Synthetic Biology and AI for Next Generation Stem Cell Research
A special issue of SynBio (ISSN 2674-0583).
Deadline for manuscript submissions: 31 August 2026 | Viewed by 301
Special Issue Editor
Interests: biomedical engineering; quantitative and synthetic biology; bioinformatics and computational biology; biocomplexity and theoretical biology
Special Issue Information
Dear Colleagues,
Synthetic biology is transforming stem cell research by enabling programmable control of cell state, behavior, and tissue architecture. Recent advances in genome/epigenome editing, RNA regulation, and synthetic gene circuits make it possible to dissect mechanisms of pluripotency and differentiation, improve the robustness and scalability of cell manufacturing, and accelerate translation to regenerative medicine. Meanwhile, single-cell and spatial multiomics, high-content imaging, and lineage recording generate data at a scale that benefits from AI-driven design–build–test–learn cycles. Together, these developments position synthetic biology as a key driver for next-generation, quantitatively guided stem cell engineering.
This Special Issue aims to highlight innovations that integrate synthetic biology with stem cell science—supported by AI and computational biology—to advance mechanistic insight, controllable differentiation, and therapeutic development.
We invite original articles and forward-looking reviews on programmable CRISPR platforms, safe-harbor landing pads, recombinase logic, and RNA-guided transcriptional/translational control. Additional topics include state-sensing and closed-loop circuits; opto-/chemogenetic actuators; synthetic morphogen gradients; mechanosensitive modules coupled to biomaterials and microphysiological systems; and safety and manufacturing (genomic stability, kill switches, immunoevasion, and nonviral delivery such as LNPs, eVLPs, and transposons). Computational submissions are encouraged, including generative promoter/circuit design, protein/ligand design for niche signaling, reinforcement learning for differentiation protocols, multiscale organoid modeling, and “digital twins” built from multiomics, imaging, and lineage tracing.
Dr. Guanyu Wang
Guest Editor
Manuscript Submission Information
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Keywords
- synthetic biology
- stem cells
- genome/epigenome editing
- gene circuits
- artificial intelligence
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