Error Minimization in Lateral Inhibition Circuits

Omer Barad, Dalia Rosin, Eran Hornstein,* Naama Barkai*

*To whom correspondence should be addressed. E-mail: naama.barkai{at}weizmann.ac.il (N.B.); eran.hornstein{at}weizmann.ac.il (E.H.)

This PDF file includes:

• Section S1. Probabilistic model of lateral inhibition: rules for successful selection.
• Section S2. Error rate—formal calculation.
• Section S3. Error rate in a purely stochastic lateral inhibition process.
• Section S4. Error rate in a lateral inhibition process with a pre-pattern bias.
• Section S5. Error rate in an iterative process.
• Section S6. Transcription-based feedback model of lateral inhibition process: the effective time delay, τ^eff.
• Section S7. Cis inhibition model of lateral inhibition process: the effective time delay, τ^eff.
• Section S8. Predicted error rates for different combinations of Notch, Delta, and Serrate heterozygotes.
• Section S9. A smooth inhibition function leads to a higher error rate.
• Section S10. Validation for the use of the mα reporter.
• Fig. S1. Minimizing inhibition delay reduces error rate in a lateral inhibition process with a pre-pattern bias.
• Fig. S2. Reduction of error rate in an iterative process.
• Fig. S3. Simulation results showing the error rates as a function of the Hill coefficient.
• Fig. S4. Validation of SOP detection using the mα reporter.
• Table S1. Summary of the specific genotypes tested for ectopic selection of anterior scutellar (aSC) bristles.
• References

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