Performance of the models evaluated against the actual data reveals that the models formulated from genetic programming outperform the multi-adaptive regression splines models.
Therefore, the models learned from genetic data should always be tested on independent data not used for training the model.
Besides, demographic models inferred from genetic data complement archeological evidence in understanding pre-historical events, such as number and timing of major continental fluctuations of population size as well as migration.
The histological presentation of overtly normal vascular structure, but with ultrastructural evidence of abnormal development of collagen fibrils, is consistent with similar models resulting from genetic manipulation of genes encoding different forms of collagen.
Mouse models from different genetic backgrounds often display variable phenotypes.
Multienvironment models can benefit from genetic correlations between environments (Burgueño et al. 2012).
The scope of such models can range from genetic circuits [ 1, 2], to organelles [ 1], to whole cells [ 2, 3], to whole organs [ 4].
This distinguishes the Snord116del mouse model from the genetic mouse models of obesity, including ob/ob, db/db, Sim1 haploinsufficiency [61], Mc4r deletion [62] and others [63] that lack proper homeostasis control.
To assess the robustness of the proposed test, we also simulate data from genetic models other than the GGM: over-dominant model (Λ1 = 1.5, Λ2 = 1.4 ) and under-dominant model (Λ1 = 0.9, Λ2 = 1.4).
Testing this designed organisms can be achieved by using controlled bioreactor models, with single and heterogeneous populations, and accurate computational models including different scales (from genetic constructs and metabolic pathways to population dynamics).
