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QUB: Quantify Unknown Biophysics
Use QUB to explore the dynamics of hidden states in a memoryless system. Simulate the output from a model system responding to a voltage / pressure / concentration ladder, or recover the most likely transition rates from noisy data. [read more...]
QUB was created to solve problems in ion channel research. It has also been used to model molecular motors, protein (un)folding, fluorescent emitters, DNA transport, and more... Try it today: start the app...

QUB represents a molecule or other mechanism using a state model, like the one below. It's called a Hidden Markov model, because it can have multiple states with the same apparent measurement, and because the probability of a transition from one state to another depends only on which state it's in, and not on its history.

[state model image]

The boxes are "states," and the arrows are labeled with the transition rate per second. Here, the transition rate from state 0 to state 1 is pressure-sensitive. A rate constant can be sensitive to ligand concentration, voltage, or pressure, or any other stimulus that fits this Eyring-type formula for the effective rate constant k:

k = k0*L * exp(k1*V + k2*P)

We use color to group states into "conductance classes" ("classes" for short). By convention, class 0 (black) states are closed/non-conducting. The conductance (measurement) is assumed to be normally distributed, either as a constant (mean +/- std), or as a function of voltage and reversal potential.

Simulated PIEZO1 channel response to a pressure pulse:

This PIEZO1 model was published in (Bae et al, 2013). Above it is an energy landscape visualizing states as low-energy wells, with a ball indicating the current state. Notice how states 0 and 2 are both non-conducting.

Simulated ensemble response of 100 PIEZO1 channels:
[simulated trace]

The rate constants form the matrix Q (G in some literature), from which we can derive:

The likelihood calculations are the heart of QUB. By maximizing likelihood, we can idealize data with the SKM algorithm, recovering the most likely state sequence and detecting events in the presence of substantial noise. We can also optimize with the MIL and MAC algorithms, finding the most likely rate constants and conductance distributions for a given dataset. Voltage- and pressure-sensitivity constants can be recovered from data recorded with multiple stimulus levels.

Optimized rate constants provide a quantitative description of behavior, which can add rigor to comparisons, for example, with or without a point mutation. They enable simulations to plan and compare against future experiments, or where experiments would be impractical. They also open up some advanced analyses.

People use QUB for these problems because it's the most complete package of its kind. While some programs share a subset of our features, QUB is the only software that can

Please cite our papers when you use this software. Thanks.

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