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Minimal model of transcriptional regulation
Inferring parameters from published data sets
Computing the moments of the mRNA and protein distributions
Accounting for cell-cycle dependent variability in gene dosage
Maximum Entropy approximation
Theoretical prediction of the channel capacity
Materials and Methods
E. coli strains
Growth conditions
Microscopy imaging procedure
Data and Code Availability
Three-state promoter model for simple repression
Parameter inference
Unregulated promoter rates
Accounting for variability in the number of promoters
Repressor rates from three-state regulated promoter.
Computing moments from the master equation
Computing moments of a distribution
Moment closure of the simple-repression distribution
Computing single promoter steady-state moments
Accounting for the variability in gene copy number during the cell cycle
Numerical integration of moment equations
Exponentially distributed ages
Reproducing the equilibrium picture
Comparison between single- and multi-promoter kinetic model
Comparison with experimental data
Maximum entropy approximation of distributions
The MaxEnt principle
The Bretthorst rescaling algorithm
Predicting distributions for simple repression constructs
Comparison with experimental data
Gillespie simulation of master equation
mRNA distribution with Gillespie simulations
Protein distribution with Gillespie simulations
Computational determination of the channel capacity
Blahut's algorithm
Channel capacity from arbitrary units of fluorescence
Assumptions involved in the computation of the channel capacity
Empirical fits to noise predictions
Multiplicative factor for the noise
Additive factor for the noise
Correction factor for channel capacity with multiplicative factor
Derivation of the cell age distribution
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Supplemental Information for: First-principles
prediction of the information processing capacity of a
simple genetic circuit
Manuel Razo-Mejia
1
,SarahMarzen
2
,Gri