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February 12, 2026Proceedings of the National Academy of Sciences0 citations

Optogenetic control of transition to metamorphosis

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CWC H WangDFDenis F. FaerbergYSYuka Sekine

Key Points

  • The aim is to establish a predictive model for metamorphosis commitment in Drosophila and investigate neuroendocrine circuit functions.
  • Applied system identification approaches to model metamorphosis.
  • Utilized optogenetics to perturb a signaling node in endocrine glands.
  • Analyzed commitments in otherwise undisturbed larvae.
  • Developed a predictive model that accurately forecasts larval commitment to metamorphosis.
  • Confirmed the role of neuroendocrine circuits in assessing growth and triggering metamorphosis.

Abstract

System identification approaches are commonly used in engineering to infer simple yet predictive models of complex systems from their responses to time-dependent perturbations. Here, we apply this strategy at the whole organism scale, establishing a predictive model of commitment to metamorphosis in Drosophila . At this critical point in animal development, the larva stops feeding and proceeds to take on the adult form. The neuroendocrine circuits governing commitment to metamorphosis assess the growth and patterning programs, eventually triggering the production of systemic hormones that terminate growth and initiate metamorphosis. Previous studies of these circuits relied on relatively static genetic perturbations and starvation experiments. Here, we take advantage of optogenetic approaches in Drosophila to flexibly perturb a key signaling node within the endocrine gland in otherwise undisturbed larvae. We used this approach to infer parameters in a compact mathematical model and demonstrate that it makes accurate predictions of larval commitment to metamorphosis. Our work paves the way for quantitative studies of other juvenile-to-adult transitions, including mammalian puberty, which relies on strikingly similar mechanisms.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/698d6f0d5be6419ac0d551ddhttps://doi.org/10.1073/pnas.2524141123
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