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A physiologically based mechanistic modeling approach integrating data from aqueous solutions, U87 glioma cells, mice, and cancer patients was utilized to characterize the brain disposition of temozolomide (TMZ), the cornerstone of chemotherapy against glioblastoma multiforme.
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We interrogated diet-induced epigenomic, transcriptomic, proteomic, and metabolomic alterations using high-throughput omic methods and used a network modeling approach to integrate these diverse molecular signals.
To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles.
The proposed method utilizes a coupled suspension-bridge modelling approach, which integrates an analytical form-finding method with the conventional finite element (FE) model to enhance the FE modelling efficiency during the optimization process.
A spatial resource allocation modeling approach has been developed by integrating some earlier study efforts.
The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states.
To solve the proposed model, a solution approach integrating a sample average approximation method with a simulated annealing-based heuristic algorithm was developed.
Due to its formulation in terms of abstract application programming interfaces, the LWDF methodology can be integrated with a wide variety of simulation- and implementation-oriented languages, and can be targeted across different platforms, which allows engineers to integrate dataflow modeling approaches relatively easily into existing design processes.
The panel of rice varieties was screened for the presence of sub-populations using a model-based approach integrated by neighbor joining phylogenetic and Principal Coordinate (PCoA) analyses.
Applying a model-to-model transformation, our approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques.
We modelled the E. coli datasets by using the ensemble approach integrating both correlation and ratio procedures as described above.
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