SimPol

☰ Parameters

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Translocation	Nucleotide incorporation Base to add:	Deactivation	Cleavage
Slippage

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Approximate Bayesian Computation - Fit the parameters to experimental data using approximate Bayesian computation (ABC)

Inference Method:

Rejection
(R-ABC)

Markov chain Monte Carlo
(MCMC-ABC)

Length settings:

N iterations:

N trials per data point:

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Threshold settings:

∈_min =

∈₀ =

γ =

ε =

P(ρ ≤ ε) =

Chain settings:

Burn-in (%):

Log every (states):

Actions:

Posterior Summary

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	Assume NTP binding equilibrium, $\mathbb{BE}$
	K_D =	μM
	k_cat =	s^-1
	k_bind =	s^-1

	Assume translocation equilibrium, $\mathbb{TE}$
	ΔG^†_𝜏 =	k_BT
	ΔG_𝜏1 =	k_BT
	δ₁ =	Å
	Transition state, $\mathbb{TS}$:
Translocation kinetic summary

× Mean forward translocation rate: s^-1 Mean backward translocation rate: s^-1 Mean forward equilibrium constant: Mean backward equilibrium constant: Mean Gibbs energy of transition state: k_BT Mean forward hypertranslocation rate: s^-1 Mean backward hypertranslocation rate: s^-1

	Enable inactivation, $\mathbb{IS}$
	k_U =	s^-1
	k_A =	s^-1
	Enable backtracking, $\mathbb{BT}$
	ΔG^†_τ− =	k_BT
	Backstep model, $\mathbb{GT}$:
	Enable hypertranslocation, $\mathbb{HT}$
	ΔG^†_τ+ =	k_BT

	λ_b =	bp
	Enable mRNA folding, $\mathbb{RB}$
	RNA blockade model:

	Arrest time:	s
	Proposal width:
	Enable geometric reaction sampling
	Normalise translocation barriers

	k_cleave =	s^-1
	λ_cleave =	nt