 1.21083 298.701
350 44.0763 55.9237 8.99134 32.3254 258.683 0.0865467 1.19615 298.717
360 45.6616 54.3384 8.90852 30.1892 260.902 0.0849479 1.18437 298.731
370 47.235 52.765 8.88536 28.4527 262.662 0.0836878 1.17523 298.741
380 48.7964 51.2036 8.91489 27.0843 264.001 0.0827465 1.16793 298.749
390 50.3457 49.6543 8.99514 26.0469 264.958 0.0820237 1.1629 298.755
400 51.8827 48.1173 9.12685 25.302 265.571 0.0815955 1.15902 298.759
410 53.407 46.593 9.31204 24.8128 265.875 0.0812996 1.15776 298.761
420 54.9183 45.0817 9.55322 24.5458 265.901 0.0812354 1.15696 298.762
430 56.4163 43.5837 9.85309 24.4717 265.675 0.0813329 1.15793 298.761
440 57.9007 42.0993 10.2144 24.5657 265.22 0.0815619 1.15975 298.759
450 59.3709 40.6291 10.6401 24.8071 264.553 0.0819101 1.1626 298.755
460 60.8266 39.1734 11.1331 25.1784 263.689 0.082403 1.16636 298.751
470 62.267 37.733 11.697 25.6654 262.638 0.0830249 1.1711 298.746
480 63.6918 36.3082 12.3354 26.2561 261.409 0.0837672 1.17677 298.739
490 65.1002 34.8998 13.0525 26.9406 260.007 0.0846309 1.18333 298.732
500 66.4916 33.5084 13.853 27.7103 258.437 0.0856191 1.19083 298.724
510 67.8651 32.1349 14.7421 28.5575 256.7 0.0867394 1.19923 298.714
520 69.2201 30.7799 15.7253 29.4753 254.799 0.087993 1.20858 298.703
530 70.5555 29.4445 16.8085 30.4576 252.734 0.0893904 1.21894 298.692
540 71.8705 28.1295 17.9981 31.498 250.504 0.0909402 1.23034 298.679
550 73.1641 26.8359 19.3003 32.5903 248.109 0.0926457 1.24284 298.665
560 74.4351 25.5649 20.7218 33.7288 245.549 0.094524 1.2565 298.649
570 75.6825 24.3175 22.2695 34.9081 242.822 0.0966081 1.27135 298.632
580 76.9049 23.0951 23.95 36.1222 239.928 0.0988934 1.28753 298.614
590 78.1011 21.8989 25.7697 37.3654 236.865 0.101401 1.30512 298.593
600 79.2697 20.7303 27.7345 38.6316 233.634 0.104141 1.32423 298.572
610 80.4092 19.5908 29.8495 39.9148 230.236 0.107145 1.34494 298.548
620 81.5181 18.4819 32.1196 41.2092 226.671 0.110446 1.36734 298.522
630 82.5948 17.4052 34.5485 42.5092 222.942 0.114081 1.39155 298.494
640 83.6377 16.3623 37.1397 43.8095 219.051 0.118068 1.41777 298.464
650 84.6451 15.3549 39.8954 45.1052 214.999 0.122433 1.44619 298.431
660 85.6153 14.3847 42.8168 46.3914 210.792 0.127204 1.47703 298.396
670 86.5468 13.4532 45.9041 47.6637 206.432 0.132456 1.51034 298.357
680 87.4379 12.5621 49.156 48.918 201.926 0.138262 1.54623 298.316
690 88.287 11.713 52.5706 50.1509 197.278 0.144696 1.58487 298.27
700 89.0928 10.9072 56.1448 51.359 192.496 0.151763 1.62684 298.221
710 89.8541 10.1459 59.8745 52.5394 187.586 0.15951 1.6726 298.168
720 90.5699 9.43009 63.7548 53.6894 182.556 0.168093 1.72225 298.11
730 91.2395 8.76052 67.7798 54.807 177.413 0.177652 1.77591 298.046
740 91.8625 8.13753 71.9429 55.8901 172.167 0.188334 1.83368 297.978
750 92.4389 7.56115 76.2367 56.9371 166.826 0.200115 1.89649 297.903
760 92.969 7.03098 80.6536 57.9466 161.4 0.213182 1.96512 297.822
770 93.4538 6.54622 85.1853 58.9173 155.897 0.227732 2.04052 297.732
780 93.8943 6.10567 89.8237 59.8478 150.328 0.244203 2.1226 297.633
790 94.2923 5.70769 94.5604 60.737 144.703 0.262836 2.21145 297.526
800 94.6497 5.35034 99.3871 61.5838 139.029 0.283593 2.30791 297.408
810 94.9686 5.03137 104.295 62.387 133.318 0.306739 2.41476 297.278
820 95.2517 4.74828 109.278 63.1451 127.577 0.333658 2.53329 297.133
830 95.5016 4.49843 114.326 63.8568 121.817 0.364234 2.66238 296.973
840 95.7209 4.27908 119.434 64.5202 116.046 0.398166 2.80562 296.796
850 95.9125 4.08747 124.594 65.1334 110.273 0.438222 2.96527 296.597
860 96.0791 3.9209 129.8 65.694 104.506 0.483979 3.14057 296.375
870 96.2232 3.77676 135.046 66.1993 98.7546 0.536628 3.33905 296.124
880 96.3474 3.65259 140.327 66.6459 93.0266 0.599084 3.55665 295.844
890 96.4539 3.5461 145.639 67.03 87.3311 0.670096 3.80538 295.525
900 96.5448 3.4552 150.976 67.3471 81.6773 0.754931 4.08417 295.161
910 96.622 3.37799 156.334 67.5917 76.0747 0.854734 4.39998 294.745
920 96.6872 3.31278 161.709 67.7574 70.5336 0.973449 4.75804 294.269
930 96.7419 3.2581 167.098 67.8368 65.0653 1.11532 5.16568 293.719
940 96.7874 3.21265 172.497 67.821 59.6823 1.28568 5.63107 293.083
950 96.8247 3.17532 177.902 67.6993 54.3984 1.49146 6.16349 292.345
960 96.8548 3.14519 183.311 67.4595 49.2298 1.74128 6.77307 291.486
970 96.8785 3.12151 188.719 67.0867 44.1946 2.04588 7.47047 290.484
980 96.8964 3.10364 194.123 66.5633 39.3142 2.41861 8.26669 289.315
990 96.9089 3.09114 199.518 65.8689 34.613 2.87563 9.17229 287.952
1000 96.9163 3.08366 204.901 64.9797 30.1194 3.43624 10.1954 286.368
## Printing Variable Parameter time courses
## No Values.
## Printing Reaction Flux time courses
#time J0 J1 J2 J3 J4 J5 J6 J7 J8 J9
0 1.06579 0.138889 0.237288 0.1 0.3 0.3 0.237288 0.1 0.2 0.2
10 1.11122 0.176695 0.457544 0.210822 0.274981 0.327984 0.206039 0.0923534 0.187708 0.212716
20 1.15173 0.195557 0.68154 0.345277 0.275561 0.360468 0.206731 0.0961711 0.175772 0.220773
30 1.17573 0.206873 0.909429 0.505265 0.301413 0.395004 0.239158 0.114476 0.166152 0.227163
40 1.16918 0.214298 1.13665 0.687115 0.345371 0.429274 0.303787 0.14981 0.161237 0.234019
50 1.11954 0.219388 1.35489 0.881551 0.397653 0.461278 0.401593 0.205602 0.163399 0.242903
60 1.02139 0.222924 1.55322 1.07516 0.449823 0.489698 0.53302 0.286266 0.17444 0.254709
70 0.881949 0.225361 1.72081 1.25372 0.49663 0.514017 0.69674 0.396133 0.194929 0.269464
80 0.721711 0.226995 1.85021 1.40593 0.535941 0.534331 0.889083 0.537732 0.223654 0.286366
90 0.566669 0.228043 1.93977 1.52624 0.567725 0.551069 1.10446 0.710477 0.257688 0.304144
100 0.43611 0.228676 1.99346 1.61512 0.592973 0.564787 1.33645 0.910682 0.293282 0.321547
110 0.336343 0.229022 2.01822 1.67697 0.612958 0.576031 1.57879 1.13276 0.327101 0.337676
120 0.263912 0.229172 2.02106 1.71729 0.628859 0.585274 1.82593 1.37067 0.357032 0.352054
130 0.211911 0.229188 2.00762 1.74108 0.641636 0.592895 2.073 1.61885 0.38226 0.364537
140 0.174064 0.229107 1.98174 1.75218 0.652023 0.599188 2.31533 1.87259 0.402881 0.375182
150 0.145837 0.228954 1.94586 1.75331 0.660567 0.604376 2.54783 2.12794 0.419453 0.38413
160 0.124204 0.228743 1.90147 1.74645 0.667676 0.608614 2.76398 2.38151 0.432675 0.39152
170 0.107205 0.228484 1.84949 1.7331 0.673651 0.612016 2.95373 2.63003 0.443217 0.397435
