Sentence examples for how computational models from inspiring English sources

Finally we suggest how computational models of biochemical networks can be employed in metabolic engineering design.

It also reiterates how computational models can be exceptionally useful for gaining insight even though, or because, they may fail in reproducing certain facets of reality.

Our aim was to demonstrate how computational models can be developed using current knowledge of the system to highlight important gaps in knowledge, to test new hypotheses, and to predict outcomes for different therapeutic approaches.

In conclusion, this study has shown how computational models can be useful tools for investigating and comparing the biological behaviour of signal transduction pathways as they can suggest new hypotheses to explain the observed biological data and help understand the dynamics of how the pathway functions.

We report how computational modelling predicted the binding mode of ligands of special interest to the Chk1 ATP site, for representatives of an indazole series and debromohymenialdisine.

Together, our study demonstrates how computational modeling can guide experimental design to further understand a specific metabolic signaling pathway during viral infection in a mammalian system.

Recent reviews have highlighted how computational modelling and simulation can help in the investigation of arrhythmias and antiarrhythmic therapy.

These examples demonstrate how computational modeling of IEMs could provide biochemical explanations for adaptive mutations in alternative pathways and for phenotypic variability within the same IEM groups.

This paper shows how the computational model, which simulates the coordinated movements of human-like bipedal locomotion, can be evolutionarily generated without elaboration of manual coding.

We have demonstrated how a computational model of L2/3 barrel cortex can develop a map of whisker deflection direction that is a strong qualitative match to that measured in the rat barrel cortex by Andermann & Moore [8].

Here we will discuss how we interpret measurements of cell death, how cell-to-cell variability impacts it, and how we build computational models to understand and predict cell death responses.