An important aspect of our present study was the systems approach that we adopted, which integrated extensive experimentation with graph theoretical analysis and mathematical modeling.
We will assemble and integrate remote online experimentation with other multimedia learning resources like virtual reality and simulations, and develop course modules utilising the complementary learning effects of physical experiments.
In line with this call to integrate modeling and experimentation, we present a new systems biology modeling approach that has already enabled a significant model-driven experimental finding, which we present as part of this work.
Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action.
Therefore, the information gathered from these studies must be appropriately integrated and organized to facilitate experimentation on biomarkers.
As an example, we have begun to construct mathematical models of human myometrial function that integrate biophysical details from current experimentation and that in the literature [e.g. [ 14]].
The approach is centred around socio-organisational strengthening and farmer learning through experimentation and discovery, both integrated into a coherent intervention process design.
The central themes of the research projects integrated quantitative molecular biology, modeling, and experimentation at multiple scales.
In this paper, we describe the necessary requirements to integrate external Web-based applications for remote experimentation into eLogbook.
Once combinatorial synthesis, high-throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems.
