ArticleACS synthetic biology2023
Bayesian Optimization for Design of Multiscale Biological Circuits.
Article in ACS synthetic biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- GenAI-Net: A generative AI framework for automated biomolecular network design.Science advances · 2026Article
- Applications of synthetic biology in biomedicine.Molecular biomedicine · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- The renaissance ofFrontiers in immunology · 2026Review
- Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.Computational and structural biotechnology journal · 2026Article
- Exploring single-cell biosynthetic noise and dynamics for enhanced betaxanthin production in Escherichia coli.Nature communications · 2025Article
- Rules of life at the interface of calcium signaling and mechanobiology.APL bioengineering · 2025Review
- Current status and future perspectives of multi-modal bacteria-based cancer therapies.Clinical and translational medicine · 2025Review
- Integration of Bayesian optimization and solution thermodynamics to optimize media design for mammalian biomanufacturing.iScience · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Accelerating cell culture media development using Bayesian optimization-based iterative experimental design.Nature communications · 2025Article
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- Optimisation of surfactin yield inComputational and structural biotechnology journal · 2024Article
- Machine learning framework to extract the biomarker potential of plasma IgG N-glycans towards disease risk stratification.Computational and structural biotechnology journal · 2024Article
- Applications of artificial intelligence and machine learning in dynamic pathway engineering.Biochemical Society transactions · 2023Review
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Authors and funding
3 authors.
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Abstract
Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient
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