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In silico feedback for in vivo regulation of a gene expression circuit

This study by lxshen explores in silico feedback for in vivo regulation of a gene expression circuit, highlighting the shared nature of biological and electronic control circuits. The research compares the implementation of control circuits in synthetic biological and electronic circuits, showing that while biological circuits offer more functions and are fast and robust, they come with complexities and are not easy to control. By introducing an interface model using a fourth-order linear ordinary differential equation and conducting an identification experiment to determine parameters affecting fluorescence delay, the study aims to increase fluorescence levels over a 7-hour period. The open-loop control method fails to regulate fluorescence accurately, leading to the application of a closed-loop feedback control system with a Kalman filter. This feedback control approach proves more predictable, accurate, and advantageous for synthetic systems compared to bio-circuits, facilitating better regulation of gene expression circuits."

jenniferfranklin
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In silico feedback for in vivo regulation of a gene expression circuit

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Presentation Transcript

  1. In silico feedback for in vivo regulation of a gene expression circuit Shared by lxshen

  2. Biological feedback • Signals • Biological control circuits • Implement of control

  3. Comparison of control circuits Synthetic biological circuits Electronic circuits Prosperous Easy to control More functions Fast • Nature circuits robust • Difficulty in artificial circuits • Not easy to control • slow

  4. Interface

  5. model • Fourth-order linear ordinary differential equation • Identification experiment to determine five parameters fluorescence delay (fluorescence ≠ gene expression)

  6. Applying model Ⅰ • Task: increasing fluorescence 4 or 7 times over a 7-h period • Open-loop(OL)control • Fail to regulate fluorescence to desired levels due to inaccuracies of the model and something unpredictable

  7. Applying model Ⅱ • Closed-loop(CL) feedback control • Kalman filter

  8. Significance • More predictable and accurate • Avoid the disadvantage of bio-circuits • Facilitate synthetic systems

  9. Q﹠A

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