Model systems are simplified, experimentally tractable representations of biological processes used to investigate mechanisms of health and disease and to predict how interventions will behave before they reach patients. They span a continuum of complexity, from molecular and biochemical assays through cultured cell…
Model systems are simplified, experimentally tractable representations of biological processes used to investigate mechanisms of health and disease and to predict how interventions will behave before they reach patients. They span a continuum of complexity, from molecular and biochemical assays through cultured cell lines, primary cells, and tissue constructs, to whole-organism models in invertebrates and vertebrates, as well as computational and in silico models. Each level trades fidelity against control and throughput. In vitro disease models reconstruct features of a pathology in a dish, allowing mechanistic dissection and screening; they play a growing role in preclinical safety assessment, for instance in cardiotoxicity testing where human-relevant systems can flag adverse effects earlier. Integrated drug-design pipelines combine in silico prediction, in vitro validation, and in vivo confirmation to evaluate efficacy and toxicity across scales. Comparative and evolutionary studies of conserved genes and proteins across species likewise rely on model organisms to infer function from sequence and structure. Choosing an appropriate model requires matching its biology to the question, acknowledging the limits of extrapolation between systems and to humans. Well-validated model systems underpin reproducible research, reduce reliance on animal testing where alternatives suffice, and accelerate the translation of discoveries into therapies.
7 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 7 articles above have been cited 6 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
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