A few algorithms, e.g., COACH [ 17], CORE [ 18], MCL-CA [ 13] and CACHET [ 19], have employed this model to predict biologically meaningful complexes.
Continuous measurement of sand levels is an example of a geomorphic technique that will help to develop models that predict biologically meaningful processes, not just extremes.
We ask under what conditions a stochastic equivalent of the deterministic model still presents wave-pinning, i.e. after triggering the formation of sustained regions within the cell with respectively low and high levels of the active form, and if our approach predicts biologically relevant conditions under which wave-pinning may be unsustainable in live, noisy cells.
Both methods are applied to similarity searching of chemical databases and are incorporated into SVM models to efficiently predict biologically active compounds.
When tested with a dynamic, drought prone environment, the model predicts viability levels that are biologically plausible.
Using model parameters that are biologically plausible, the Model predicts oscillation periods of 3.5 to 5.2 hrs – well within the reported range of experimental values [3].
We find that the mathematical model predicts regulatory relationships present in the biologically-derived network with a high degree of accuracy, and that it predicts more features of the biological network than does a knowledge-driven model derived from the same data set.
A good model enables us to predict biologically relevant quantities of protein motion accurately and efficiently.
The model predicts that the hrp regulon is a biologically stable two-state system, with each of the stable states being strongly attractive, a feature indicative of selection for a tightly regulated and responsive system.
Further, when the robustness of steady states of the T-helper and T-cell activation networks is analyzed, we show that the SIN model predicts low robustness properties in both cases whereas the SIF model predicts more biologically relevant behavior with higher robustness.
