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These constraint-based modelling approaches allow integration of high-throughput post-genomic data but describe steady states and generally offer no information about metabolite concentrations or the temporal dynamics of the system [18, 19, 20].
These modelling approaches allow the risk factor effects varying with c and the disease level at examination j - 1.
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The flexibility of this stochastic modelling approach allowed for the accounting of fundamental differences between model compounds and the moieties within the real systems they are meant to mimic.
The fuzzy logic modelling approach allows designing the monitoring scheme using the reduced number of available information.
The modelling approach allows the design team to split the work into several parts (concurrent engineering) and validate each part independently.
This generic modelling approach allows an easy-to-use and effective representation of the process by a synergic use of available technologies.
Linking several cells using this modelling approach allows the performance of flotation circuits to be determined, and, together with a suitable optimisation algorithm, for the optimal layout of the circuit to be established.
The modelling approach allows for inspection of a range of posterior distribution statistics.
The additive modelling approach allows investigating each covariate effect non-parametrically, i.e. considering each effect non-constant through time.
Such a flexible modelling approach allows joint analysis of nonlinear and fixed determinants either at group or individual level, as well as observed and unobserved determinants, which may exhibit spatial variation.
The analysis of multivoltinism within a social context [ 43, 49] illuminates particular aspects of the first potential transition between non-social reproduction to discrete broods while our modelling approach allows one to determine the necessary conditions for the evolution of continuous reproduction.
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