180 0.0935769 0.228183 1.79026 1.71434 0.678721 0.614657 3.09877 2.8697 0.451649 0.401835
190 0.0824838 0.227845 1.72374 1.69093 0.683056 0.616582 3.15993 3.09479 0.458426 0.404415
200 0.0733609 0.227471 1.64965 1.66343 0.686789 0.617799 3.03835 3.29337 0.4639 0.404156
210 0.0658398 0.227064 1.56754 1.63223 0.690023 0.618286 2.46257 3.43095 0.468327 0.397624
220 0.0597736 0.226624 1.47702 1.59764 0.692838 0.617993 1.18106 3.37652 0.471847 0.371434
230 0.0554258 0.226153 1.37807 1.5599 0.695298 0.616834 0.422223 2.7538 0.474404 0.288872
240 0.0535544 0.225654 1.27159 1.5193 0.697453 0.614689 0.194376 1.32605 0.475724 0.133209
250 0.0538114 0.225134 1.15998 1.47618 0.699345 0.611412 0.0754307 0.595179 0.476025 0.0574806
260 0.0545689 0.224595 1.04728 1.43062 0.701007 0.606842 0.0490639 0.493994 0.476064 0.0460337
270 0.0553204 0.224036 0.939113 1.38254 0.702466 0.600835 0.0445779 0.483004 0.476072 0.0435781
280 0.0560137 0.223457 0.841509 1.33198 0.703745 0.593308 0.0428646 0.481231 0.476077 0.0421732
290 0.0566508 0.222856 0.75884 1.2792 0.704861 0.58427 0.0415909 0.480501 0.476081 0.041028
300 0.0572379 0.222232 0.692439 1.22471 0.70583 0.573849 0.0405129 0.479873 0.476084 0.0400399
310 0.0577807 0.221584 0.640823 1.16922 0.706665 0.562287 0.0395859 0.479152 0.476086 0.0391776
320 0.0582842 0.220909 0.601018 1.11359 0.707378 0.549921 0.0387983 0.478681 0.476088 0.0384562
330 0.0587525 0.220207 0.569877 1.05873 0.70798 0.537161 0.0381239 0.478242 0.47609 0.0378457
340 0.0591893 0.219475 0.544809 1.00558 0.708482 0.524463 0.0375657 0.477979 0.476092 0.0373465
350 0.0595976 0.218713 0.52397 0.954961 0.708894 0.512284 0.0370996 0.477621 0.476093 0.0369271
360 0.0599801 0.217917 0.506174 0.907537 0.709225 0.501047 0.0367305 0.477321 0.476094 0.03659
370 0.0603392 0.217086 0.490715 0.863759 0.709483 0.491098 0.0364327 0.477101 0.476095 0.0363282
380 0.0606768 0.216218 0.477186 0.82383 0.709678 0.482679 0.0362088 0.476768 0.476096 0.0361187
390 0.0609949 0.21531 0.465352 0.787721 0.709815 0.475923 0.0360244 0.476584 0.476096 0.0359743
400 0.0612949 0.21436 0.455053 0.755214 0.709903 0.470858 0.0359203 0.476209 0.476096 0.035863
410 0.0615786 0.213365 0.446154 0.725962 0.709947 0.467428 0.0358317 0.476271 0.476096 0.0358267
420 0.0618469 0.212322 0.438512 0.699547 0.70995 0.465519 0.035807 0.476012 0.476097 0.0358037
430 0.0621011 0.211228 0.431972 0.675526 0.709918 0.464986 0.0358193 0.47598 0.476096 0.0358317
440 0.0623421 0.210079 0.426363 0.65347 0.709853 0.465663 0.0358581 0.475855 0.476096 0.0358838
450 0.062571 0.208872 0.421505 0.632983 0.709757 0.467387 0.0359198 0.475742 0.476096 0.0359657
460 0.0627885 0.207603 0.417213 0.613715 0.709632 0.469999 0.0360167 0.475611 0.476096 0.0360737
470 0.0629953 0.206268 0.413307 0.595365 0.70948 0.473352 0.0361424 0.475503 0.476095 0.0362098
480 0.0631922 0.204862 0.409611 0.577679 0.709299 0.477313 0.0362931 0.475401 0.476095 0.0363723
490 0.0633798 0.20338 0.405962 0.560448 0.709092 0.481764 0.0364686 0.475295 0.476094 0.0365602
500 0.0635587 0.201817 0.402205 0.543504 0.708857 0.486597 0.0366692 0.475198 0.476094 0.0367748
510 0.0637293 0.200168 0.398203 0.526713 0.708594 0.49172 0.0368967 0.475088 0.476093 0.037015
520 0.0638921 0.198427 0.393832 0.509973 0.708302 0.497051 0.0371497 0.474974 0.476092 0.0372822
530 0.0640475 0.196588 0.388983 0.493211 0.707981 0.502516 0.0374303 0.474858 0.476091 0.0375777
540 0.064196 0.194644 0.383565 0.476376 0.707628 0.508054 0.0377393 0.474735 0.47609 0.0379024
550 0.064338 0.192588 0.377504 0.459437 0.707242 0.513608 0.0380753 0.474609 0.476089 0.038258
560 0.0644736 0.190414 0.370747 0.442383 0.706822 0.51913 0.0384416 0.474475 0.476088 0.038646
570 0.0646032 0.188114 0.363259 0.425221 0.706365 0.524586 0.0388475 0.474319 0.476087 0.0390671
580 0.0647272 0.185681 0.355024 0.407966 0.70587 0.529939 0.0392864 0.474157 0.476085 0.039525
590 0.0648457 0.183108 0.346046 0.39065 0.705333 0.535164 0.0397617 0.473987 0.476084 0.0400217
600 0.064959 0.180387 0.336347 0.373309 0.704753 0.540236 0.0402717 0.473813 0.476082 0.0405603
610 0.0650673 0.177512 0.325966 0.35599 0.704126 0.545137 0.0408228 0.473622 0.47608 0.0411424
620 0.0651708 0.174477 0.31496 0.338746 0.703449 0.549855 0.0414197 0.473408 0.476078 0.0417705
630 0.0652697 0.171276 0.303402 0.321636 0.70272 0.554379 0.0420693 0.473164 0.476076 0.0424472
640 0.0653641 0.167906 0.291377 0.304724 0.701934 0.558705 0.0427683 0.472907 0.476074 0.0431778
650 0.0654543 0.164365 0.278981 0.288073 0.701087 0.562828 0.0435166 0.472648 0.476071 0.0439674
660 0.0655403 0.160653 0.266318 0.27175 0.700175 0.56675 0.0443135 0.472393 0.476069 0.0448208
670 0.0656224 0.156774 0.253496 0.255822 0.699194 0.57047 0.0451729 0.472102 0.476066 0.0457391
680 0.0657007 0.152734 0.240626 0.240353 0.698139 0.573993 0.046106 0.471745 0.476062 0.0467245
690 0.0657752 0.148544 0.227821 0.225407 0.697004 0.577324 0.0471213 0.471304 0.476059 0.0477806
700 0.0658463 0.14422 0.215188 0.211042 0.695782 0.580468 0.048202 0.470865 0.476055 0.048922
710 0.0659142 0.139782 0.202833 0.197314 0.694468 0.583431 0.049345 0.470467 0.476051 0.0501601
720 0.0659789 0.135256 0.19085 0.18427 0.693054 0.586219 0.0505773 0.470044 0.476047 0.0514959
730 0.0660407 0.130672 0.179327 0.171952 0.691532 0.588841 0.051915 0.469527 0.476042 0.0529303
740 0.0660997 0.126065 0.16834 0.160392 0.689893 0.591304 0.0533714 0.468851 0.476037 0.0544646
750 0.0661562 0.121475 0.157951 0.149613 0.688128 0.593613 0.0549082 0.468122 0.476031 0.0561209
760 0.0662107 0.116941 0.14821 0.13963 0.686224 0.595778 0.0565422 0.467385 0.476025 0.0579165
770 0.0662635 0.112507 0.139153 0.130445 0.684171 0.597803 0.0582863 0.466699 0.476018 0.0598726
780 0.0663149 0.108213 0.130799 0.122051 0.681954 0.599695 0.0602042 0.465885 0.47601 0.0619823
790 0.0663651 0.104097 0.123156 0.114432 0.679557 0.60146 0.0622969 0.46481 0.476002 0.0642435
800 0.0664145 0.100191 0.116218 0.10756 0.676962 0.603102 0.0644935 0.463469 0.475993 0.0666722
810 0.066464 0.0965242 0.109968 0.101403 0.674149 0.604627 0.0667903 0.462152 0.475983 0.0693309
820 0.0665144 0.0931161 0.104379 0.0959211 0.671095 0.606037 0.0694015 0.460823 0.475972 0.0722422
830 0.0665657 0.0899799 0.0994169 0.0910687 0.667773 0.607336 0.0721969 0.459059 0.475959 0.0753686
840 0.0666187 0.0871213 0.0950405 0.0867977 0.664152 0.608526 0.0750181 0.457132 0.475946 0.0787846
