Table 2.
Independent parameter estimates and properties of their models
| #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | CV | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Optimization algorithmsa | SRES | SRES HJ | PS | SRES HJ Praxis | GA SRES | SRES | SRES GA GASR | SRES | GA HJ | SRES | |
| Sum squares | 0.01906 | 0.01906 | 0.01908 | 0.01909 | 0.01918 | 0.01917 | 0.01937 | 0.01943 | 0.01970 | 0.01975 | |
| kInDuo (d -1 ) | 0.236 | 0.251 | 0.322 | 0.131 | 0.250 | 0.0690 | 0.158 | 0.134 | 0.0381 | 0.0347 | 61.1% |
| kInLiver (d -1 ) | 9.40 | 9.99 | 12.7 | 5.35 | 10.3 | 2.98 | 6.83 | 5.72 | 1.69 | 1.55 | 58.3% |
| kInRBC (d -1 ) | 1.09 | 1.09 | 1.10 | 1.09 | 1.08 | 1.08 | 1.04 | 1.07 | 1.19 | 1.20 | 4.64% |
| kInRest (d -1 ) | 18.3 | 19.4 | 24.7 | 10.4 | 22.0 | 6.16 | 13.9 | 11.3 | 3.20 | 2.95 | 58.6% |
| kInBM (d -1 ) | 49.5 | 52.7 | 67.1 | 28.1 | 58.8 | 15.8 | 35.3 | 29.7 | 9.07 | 8.26 | 59.1% |
| kDuoLoss (d -1 ) | 5.00.10-4 | 5.45.10-4 | 5.05.10-4 | 9.29.10-4 | 0.00359 | 0.0270 | 0.0313 | 9.38.10-4 | 0.00150 | 0.00150 | 173% |
| kRestOut (d -1 ) | 0.0238 | 0.0239 | 0.0239 | 0.0238 | 0.0234 | 0.0236 | 0.0234 | 0.0236 | 0.0236 | 0.0234 | 0.851% |
| kBMSpleen (d -1 ) | 0.173 | 0.173 | 0.183 | 0.143 | 0.0453 | 0.0619 | 0.0680 | 0.0900 | 0.0585 | 0.0567 | 53.3% |
| VDuoNTBI (mol d -1 ) | 2.03 | 11.4 | 3.56 | 0.792 | 0.910 | 0.200 | 0.200 | 0.901 | 0.0978 | 0.566 | 167% |
| VLiverNTBI (mol d -1 ) | 0.551 | 3.15 | 0.982 | 0.203 | 0.0946 | 0.0261 | 0.0295 | 0.204 | 0.00847 | 0.0937 | 181% |
| VSpleenNTBI (mol d -1 ) | 35.5 | 202 | 63.9 | 12.6 | 4.86 | 1.342 | 1.49 | 10.9 | 0.434 | 3.89 | 185% |
| VRestNTBI (mol d -1 ) | 0.350 | 2.01 | 0.634 | 0.121 | 0.0212 | 0.0109 | 0.0128 | 0.102 | 0.00119 | 0.0114 | 191% |
| vRBCSpleen (d -1 ) | 0.0833 | 0.0831 | 0.0870 | 0.0706 | 0.0132 | 0.0235 | 0.0283 | 0.043 | 0.0209 | 0.0190 | 64.4% |
| Km (M) | 0.239 | 1.38 | 0.412 | 0.100 | 0.128 | 0.0159 | 0.0145 | 0.119 | 0.00850 | 0.117 | 164% |
| vDiet (M d -1 ) | 0.0042 | 0.0042 | 0.0042 | 0.0042 | 0.00318 | 0.00377 | 0.00397 | 0.0042 | 0.0042 | 0.0042 | 8.25% |
| kNTBI_Fe1Tf (M -1 d -1 ) | 5.107 | 5.107 | 5.107 | 5.107 | 4.98.106 | 1.08.109 | 5.25.108 | 5.107 | 5.107 | 5.107 | 176% |
| kFe1Tf_Fe2Tf (M -1 d -1 ) | 5.107 | 5.107 | 5.107 | 5.107 | 10.1 | 1.08.109 | 5.25.108 | 5.107 | 5.107 | 5.107 | 177% |
| TF saturation (%) | 46.6 | 43.7 | 36.0 | 70.0 | 7.15 | 47.0 | 25.1 | 43.7 | 70.0 | 70.0 | |
| Total Fe (mg) | 2.01 | 2.01 | 2.00 | 2.04 | 2.20 | 2.20 | 2.20 | 2.20 | 2.20 | 2.20 | |
| RBC trans. Time (d) | 12.0 | 12.0 | 11.5 | 14.2 | 75.9 | 42.5 | 35.3 | 23.3 | 47.9 | 52.6 |
Model #6 was chosen due to its RBC transition time and Tf saturation being closer to known values in mice. Coefficient of variation (CV) is expressed for each of the estimated parameters across all independent estimates
aKey to optimization algorithms: SRES Evolution strategy with Stochastic Ranking, HJ Hooke & Jeeves, PS Particle Swarm, GA Genetic Algorithm, GASR Genetic Algorithm with stochastic ranking