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Currently available drug-inducible model systems usually rely on the transactivation of artificial promoters by chimeric transcription factors.
The shared set of laboratory model systems (usually using microbes) and concerns for experimental design again yield common ground between conservation and medical fields.
Complex model systems usually do not allow for a large number of samples, but robustly learning network structure with few samples is difficult [ 7, 8].
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The functions of PPARγ in different model systems are usually interpreted based on the agonist action of synthetic ligands.
Channel models for outdoor wireless systems usually assume two-dimensional (2D) random scattering media.
Motivation: Mathematical models of complex biological systems usually consist of sets of differential equations which depend on several parameters which are not accessible to experimentation.
The models that try to describe these systems usually fix the spatial and temporal scale and use the mathematical or computational formalism.
In addition, current cloud storage systems usually adopt a key-value model to organize data to retrieve data efficiently.
Typical results of interdisciplinary efforts in cancer systems biology are models with various granularities describing the studied biological systems, usually tumor cells, cellular processes, or signaling pathways.
Model-based methods start by formulating a computational model of the system, usually in the form of differential or difference equations and recast the network inference problem as learning the parameters of such a model.
Biological systems are usually much more complex than most of physicists' beloved model systems and require a sound understanding of biological details.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com