850 0.0666749 0.0845394 0.0912062 0.0830586 0.660196 0.609609 0.0782538 0.455029 0.47593 0.0825278
860 0.0667341 0.0822274 0.0878681 0.0798013 0.655863 0.610584 0.0816631 0.452313 0.475913 0.086562
870 0.0667984 0.0801739 0.0849799 0.0769768 0.651103 0.611452 0.0852734 0.449514 0.475894 0.0910366
880 0.0668679 0.0783643 0.0824964 0.0745383 0.645859 0.61221 0.0893173 0.445747 0.475872 0.0958321
890 0.066945 0.0767813 0.0803743 0.0724415 0.640063 0.612855 0.093363 0.441799 0.475847 0.101178
900 0.067031 0.0754067 0.0785734 0.0706453 0.633633 0.613383 0.0978435 0.43699 0.475819 0.107004
910 0.0671278 0.074222 0.0770562 0.0691122 0.626475 0.613788 0.10253 0.43135 0.475787 0.113402
920 0.0672375 0.0732088 0.0757894 0.0678083 0.618473 0.614061 0.107461 0.424639 0.475749 0.120408
930 0.0673632 0.0723501 0.074743 0.0667032 0.60949 0.614191 0.112577 0.416681 0.475706 0.128081
940 0.0675079 0.07163 0.0738908 0.0657698 0.599362 0.614165 0.117791 0.407244 0.475656 0.136471
950 0.0676757 0.0710342 0.0732102 0.0649845 0.587893 0.613965 0.122993 0.396064 0.475597 0.145616
960 0.0678713 0.0705505 0.0726824 0.0643265 0.574848 0.613569 0.128011 0.382855 0.475529 0.155538
970 0.0680999 0.0701683 0.0722917 0.0637776 0.559949 0.61295 0.132608 0.36732 0.475449 0.166229
980 0.0683679 0.069879 0.0720256 0.0633216 0.542872 0.61207 0.136471 0.349209 0.475354 0.177651
990 0.0686825 0.0696759 0.0718749 0.0629445 0.523245 0.610886 0.139204 0.328352 0.475244 0.189727
1000 0.0690512 0.0695542 0.0718328 0.0626331 0.500662 0.609339 0.140345 0.304699 0.475114 0.202327
## Printing Species time courses
#time MKKK MKKK_P MKK MKK_P MKK_PP MAPK MAPK_P MAPK_PP
0 90 10 280 10 10 280 10 10
0.5 89.5367 10.4633 280.029 10.0694 9.90171 279.982 10.0678 9.95
1 89.0736 10.9264 280.053 10.1402 9.80684 279.966 10.1341 9.90002
4 86.2964 13.7036 280.096 10.5951 9.30931 279.898 10.5014 9.60098
9 81.6517 18.3483 279.782 11.4656 8.75197 279.874 11.0149 9.11091
16 75.0723 24.9277 278.541 12.9208 8.53802 279.952 11.5834 8.46472
25 66.4598 33.5402 275.564 15.2102 9.2258 280.064 12.1744 7.76191
FIRST INTEGRATION RUN WITH:
SOSlib INTEGRATION SETTINGS
1) CVODE SPECIFIC SETTINGS:
absolute error tolerance for each output time: 1e-20
relative error tolerance for each output time: 1e-14
max. nr. of steps to reach next output time: 500
Nonlinear solver method: 1: ADAMS-MOULTON
Maximum Order: 12
Iteration method: 0: NEWTON
Sensitivity: 0: no
method: 0: simultaneous
2) SOSlib SPECIFIC SETTINGS:
Jacobian matrix: 1: generate Jacobian
Indefinitely: 1: infinite integration
Event Handling: 0: keep integrating
Steady States: 0: keep integrating
Steady state threshold: 1e-11
Store Results: 0: don't store results
3) TIME SETTINGS:
Infinite integration with time step 0.1
#time S1 S2 R1 R2 c
0.05 1.5e-15 1.5e-15 1.5e-15 0 1
0.1 1.42685e-15 1.57315e-15 1.3905e-15 0 1
0.2 1.29107e-15 1.70893e-15 1.24547e-15 0 1
0.3 1.16821e-15 1.83179e-15 1.15887e-15 0 1
0.4 1.05704e-15 1.94296e-15 1.00721e-15 0 1
0.5 9.56446e-16 2.04355e-15 8.75411e-16 0 1
0.6 8.65427e-16 2.13457e-15 7.60848e-16 0 1
0.7 7.83069e-16 2.21693e-15 7.60848e-16 0 1
0.8 7.0855e-16 2.29145e-15 6.61276e-16 0 1
0.9 6.41121e-16 2.35888e-15 5.74732e-16 0 1
1 5.8011e-16 2.41989e-15 5.74732e-16 0 1
1.1 5.24904e-16 2.4751e-15 4.99515e-16 0 1
1.2 4.74952e-16 2.52505e-15 4.34142e-16 0 1
NOW, LET'S TRY AGAIN WITH DIFFERENT SETTINGS:
SOSlib INTEGRATION SETTINGS
1) CVODE SPECIFIC SETTINGS:
absolute error tolerance for each output time: 1e-16
relative error tolerance for each output time: 1e-14
max. nr. of steps to reach next output time: 500
Nonlinear solver method: 0: BDF
Maximum Order: 5
Iteration method: 0: NEWTON
Sensitivity: 0: no
method: 0: simultaneous
2) SOSlib SPECIFIC SETTINGS:
Jacobian matrix: 1: generate Jacobian
Indefinitely: 0: finite integration
Event Handling: 0: keep integrating
Steady States: 0: keep integrating
Steady state threshold: 1e-11
Store Results: 0: don't store results
3) TIME SETTINGS:
endtime: 1.2
steps: 6
0 1.5e-15 1.5e-15 1.5e-15 0 1
0.2 1.22919e-15 1.77081e-15 8.9691e-16 0 1
0.4 1.00169e-15 1.99831e-15 8.9691e-16 0 1
0.6 8.18483e-16 2.18152e-15 6.1341e-16 0 1
0.8 6.67153e-16 2.33285e-15 6.1341e-16 0 1
1 5.44887e-16 2.45511e-15 3.91963e-16 0 1
1.2 4.44381e-16 2.55562e-15 3.91963e-16 0 1
FINISHED SUCCESSFULLY!
Please, note the different values e.g. at time 1.2.
The values for the first run a more exact, due to the much
lower error tolerances. The error tolerances have to be
adapted to the ranges of each model!!
0 1.5e-15 1.5e-15 1.5e-15 1.5e-15
0.1 1.35782e-15 1.64218e-15 1.35782e-15 1.64218e-15
0.2 1.22853e-15 1.77147e-15 1.22853e-15 1.77147e-15
0.3 1.11115e-15 1.88885e-15 1.11115e-15 1.88885e-15
0.4 1.0049e-15 1.9951e-15 1.0049e-15 1.9951e-15
0.5 9.08806e-16 2.09119e-15 1.5e-15 1.5e-15
0.6 8.21917e-16 2.17808e-15 1.5e-15 1.5e-15
0.7 7.43196e-16 2.2568e-15 1.5e-15 1.5e-15
0.8 6.72259e-16 2.32774e-15 1.5e-15 1.5e-15
0.9 6.08132e-16 2.39187e-15 1.5e-15 1.5e-15
1 5.50247e-16 2.44975e-15 1.5e-15 1.5e-15
1.1 4.97953e-16 2.50205e-15 1.5e-15 1.5e-15
1.2 4.50739e-16 2.54926e-15 1.5e-15 1.5e-15
1.3 4.08331e-16 2.59167e-15 1.5e-15 1.5e-15
1.4 3.69986e-16 2.63001e-15 1.5e-15 1.5e-15
1.5 3.35099e-16 2.6649e-15 1.5e-15 1.5e-15
1.6 3.03473e-16 2.69653e-15 1.5e-15 1.5e-15
1.7 2.74823e-16 2.72518e-15 1.5e-15 1.5e-15
1.8 2.48789e-16 2.75121e-15 1.5e-15 1.5e-15
1.9 2.25245e-16 2.77476e-15 1.5e-15 1.5e-15
2 2.03868e-16 2.79613e-15 1.5e-15 1.5e-15
2.1 1.84549e-16 2.81545e-15 1.5e-15 1.5e-15
2.2 1.67028e-16 2.83297e-15 1.5e-15 1.5e-15
2.3 1.51188e-16 2.84881e-15 1.5e-15 1.5e-15
2.4 1.36844e-16 2.86316e-15 1.5e-15 1.5e-15
2.5 1.23859e-16 2.87614e-15 1.5e-15 1.5e-15
2.6 1.12117e-16 2.88788e-15 1.5e-15 1.5e-15
2.7 1.0148e-16 2.89852e-15 1.5e-15 1.5e-15
2.8 9.18948e-17 2.90811e-15 1.35782e-15 1.64218e-15
2.9 8.32257e-17 2.91677e-15 1.22853e-15 1.77147e-15
3 7.53616e-17 2.92464e-15 1.11115e-15 1.88885e-15
Successfully constructed the jacobian matrix J
We might be interested in the `sparsity' of J,
... we can just evaluate the jacobian entries:
Jacobian with initial conditions:
i\j 0 1 2 3 4 5 6 7
0 - + 0 0 0 0 0 +
1 + - 0 0 0 0 0 -
2 0 - - + 0 0 0 0
3 0 + + - + 0 0 0
4 0 + 0 + - 0 0 0
5 0 0 0 0 - - + 0
6 0 0 0 0 + + - +
7 0 0 0 0 + 0 + -
Does it change after integration?
