Two kinds of moments, i.e. the point value (PV) and the volume-integrated average (VIA) are defined and independently updated in the present model by different numerical formulations.
The overall metabolic network reconstruction workflow used to establish the three networks is depicted in Figure 2. First, the human genome sequence and annotation was updated in the SimPheny™ modeling platform (Genomatica, Inc).
Driving the reservoir The hidden states S hidden(m, k) is updated in the light of two model parameters {W res (k), W in k)}.
Thus, the use of the Glass model, based on piece-wise linear differential equations, reveals that the time ordering in the emergence of the cell-fate patterns is not an artifact of the synchronous updating in the Boolean model; however, the stamen and carpel peaks are reversed in time between the Boolean and Glass models (Figure 3 vs. Figure 6A).
Thus, time ordering in the emergence of cell-fate patterns is not an artifact of synchronous updating in the Boolean model.
To be more explicit, the updating in the sequential model works as follows.
In the following, the mass and stiffness matrices are updated in the algorithm of the proposed iterative model updating method using the structural discrepancy matrices obtained from the LSMR technique.
Compared to previously published cost-effectiveness models [ 22- 26], our modeling study made several updates in the model structure and inputs.
This is equivalent to the infinite variance approach set in Olsen et al. (2007) and therefore requires several iterations of the optimization process until further updates in the model parameters become insignificant.
It is our hope that the present model serves to focus translational research on issues that may directly impact patient care, and that the resulting activity will lead to updates in the model.
