ReviewBiomolecules2026
Current Progress and Future Outlook for Synthetic Gene Circuits in Cardiovascular Therapy.
Review in Biomolecules, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite decades of therapeutic advances, cardiovascular diseases remain the leading cause of global mortality, underscoring the need for strategies that move beyond untargeted systemic pharmacotherapy. Synthetic biology introduces a programmable therapeutic paradigm in which engineered gene circuits can sense, compute, and respond to pathological signals with spatiotemporal precision. This review examines the current progress of synthetic gene circuits for cardiovascular therapy, organized across three domains of clinical relevance. The first domain comprises circuits engineered for direct cardiac applications, from inducible switches to classifier systems. This discussion is further expanded by exploring circuits that indirectly target cardiovascular disease; these circuits address upstream risk factors such as cholesterol dysregulation and chronic inflammation. Looking ahead, the focus shifts to orthogonal architectures pioneered in other therapeutic contexts that hold promise for future cardiac applications. This review further discusses the emerging role of computational tools, including gene regulatory network inference and foundation models, in accelerating target discovery. Finally, a modified Design-Build-Test-Learn framework is proposed to overcome translational bottlenecks, thus paving the way for next-generation cardiac therapeutics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.