Jacobian at time 1000:
i\j 0 1 2 3 4 5 6 7
0 - + 0 0 0 0 0 +
1 + - 0 0 0 0 0 -
2 0 - - + 0 0 0 0
3 0 + + - + 0 0 0
4 0 + 0 + - 0 0 0
5 0 0 0 0 - - + 0
6 0 0 0 0 - + - +
7 0 0 0 0 + 0 + -
J[6,4] changed its sign. Let's take a look at the equations:
The ODE dMAPK_P/dt =
(J6 - J7 + J8 - J9) / uVol
The jacobian entry (dMAPK_P/dt)/dMKK_PP =
(0.025 * MAPK / (15 + MAPK) - 0.025 * MAPK_P / (15 + MAPK_P)) / uVol
MAPK_P is both a substrate and a product of MKK_PP in different reactions.
Therefor the corresponding entry in the jacobian can change its sign, depending on concentrations!
Take a look at jacobian interaction graph infile jacobian_jm.gif that has just been constructed.
If you have compiled w/o graphviz, you just have a textfile jacobian.dot
Thx and good bye!
Successfully constructed the parametric matrix S as used for CVODES sensitivity analysis
We might be interested in the `sparsity' of S,
... we can just evaluate the parametric entries:
... how many parametric entries: 4
ODE VARIABLE 1: MKKK
dY/dt = (J1 - J0) / uVol
Parameter 0: uVol S[0][0] = -((0.25 * MKKK_P / (8 + MKKK_P) - V1 * MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK))) / uVol^2)
Parameter 1: V1 S[0][1] = -(MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK)) / uVol)
Parameter 2: Ki S[0][2] = -(V1 * MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK))^2 * (MAPK_PP / Ki)^(1 - 1) * (MAPK_PP / Ki^2) * (K1 + MKKK) / uVol)
Parameter 3: K1 S[0][3] = V1 * MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK))^2 * (1 + MAPK_PP / Ki) / uVol
ODE VARIABLE 2: MKKK_P
dY/dt = (J0 - J1) / uVol
Parameter 0: uVol S[1][0] = -((V1 * MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK)) - 0.25 * MKKK_P / (8 + MKKK_P)) / uVol^2)
Parameter 1: V1 S[1][1] = MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK)) / uVol
Parameter 2: Ki S[1][2] = V1 * MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK))^2 * (MAPK_PP / Ki)^(1 - 1) * (MAPK_PP / Ki^2) * (K1 + MKKK) / uVol
Parameter 3: K1 S[1][3] = -(V1 * MKKK / ((1 + MAPK_PP / Ki) * (K1 + MKKK))^2 * (1 + MAPK_PP / Ki) / uVol)
ODE VARIABLE 3: MKK
dY/dt = (J5 - J2) / uVol
Parameter 0: uVol S[2][0] = -((0.75 * MKK_P / (15 + MKK_P) - 0.025 * MKKK_P * MKK / (15 + MKK)) / uVol^2)
Parameter 1: V1 S[2][1] = 0
Parameter 2: Ki S[2][2] = 0
Parameter 3: K1 S[2][3] = 0
ODE VARIABLE 4: MKK_P
dY/dt = (J2 - J3 + J4 - J5) / uVol
Parameter 0: uVol S[3][0] = -((0.025 * MKKK_P * MKK / (15 + MKK) - 0.025 * MKKK_P * MKK_P / (15 + MKK_P) + 0.75 * MKK_PP / (15 + MKK_PP) - 0.75 * MKK_P / (15 + MKK_P)) / uVol^2)
Parameter 1: V1 S[3][1] = 0
Parameter 2: Ki S[3][2] = 0
Parameter 3: K1 S[3][3] = 0
ODE VARIABLE 5: MKK_PP
dY/dt = (J3 - J4) / uVol
Parameter 0: uVol S[4][0] = -((0.025 * MKKK_P * MKK_P / (15 + MKK_P) - 0.75 * MKK_PP / (15 + MKK_PP)) / uVol^2)
Parameter 1: V1 S[4][1] = 0
Parameter 2: Ki S[4][2] = 0
Parameter 3: K1 S[4][3] = 0
ODE VARIABLE 6: MAPK
dY/dt = (J9 - J6) / uVol
Parameter 0: uVol S[5][0] = -((0.5 * MAPK_P / (15 + MAPK_P) - 0.025 * MKK_PP * MAPK / (15 + MAPK)) / uVol^2)
Parameter 1: V1 S[5][1] = 0
Parameter 2: Ki S[5][2] = 0
Parameter 3: K1 S[5][3] = 0
ODE VARIABLE 7: MAPK_P
dY/dt = (J6 - J7 + J8 - J9) / uVol
Parameter 0: uVol S[6][0] = -((0.025 * MKK_PP * MAPK / (15 + MAPK) - 0.025 * MKK_PP * MAPK_P / (15 + MAPK_P) + 0.5 * MAPK_PP / (15 + MAPK_PP) - 0.5 * MAPK_P / (15 + MAPK_P)) / uVol^2)
Parameter 1: V1 S[6][1] = 0
Parameter 2: Ki S[6][2] = 0
Parameter 3: K1 S[6][3] = 0
ODE VARIABLE 8: MAPK_PP
dY/dt = (J7 - J8) / uVol
Parameter 0: uVol S[7][0] = -((0.025 * MKK_PP * MAPK_P / (15 + MAPK_P) - 0.5 * MAPK_PP / (15 + MAPK_PP)) / uVol^2)
Parameter 1: V1 S[7][1] = 0
Parameter 2: Ki S[7][2] = 0
Parameter 3: K1 S[7][3] = 0
Sensitivity: parametric matrix with initial conditions:
i\j 0 1 2 3
0 + - - +
1 - + + -
2 - 0 0 0
3 - 0 0 0
4 + 0 0 0
5 + 0 0 0
6 - 0 0 0
7 + 0 0 0
Thx and good bye!
### Printing Sensitivities to K1 (10) on the fly:
#time MKKK MKKK_P MKK MKK_P MKK_PP MAPK MAPK_P MAPK_PP J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 uVol V1 Ki K1
0 0 0 0 0 0 0 0 0
30 0.376101 -0.376101 0.118593 -0.0574272 -0.0611659 0.0130732 -0.00707336 -0.00599986
60 0.827581 -0.827581 0.515957 -0.190622 -0.325335 0.127081 -0.0641635 -0.0629178
90 1.05294 -1.05294 1.15453 -0.311331 -0.843199 0.504418 -0.207313 -0.297105
120 0.986708 -0.986708 1.80548 -0.356609 -1.44887 1.27134 -0.37598 -0.895362
150 0.915832 -0.915832 2.3474 -0.320053 -2.02735 2.41127 -0.472765 -1.93851
180 0.884752 -0.884752 2.75501 -0.179487 -2.57553 3.7224 -0.336436 -3.38596
210 0.868732 -0.868732 2.94006 0.149011 -3.08907 3.44841 1.54277 -4.99118
240 0.865232 -0.865232 2.71185 0.829009 -3.54086 0.12877 1.59264 -1.72141
270 0.884511 -0.884511 1.9523 1.92312 -3.87542 0.00481648 0.0298059 -0.0346223
300 0.903155 -0.903155 1.12274 2.87547 -3.99822 0.00350199 0.0240978 -0.0275998
Let's look at a specific ODE variable:
### RESULTS for Sensitivity Analysis for one ODE variable
#time Variable Sensitivity Params...
#time MAPK_P uVol V1 Ki K1
0 10 0 0 0 0
30 12.489 -1.9166 0.260431 0.0365621 -0.00707336
60 15.5756 -9.98879 2.11944 0.286395 -0.0641635
90 23.2928 -31.2772 6.02119 0.799356 -0.207313
120 35.6933 -55.2295 9.66907 1.29008 -0.37598
150 49.7262 -68.2277 11.0066 1.492 -0.472765
180 61.3803 -49.717 7.31996 1.00958 -0.336436
210 58.241 188.72 -29.7585 -4.2461 1.54277
240 5.44918 189.301 -28.9929 -4.23937 1.59264
270 1.43183 1.59018 -0.433512 -0.0710146 0.0298059
300 1.30543 0.976291 -0.33193 -0.0566546 0.0240978
What do sensitivities mean? Let's try out!
... add 1 to V1: 2.5 + 1 = 3.5
... and integrate again:
#time MAPK_P
0 10
30 12.7519
60 17.6623
90 28.6524
120 43.378
150 57.4222
180 62.5827
210 13.7676
240 1.36179
270 1.22088
300 1.14518
See the difference?
Look what happens when the sensitivity changes its sign
between times 180 and 210.
This example performs forward and adjoint sensitivity analysis for data mis-match functional
J(x) = int_0^T | x - x_data |^2 dt. The two methods should give (up-to-numerics) equivalent results.
### Printing Sensitivities to rho (2) on the fly:
#time x1 x2 x3 y1 y2 y3 compartment alpha beta rho
0 0 0 0 0 0 0
20 -0.0993213 -0.041987 0.0250429 -0.103489 -0.0424494 0.029678
40 -0.0749654 -0.00271113 -0.0247045 -0.0742884 0.00827753 -0.0365409
60 -0.124396 0.128368 -0.102166 -0.120113 0.122763 -0.100837
80 0.226478 -0.101326 -0.216814 0.238041 -0.103316 -0.227915
100 -0.0267959 -0.237976 0.19126 -0.0403631 -0.256414 0.221589
### Printing Forward Sensitivities: int_0^T <x-x_delta, x_sens> dt
Computed J=0.791943307218501
### Now computing and printing out directional sensitivities
with dp[0] = 0 dp[1] = 0 dp[2] = 0 dp[3] = 1
#time x1 x2 x3 y1 y2 y3 compartment alpha beta rho
0 0 0 0 0 0 0
20 -0.099321343 -0.04198701 0.025042891 -0.10348941 -0.042449383 0.029678027
40 -0.074965374 -0.0027111265 -0.024704493 -0.07428837 0.0082775293 -0.036540892
60 -0.12439585 0.1283681 -0.10216632 -0.12011316 0.12276325 -0.10083711
80 0.22647809 -0.10132623 -0.21681407 0.23804116 -0.10331586 -0.22791524
100 -0.026795859 -0.23797569 0.19126016 -0.040363128 -0.25641373 0.22158857
### Commencing adjoint integration:
#time x1 x2 x3 y1 y2 y3 compartment alpha beta rho
100 0 0 0 0 0 0
80 -0.148492 2.89523 -3.45125 -0.0681658 3.1721 -3.52727
60 -4.7035 0.957922 1.07116 -4.71783 1.24969 0.483427
40 -9.72103 -3.7932 2.86995 -11.8885 -3.73705 3.38892
20 4.57572 -24.3875 -1.83815 5.08817 -26.7492 -1.66414
0 -0.204992 1.68361 -9.70708 0.0167496 2.25346 -13.7315
### Printing Adjoint Sensitivities: int_0^T <df/dp, psi> dt
dJ/dp_0=0 dJ/dp_1=27.8381863734458 dJ/dp_2=9.54699344761958 dJ/dp_3=-0.304136075267367
set xlabel 'time'
set ylabel 'Ki'
splot '-' using 1:2:3 title 'MAPK_PP' with lines
0 0 10
Parameter = 0
0 5 10
50 5 6.52749
100 5 13.3613
150 5 47.0214
200 5 122.331
250 5 236.759
300 5 297.844
350 5 298.164
400 5 298.226
450 5 298.191
500 5 298.059
550 5 297.802
600 5 297.352
650 5 296.559
700 5 295.078
750 5 292.038
800 5 285.317
850 5 271.696
900 5 251.393
950 5 228.794
1000 5 205.889
1050 5 182.917
1100 5 160.068
1150 5 137.474
1200 5 115.275
1250 5 93.6303
1300 5 72.792
1350 5 53.1976
1400 5 35.6753
1450 5 21.864
1500 5 14.7435
1550 5 18.4589
1600 5 38.2963
1650 5 80.1127
1700 5 145.079
1750 5 226.734
1800 5 290.959
1850 5 296.381
1900 5 295.995
1950 5 295.027
2000 5 292.989
2050 5 288.528
2100 5 278.829
2150 5 261.724
2200 5 240.016
2250 5 217.264
2300 5 194.385
2350 5 171.496
2400 5 148.764
2450 5 126.352
2500 5 104.406
2550 5 83.1233
2600 5 62.8331
2650 5 44.1313
2700 5 28.1815
2750 5 17.2424
2800 5 14.9491
2850 5 25.9325
2900 5 56.1904
2950 5 109.883
3000 5 184.621
3050 5 266.586
3100 5 296.017
3150 5 296.263
3200 5 295.61
3250 5 294.216
3300 5 291.235
3350 5 284.627
3400 5 271.3
3450 5 251.34
3500 5 228.868
3550 5 206.03
3600 5 183.148
3650 5 160.275
3700 5 137.645
3750 5 115.466
3800 5 93.8351
3850 5 72.9874
3900 5 53.3786
3950 5 35.8307
4000 5 21.9721
4050 5 14.7701
4100 5 18.3727
4150 5 38.0597
4200 5 79.6707
4250 5 144.436
4300 5 226.005
4350 5 290.757
4400 5 296.383
4450 5 296.003
4500 5 295.041
4550 5 293.018
4600 5 288.591
4650 5 278.981
4700 5 261.952
4750 5 240.265
4800 5 217.516
4850 5 194.637
4900 5 171.748
4950 5 149.013
5000 5 126.597
5050 5 104.644
5100 5 83.353
5150 5 63.049
5200 5 44.3244
5250 5 28.3335
5300 5 17.3215
5350 5 14.9102
5400 5 25.7041
5450 5 55.6529
5500 5 109.033
5550 5 183.559
5600 5 265.672
5650 5 295.983
5700 5 296.267
5750 5 295.621
5800 5 294.238
5850 5 291.285
5900 5 284.742
5950 5 271.512
6000 5 251.602
6050 5 229.144
6100 5 206.303
6150 5 183.402
6200 5 160.566
6250 5 137.974
6300 5 115.767
6350 5 94.1074
6400 5 73.2476
6450 5 53.6193
6500 5 36.0374
6550 5 22.1167
6600 5 14.8035
6650 5 18.2217
6700 5 37.6357
6750 5 78.9166
6800 5 143.412
6850 5 224.885
6900 5 290.418
6950 5 296.385
7000 5 296.013
7050 5 295.063
7100 5 293.065
7150 5 288.692
7200 5 279.19
7250 5 262.278
7300 5 240.627
7350 5 217.88
7400 5 195.001
7450 5 172.111
7500 5 149.373
7550 5 126.95
7600 5 104.987
7650 5 83.6839
7700 5 63.3611
7750 5 44.6051
7800 5 28.5568
7850 5 17.4406
7900 5 14.8632
7950 5 25.413
8000 5 55.0413
8050 5 108.064
8100 5 182.303
8150 5 264.565
8200 5 295.939
8250 5 296.271
8300 5 295.631
8350 5 294.262
8400 5 291.338
8450 5 284.852
8500 5 271.717
8550 5 251.868
8600 5 229.489
8650 5 206.638
8700 5 183.742
8750 5 160.902
8800 5 138.301
8850 5 116.086
8900 5 94.4173
8950 5 73.5433
9000 5 53.892
9050 5 36.2716
9100 5 22.2804
9150 5 14.8401
9200 5 18.0601
9250 5 37.2122
9300 5 78.1867
9350 5 142.415
9400 5 223.791
9450 5 290.093
9500 5 296.39
9550 5 296.028
9600 5 295.092
9650 5 293.125
9700 5 288.823
9750 5 279.462
9800 5 262.684
9850 5 241.08
9900 5 218.342
9950 5 195.465
10000 5 172.573
0 10 10
50 10 7.42769
100 10 22.8334
150 10 83.7157
200 10 204.371
250 10 298.24
300 10 298.648
350 10 298.758
400 10 298.79
450 10 298.783
500 10 298.755
550 10 298.705
600 10 298.629
650 10 298.516
700 10 298.349
750 10 298.103
800 10 297.73
850 10 297.146
900 10 296.172
950 10 294.404
1000 10 290.856
1050 10 283.367
1100 10 269.286
1150 10 249.509
1200 10 227.688
1250 10 205.463
1300 10 183.024
1350 10 160.632
1400 10 138.572
1450 10 117.1
1500 10 96.5352
1550 10 77.4006
1600 10 60.6323
1650 10 47.9516
1700 10 42.3668
1750 10 48.3932
1800 10 71.3044
1850 10 115.099
1900 10 179.883
1950 10 257.141
2000 10 295.49
2050 10 296.576
2100 10 296.399
2150 10 295.919
2200 10 294.978
2250 10 293.172
2300 10 289.538
2350 10 282.088
2400 10 268.474
2450 10 249.334
2500 10 227.951
2550 10 206.067
2600 10 183.97
2650 10 161.768
2700 10 139.769
2750 10 118.295
2800 10 97.6907
2850 10 78.4718
2900 10 61.5472
2950 10 48.5806
3000 10 42.4918
3050 10 47.7141
3100 10 69.4974
3150 10 111.961
3200 10 175.494
3250 10 252.577
3300 10 295.079
3350 10 296.565
3400 10 296.403
3450 10 295.938
3500 10 295.023
3550 10 293.265
3600 10 289.739
3650 10 282.502
3700 10 269.169
3750 10 250.189
3800 10 228.85
3850 10 206.979
3900 10 184.89
3950 10 162.686
4000 10 140.673
4050 10 119.171
4100 10 98.5206
4150 10 79.2287
4200 10 62.1825
4250 10 49.0042
4300 10 42.5511
4350 10 47.1923
4400 10 68.1845
4450 10 109.764
4500 10 172.517
4550 10 249.522
4600 10 294.765
4650 10 296.567
4700 10 296.418
4750 10 295.969
4800 10 295.079
4850 10 293.374
4900 10 289.961
4950 10 282.939
5000 10 269.873
5050 10 251.083
5100 10 229.794
5150 10 207.936
5200 10 185.857
5250 10 163.653
5300 10 141.625
5350 10 120.094
5400 10 99.3983
5450 10 80.0326
5500 10 62.8605
5550 10 49.4629
5600 10 42.635
5650 10 46.6826
5700 10 66.8604
5750 10 107.511
5800 10 169.425
5850 10 246.25
5900 10 294.36
5950 10 296.567
6000 10 296.432
6050 10 295.998
6100 10 295.135
6150 10 293.483
6200 10 290.181
6250 10 283.392
6300 10 270.665
6350 10 252.081
6400 10 230.846
6450 10 208.997
6500 10 186.926
6550 10 164.722
6600 10 142.677
6650 10 121.115
6700 10 100.37
6750 10 80.9247
6800 10 63.6187
6850 10 49.9874
6900 10 42.757
6950 10 46.1692
7000 10 65.4547
7050 10 105.045
7100 10 165.964
7150 10 242.457
7200 10 293.78
7250 10 296.562
7300 10 296.443
7350 10 296.024
7400 10 295.184
7450 10 293.579
7500 10 290.379
7550 10 283.799
7600 10 271.372
7650 10 252.983
7700 10 231.8
7750 10 209.959
7800 10 187.897
7850 10 165.7
7900 10 143.641
7950 10 122.051
8000 10 101.258
8050 10 81.7409
8100 10 64.3166
8150 10 50.4788
8200 10 42.8884
8250 10 45.7177
8300 10 64.1865
8350 10 102.809
8400 10 162.824
8450 10 238.987
8500 10 293.145
8550 10 296.558
8600 10 296.455
8650 10 296.052
8700 10 295.237
8750 10 293.681
8800 10 290.586
8850 10 284.219
8900 10 272.095
8950 10 253.921
9000 10 232.804
9050 10 210.982
9100 10 188.931
9150 10 166.73
9200 10 144.657
9250 10 123.038
9300 10 102.203
9350 10 82.6135
9400 10 65.0663
9450 10 51.014
9500 10 43.0545
9550 10 45.3045
9600 10 62.9644
9650 10 100.57
9700 10 159.565
9750 10 235.231
9800 10 292.286
9850 10 296.547
9900 10 296.461
9950 10 296.07
10000 10 295.276
0 15 10
50 15 7.99361
100 15 28.7197
150 15 103.177
200 15 241.229
250 15 298.565
300 15 298.77
350 15 298.834
400 15 298.842
450 15 298.833
500 15 298.816
550 15 298.791
600 15 298.757
650 15 298.71
700 15 298.645
750 15 298.557
800 15 298.435
850 15 298.267
900 15 298.032
950 15 297.695
1000 15 297.195
1050 15 296.416
1100 15 295.12
1150 15 292.782
1200 15 288.25
1250 15 279.488
1300 15 265.091
1350 15 246.43
1400 15 226.173
1450 15 205.493
1500 15 184.62
1550 15 163.817
1600 15 143.547
1650 15 124.381
1700 15 107.08
1750 15 92.8071
1800 15 83.4128
1850 15 81.6923
1900 15 91.2773
1950 15 115.829
2000 15 157.561
2050 15 215.306
2100 15 275.94
2150 15 295.486
2200 15 296.125
2250 15 296.027
2300 15 295.677
2350 15 295.006
2400 15 293.823
2450 15 291.724
2500 15 287.882
2550 15 280.867
2600 15 269.178
2650 15 252.943
2700 15 234.16
2750 15 214.473
2800 15 194.456
2850 15 174.267
2900 15 154.197
2950 15 134.777
3000 15 116.699
3050 15 100.912
3100 15 88.863
3150 15 82.7777
3200 15 85.7824
3250 15 101.528
3300 15 133.08
3350 15 181.361
3400 15 242.34
3450 15 289.724
3500 15 295.853
3550 15 295.965
3600 15 295.728
3650 15 295.196
3700 15 294.233
3750 15 292.535
3800 15 289.46
3850 15 283.808
3900 15 273.962
3950 15 259.318
4000 15 241.26
4050 15 221.859
4100 15 201.894
4150 15 181.748
4200 15 161.612
4250 15 141.827
4300 15 123.153
4350 15 106.426
4400 15 92.7178
4450 15 84.1215
4500 15 83.3646
4550 15 94.0385
4600 15 119.567
4650 15 161.842
4700 15 219.282
4750 15 277.728
4800 15 295.402
4850 15 295.986
4900 15 295.845
4950 15 295.431
5000 15 294.651
5050 15 293.275
5100 15 290.812
5150 15 286.286
5200 15 278.153
5250 15 265.186
5300 15 248.128
5350 15 229.039
5400 15 209.254
5450 15 189.211
5500 15 169.005
5550 15 149.097
5600 15 129.845
5650 15 112.323
5700 15 97.3143
5750 15 86.5885
5800 15 82.5591
5850 15 88.5676
5900 15 108.241
5950 15 144.231
6000 15 196.562
6050 15 258.314
6100 15 293.548
6150 15 295.947
6200 15 295.925
6250 15 295.616
6300 15 294.988
6350 15 293.869
6400 15 291.885
6450 15 288.267
6500 15 281.648
6550 15 270.49
6600 15 254.724
6650 15 236.153
6700 15 216.527
6750 15 196.587
6800 15 176.301
6850 15 156.227
6900 15 136.686
6950 15 118.404
7000 15 102.393
7050 15 89.8727
7100 15 83.0508
7150 15 85.0039
7200 15 99.3358
7250 15 129.231
7300 15 175.882
7350 15 236.065
7400 15 287.27
7450 15 295.777
7500 15 295.969
7550 15 295.758
7600 15 295.258
7650 15 294.346
7700 15 292.736
7750 15 289.835
7800 15 284.489
7850 15 275.089
7900 15 260.839
7950 15 243.024
8000 15 223.671
8050 15 203.799
8100 15 183.683
8150 15 163.511
8200 15 143.721
8250 15 124.927
8300 15 107.936
8350 15 93.9281
8400 15 84.7335
8450 15 83.0681
8500 15 92.4335
8550 15 116.337
8600 15 156.888
8650 15 213.002
8700 15 272.985
8750 15 295.11
8800 15 295.976
8850 15 295.864
8900 15 295.478
8950 15 294.738
9000 15 293.431
9050 15 291.097
9100 15 286.817
9150 15 279.085
9200 15 266.572
9250 15 249.807
9300 15 230.829
9350 15 211.082
9400 15 191.037
9450 15 170.849
9500 15 150.848
9550 15 131.579
9600 15 113.803
9650 15 98.5591
9700 15 87.3813
9750 15 82.5966
9800 15 87.5355
9850 15 105.813
9900 15 140.232
9950 15 191.148
10000 15 252.811
end
set xlabel 'time'
set ylabel 'Sensitivity of Variable to Ki'
plot '-' using 1:2 title 'MKKK_P' with lines, '-' using 1:2 title 'MAPK_PP' with lines
#time MKKK_P
0 0
7.5 0.457051
15 0.898208
22.5 1.32158
30 1.71927
37.5 2.07977
45 2.38967
52.5 2.63491
60 2.80295
67.5 2.88606
75 2.88472
82.5 2.80982
90 2.68199
97.5 2.52671
105 2.36778
112.5 2.22199
120 2.09795
127.5 1.99784
135 1.92072
142.5 1.8633
150 1.8218
157.5 1.79291
165 1.77309
172.5 1.7605
180 1.75356
187.5 1.75104
195 1.75178
202.5 1.75494
210 1.76015
217.5 1.76734
225 1.77711
232.5 1.79129
240 1.81263
247.5 1.84084
255 1.87215
262.5 1.90462
270 1.93775
277.5 1.97138
285 2.00543
292.5 2.03983
300 2.07453
307.5 2.10947
315 2.14459
322.5 2.17985
330 2.2152
337.5 2.25059
345 2.28598
352.5 2.32132
360 2.35657
367.5 2.39168
375 2.4266
382.5 2.4613
390 2.49572
397.5 2.52981
405 2.56353
412.5 2.59682
420 2.62964
427.5 2.66192
435 2.69362
442.5 2.72466
450 2.75499
457.5 2.78455
465 2.81325
472.5 2.84104
480 2.86783
487.5 2.89354
495 2.91808
502.5 2.94137
510 2.96331
517.5 2.98379
525 3.00272
532.5 3.01996
540 3.03541
547.5 3.04894
555 3.06041
562.5 3.06969
570 3.07662
577.5 3.08106
585 3.08284
592.5 3.0818
600 3.07778
607.5 3.0706
615 3.06008
622.5 3.04605
630 3.02834
637.5 3.00679
645 2.98122
652.5 2.95151
660 2.91751
667.5 2.87913
675 2.83628
682.5 2.78891
690 2.73703
697.5 2.68067
705 2.61992
712.5 2.55493
720 2.48592
727.5 2.41315
735 2.33697
742.5 2.25779
750 2.1761
757.5 2.09236
765 2.0072
772.5 1.9212
780 1.83493
787.5 1.74905
795 1.66416
802.5 1.58078
810 1.49947
817.5 1.42069
825 1.34483
832.5 1.2722
840 1.20309
847.5 1.13767
855 1.07601
862.5 1.01823
870 0.964285
877.5 0.914061
885 0.867547
892.5 0.824554
900 0.784908
907.5 0.74846
915 0.715
922.5 0.684324
930 0.656214
937.5 0.630473
945 0.606886
952.5 0.585227
960 0.56531
967.5 0.546926
975 0.529871
982.5 0.513969
990 0.499035
997.5 0.4849
1005 0.471408
1012.5 0.45842
1020 0.445809
1027.5 0.433468
1035 0.421319
1042.5 0.409309
1050 0.397417
1057.5 0.385652
1065 0.374047
1072.5 0.362668
1080 0.351581
1087.5 0.340878
1095 0.330632
1102.5 0.320905
1110 0.311738
1117.5 0.303155
1125 0.295161
1132.5 0.287746
1140 0.280886
1147.5 0.274542
1155 0.268668
1162.5 0.263211
1170 0.258112
1177.5 0.25331
1185 0.248744
1192.5 0.244352
1200 0.240075
1207.5 0.235854
1215 0.231636
1222.5 0.227369
1230 0.223005
1237.5 0.2185
1245 0.213811
1252.5 0.208896
1260 0.203714
1267.5 0.198221
1275 0.192371
1282.5 0.186113
1290 0.179395
1297.5 0.172157
1305 0.164334
1312.5 0.155854
1320 0.146639
1327.5 0.136602
1335 0.125646
1342.5 0.113666
1350 0.100545
1357.5 0.0861544
1365 0.0703523
1372.5 0.0529824
1380 0.033871
1387.5 0.0128287
1395 -0.0103569
1402.5 -0.0359166
1410 -0.0641093
1417.5 -0.0952177
1425 -0.129556
1432.5 -0.167472
1440 -0.209345
1447.5 -0.255598
1455 -0.306693
1462.5 -0.36314
1470 -0.425494
1477.5 -0.494365
1485 -0.570417
1492.5 -0.654365
1500 -0.74699
e
#time MAPK_PP
0 0
7.5 0.000487153
15 0.00380121
22.5 0.0128691
30 0.0309547
37.5 0.0617004
45 0.109414
52.5 0.179472
60 0.278535
67.5 0.414447
75 0.59578
82.5 0.831063
90 1.12781
97.5 1.49171
105 1.92619
112.5 2.43237
120 3.00947
127.5 3.65533
135 4.36706
142.5 5.14117
150 5.97391
157.5 6.86128
165 7.79877
172.5 8.78116
180 9.80109
187.5 10.8478
195 11.9007
202.5 12.9177
210 13.7906
217.5 14.22
225 13.45
232.5 10.2001
240 4.57966
247.5 1.16188
255 0.287581
262.5 0.116204
270 0.0825271
277.5 0.0741982
285 0.0704025
292.5 0.0675083
300 0.0648891
307.5 0.0623493
315 0.0598708
322.5 0.0574697
330 0.0551535
337.5 0.0529253
345 0.0507849
352.5 0.048741
360 0.0468065
367.5 0.0449958
375 0.0433225
382.5 0.0417976
390 0.0404314
397.5 0.039232
405 0.0382028
412.5 0.0373451
420 0.0366583
427.5 0.0361413
435 0.0357927
442.5 0.0356085
450 0.0355859
457.5 0.0357219
465 0.0360136
472.5 0.0364593
480 0.0370582
487.5 0.037811
495 0.0387178
502.5 0.0397801
510 0.0410021
517.5 0.0423857
525 0.0439351
532.5 0.0456558
540 0.047553
547.5 0.0496335
555 0.0519042
562.5 0.0543742
570 0.0570524
577.5 0.0599495
585 0.0630795
592.5 0.0664561
600 0.0700952
607.5 0.0740139
615 0.0782316
622.5 0.0827682
630 0.0876427
637.5 0.0928777
645 0.098498
652.5 0.104531
660 0.111003
667.5 0.117955
675 0.12543
682.5 0.133474
690 0.142143
697.5 0.1515
705 0.161617
712.5 0.172532
720 0.184318
727.5 0.197043
735 0.210819
742.5 0.225732
750 0.241897
757.5 0.259472
765 0.278612
772.5 0.299499
780 0.322336
787.5 0.347357
795 0.374813
802.5 0.40497
810 0.438136
817.5 0.474708
825 0.515131
832.5 0.559876
840 0.609633
847.5 0.665136
855 0.727225
862.5 0.796785
870 0.8749
877.5 0.96278
885 1.06197
892.5 1.17406
900 1.30094
907.5 1.44504
915 1.60885
922.5 1.7953
930 2.00774
937.5 2.24991
945 2.52582
952.5 2.83978
960 3.19667
967.5 3.60041
975 4.05476
982.5 4.5641
990 5.12987
997.5 5.7534
1005 6.43515
1012.5 7.16866
1020 7.94808
1027.5 8.76354
1035 9.60068
1042.5 10.4413
1050 11.2674
1057.5 12.0573
1065 12.7945
1072.5 13.4623
1080 14.0514
1087.5 14.5577
1095 14.9829
1102.5 15.333
1110 15.6176
1117.5 15.8463
1125 16.029
1132.5 16.1748
1140 16.2916
1147.5 16.3866
1155 16.4657
1162.5 16.5338
1170 16.5945
1177.5 16.6504
1185 16.7032
1192.5 16.7538
1200 16.8029
1207.5 16.8503
1215 16.8956
1222.5 16.938
1230 16.9767
1237.5 17.011
1245 17.0401
1252.5 17.064
1260 17.0823
1267.5 17.0953
1275 17.103
1282.5 17.1058
1290 17.1039
1297.5 17.0978
1305 17.0877
1312.5 17.0741
1320 17.0572
1327.5 17.0372
1335 17.0143
1342.5 16.9887
1350 16.9605
1357.5 16.9297
1365 16.8965
1372.5 16.8607
1380 16.8223
1387.5 16.7814
1395 16.7376
1402.5 16.6911
1410 16.6415
1417.5 16.5887
1425 16.5324
1432.5 16.4725
1440 16.4085
1447.5 16.3402
1455 16.2673
1462.5 16.1892
1470 16.1054
1477.5 16.0156
1485 15.919
1492.5 15.8149
1500 15.7027
e
Try 3 integrations with selected parameters/ICs!
Reading in linear objective function from: 'MAPK.linobjfun'
Demonstration of forward/adjoint sensitivity (near) equivalence.
Integration time was 0.02
Param default: K1
1000 0.0429612 -0.0429612 4.9597 -0.842488 -4.11721 0.580189 1.01415 -1.59434
### Printing Forward Sensitivities
Expression for integrand of linear objective J:
0-th component: 0
1-th component: MKKK_P
2-th component: 0
3-th component: 0
4-th component: 0
5-th component: 0
6-th component: 0
7-th component: 0
dJ/dp_0=-634.93349577416 dJ/dp_1=-31733.967696185 dJ/dp_2=-6523.68261291375 dJ/dp_3=82226.3759815664
Integration time was 0.01
0 -17537.7 -13780.5 75.0971 5877.82 17233.3 634.933 6523.68 16742.8
### Printing Adjoint Sensitivities: int_0^T <df/dp, psi> dt
dJ/dp_0=-634.933386367069 dJ/dp_1=-31733.962854391 dJ/dp_2=-6523.67815618698 dJ/dp_3=82226.3648038688
############# DONE RUN NUMBER 0 #############
Integration time was 0.02
Param default: K1
1000 0.0429612 -0.0429612 4.9597 -0.842488 -4.11721 0.580189 1.01415 -1.59434
### Printing Forward Sensitivities
Expression for integrand of linear objective J:
0-th component: 0
1-th component: MKKK_P
2-th component: 0
3-th component: 0
4-th component: 0
5-th component: 0
6-th component: 0
7-th component: 0
dJ/dp_0=-634.93349577416 dJ/dp_1=-31733.967696185 dJ/dp_2=-6523.68261291375 dJ/dp_3=82226.3759815664
Integration time was 0.01
0 -17537.7 -13780.5 75.0971 5877.82 17233.3 634.933 6523.68 16742.8
### Printing Adjoint Sensitivities: int_0^T <df/dp, psi> dt
dJ/dp_0=-634.933386367069 dJ/dp_1=-31733.962854391 dJ/dp_2=-6523.67815618698 dJ/dp_3=82226.3648038688
############# DONE RUN NUMBER 1 #############
Reading in nonlinear objective now: 'MAPK.objfun'
Integration time was 0.01
Param default: K1
1000 0.0429612 -0.0429612 4.9597 -0.842488 -4.11721 0.58019 1.01415 -1.59434
### Printing Objective Function (since nonlinear objective is present)
Computed J=1101957.53407543
Integration time was 0.01
0 -17537.7 -13780.5 75.0971 5877.82 17233.3 634.933 6523.68 16742.8
### Printing Adjoint Sensitivities: int_0^T <df/dp, psi> dt
dJ/dp_0=-634.933341444755 dJ/dp_1=-31733.9617474301 dJ/dp_2=-6523.67865838288 dJ/dp_3=82226.3627068851
############# DONE RUN NUMBER 2 #############
Integration time was 0.02
Param default: K1
1000 0.0429612 -0.0429612 4.9597 -0.842488 -4.11721 0.58019 1.01415 -1.59434
### Printing Objective Function (since nonlinear objective is present)
Computed J=1101957.53407543
Integration time was 0.01
0 -17537.7 -13780.5 75.0971 5877.82 17233.3 634.933 6523.68 16742.8
### Printing Adjoint Sensitivities: int_0^T <df/dp, psi> dt
dJ/dp_0=-634.933341444755 dJ/dp_1=-31733.9617474301 dJ/dp_2=-6523.67865838288 dJ/dp_3=82226.3627068851
############# DONE RUN NUMBER 3 #############
Sensitivity analysis for 2 ICs: MAPK, MAPK_P
2 parameters: K1, Ki
Reading in objective 'MAPK_10pt.objfun' ... and data 'MAPK_10pt.dat'
Forward integration time was 0.04
### Printing Objective Value:
Computed J=70.0263887910541
Adjoint integration time was 0.02
### Printing Adjoint Sensitivities:
dJ/dp_0=-16.3541319695685 dJ/dp_1=-63.2350028482403 dJ/dp_2=-178.317002160688 dJ/dp_3=562.530836051654
Reading in objective 'MAPK_100pt.objfun' ... and data 'MAPK_100pt.dat'
Forward integration time was 0.03
### Printing Objective Value:
Computed J=60.5101718370479
Adjoint integration time was 0.13
### Printing Adjoint Sensitivities:
dJ/dp_0=-13.6035197640958 dJ/dp_1=-54.7301626955854 dJ/dp_2=-154.049545206639 dJ/dp_3=492.63125662099
Reading in objective 'MAPK_1000pt.objfun' ... and data 'MAPK_1000pt.dat'
Forward integration time was 0.08
### Printing Objective Value:
Computed J=59.5984145510258
Adjoint integration time was 0.79
### Printing Adjoint Sensitivities:
dJ/dp_0=-13.3395624513431 dJ/dp_1=-53.9121085425118 dJ/dp_2=-151.720316270095 dJ/dp_3=485.906562152899
Reading in objective 'MAPK_10000pt.objfun' ... and data 'MAPK_10000pt.dat'
Forward integration time was 0.33
### Printing Objective Value:
Computed J=59.5065264742483
Adjoint integration time was 5.33
### Printing Adjoint Sensitivities:
dJ/dp_0=-13.3141354696291 dJ/dp_1=-53.8306524232969 dJ/dp_2=-151.48539777297 dJ/dp_3=485.230044271633
## Integration Parameters:
## mxstep = 1000000 rel.err. = 1e-08 abs.err. = 1e-08
## CVode Statistics:
## nst = 4324 nfe = 4793 nsetups = 948 nje = 79
## nni = 4789 ncfn = 0 netf = 0
##
## CVode Adjoint Sensitivity Statistics:
## nstA = 0 nfeA = 0 nsetupsA = 0 njeA = 1
## nniA = 0 ncfnA = 0 netfA = 0
## ncheck = 0
==== NOW TREATING DATA AS BEING CONTINUOUS ====
Reading in objective 'MAPK_withData.objfun' ... and data 'MAPK_10pt.dat'
Forward integration time was 0.03
### Printing Objective Value:
Computed J=59.500845092995
Adjoint integration time was 0.07
### Printing Adjoint Sensitivities:
dJ/dp_0=-12.9310569637138 dJ/dp_1=-53.4894850465344 dJ/dp_2=-151.324024491527 dJ/dp_3=483.916030588705
Reading in objective 'MAPK_withData.objfun' ... and data 'MAPK_100pt.dat'
Forward integration time was 0.03
### Printing Objective Value:
Computed J=59.494020134826
Adjoint integration time was 0.07
### Printing Adjoint Sensitivities:
dJ/dp_0=-13.3126884956064 dJ/dp_1=-53.8194833622848 dJ/dp_2=-151.458430980402 dJ/dp_3=485.146784282578
Reading in objective 'MAPK_withData.objfun' ... and data 'MAPK_1000pt.dat'
Forward integration time was 0.1
### Printing Objective Value:
Computed J=59.4972886156776
Adjoint integration time was 0.26
### Printing Adjoint Sensitivities:
dJ/dp_0=-13.3106141304181 dJ/dp_1=-53.8213788234574 dJ/dp_2=-151.462605950866 dJ/dp_3=485.161708829886
Reading in objective 'MAPK_withData.objfun' ... and data 'MAPK_10000pt.dat'
Forward integration time was 0.61
### Printing Objective Value:
Computed J=59.4968267017486
Adjoint integration time was 1.27
### Printing Adjoint Sensitivities:
dJ/dp_0=-13.3111738335549 dJ/dp_1=-53.8213009644168 dJ/dp_2=-151.462027808996 dJ/dp_3=485.159981659233
## Integration Parameters:
## mxstep = 1000000 rel.err. = 1e-08 abs.err. = 1e-08
## CVode Statistics:
## nst = 20192 nfe = 22110 nsetups = 4052 nje = 358
## nni = 22106 ncfn = 0 netf = 0
##
## CVode Adjoint Sensitivity Statistics:
## nstA = 22175 nfeA = 25407 nsetupsA = 1617 njeA = 373
## nniA = 25403 ncfnA = 0 netfA = 330
## ncheck = 0
First try a few integrations with default sensitivity !
MKKK MKKK_P MKK MKK_P MKK_PP MAPK MAPK_P MAPK_PP J0 J1 J2 J3 J4 J5 J6 J7 J8 J9 uVol V1 Ki K1
Run #0:
30 61.6227 38.3773 273.234 16.6896 10.0766 280.05 12.4867 7.4635 1.17585 0.206875 0.909503 0.505293 0.301375 0.394993 0.239109 0.114441 0.166125 0.227141 1 2.5 9 10
finished.
Run #1:
30 61.6227 38.3773 273.234 16.6896 10.0766 280.05 12.4867 7.4635 1.17585 0.206875 0.909503 0.505293 0.301375 0.394993 0.239109 0.114441 0.166125 0.227141 1 2.5 9 10
finished.
Run #2:
30 61.6227 38.3773 273.234 16.6896 10.0766 280.05 12.4867 7.4635 1.17585 0.206875 0.909503 0.505293 0.301375 0.394993 0.239109 0.114441 0.166125 0.227141 1 2.5 9 10
finished.
Now Activate Sensitivity
Default case 1: sensitivities calculated for all constants
of the input SBML, using analytic matrices:
Sensitivities to parameter K1:
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
Selected parameter case 1, analytic matrices.
Sensitivities to variable MAPK_PP
30 1.49687 -1.49687 0.519736 -0.252619 -0.267117 0.106138 0.235909 0.657952
Sensitivities to parameter K1
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
Sensitivities to variable MKKK_P
30 0.097418 0.902582 -0.604668 0.30265 0.302018 -0.100022 0.05475 0.0452716
Sensitivities OF variable MKKK to above parameters and variables
30 61.6227 1.49687 0.37616 0.097418
Free Jacobian matrix via ODEModel_free(om) and supress
use of Jacobian matrix via CvodeSettings_setJacobian(set, 0)
Consequently the parametric matrix can't be used.
and instead CVODES internal approximation is active.
Selected parameter case 2, internal approximation of matrices.
Sensitivities to variable MAPK_PP
30 1.49687 -1.49687 0.519736 -0.252619 -0.267117 0.106138 0.235909 0.657952
Sensitivities to parameter K1
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
Sensitivities to parameter V1
30 -12.725 12.725 -4.25 2.06298 2.18701 -0.478912 0.259318 0.219594
Sensitivities to variable MKKK
30 0.948959 0.0510409 -0.0149958 0.00724728 0.00774854 -0.00159069 0.000860227 0.00073046
Sensitivities OF variable MKKK to above parameters and variables:
30 61.6227 0.37616 1.49687 0.948959 -12.725
Default case 2, switching between approx. and analytic
Switching back to analytic matrices and default sensitivity.
Param K1:
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
30 0.37616 -0.37616 0.118512 -0.0574118 -0.0611 0.0129955 -0.00703267 -0.00596284
Now again with internal approximation of matrices:
30 0.37616 -0.37616 0.11851 -0.0574109 -0.0611001 0.0129953 -0.00703294 -0.00596272
30 0.37616 -0.37616 0.11851 -0.0574109 -0.0611001 0.0129953 -0.00703294 -0.00596272
30 0.37616 -0.37616 0.11851 -0.0574109 -0.0611001 0.0129953 -0.00703294 -0.00596272
30 0.37616 -0.37616 0.11851 -0.0574109 -0.0611001 0.0129953 -0.00703294 -0.00596272
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