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General

What are the units for rate constants?

What are the units for the rates shown in the models (k1/K) ?

k: per second

k0: per [e.g. millimolar] per second

k1: per mV

k0: per [e.g. millimolar] per second

k1: per mV

Voltage sensitivity and partial charge

I remembered you mention once that Vs in these formulas is defined as:

Vs (a.k.a. k1) is the voltage sensitivity (with 0.01 giving an e-fold increase in the rate for a voltage change of 100 mV)

I was wondering whether you have somewhere (in the QuB page?) a way to define the Vs in these formulas as dependent on the partial charge instead. Is there any conversion equation to use here? I am thinking that if I am going to use the combined formula below for k0 to give the "averaged" value, it'd be more useful to be able to define it as a function of the partial charge, which is what most people use.

Here is a derivation of Vs from forms of the Arrhenius equation:

k = (k0 * conc) * exp(Vs * mV) k = (k0' * conc * kB*T / h) * exp(-( charge*mV / (kB*T) )) Vs * mV = -( charge*mV/(kB*T) ) Vs = -charge / (kB*T)

The units of charge are electrons. The units of T are degrees Kelvin. kB = 8.617x10^-2 meV/K, so kB*T = 25.9 meV. At 300K:

Vs = -charge / 25.9

Vs can be positive or negative depending on its excitatory or inhibitory effect.

Temperature dependent rate constants?

Does QuB allow for making the rate constants in a model dependent on temprature instead of just voltage and agonist concentrations? I haven't found this in any software so far, but I would be interested in trying it. Do you know anything about this? Maybe we could do something like this by changing the formula that determines the dependence of the rate constants (currently, voltage dependence). Is there any way to set a new formula for the rate constants?

Temperature dependence is not directly supported yet. We may add it to the model in the future, but you can do it with today's model... Whereas our rates are calculated

k = k0*P*exp(k1*Q),

temperature dependence is calculated

k = k0 * T * exp(k1 / T)

In other words, P=T and Q=1/T. You can emulate temperature dependence by making a rate both P-sensitive to temperature and Q-sensitive to (1/temperature). For ramp simulations, this means adding a d/a channel in the acquisition protocol, from (1/T0) to (1/T1) and assigning it (channel 2) to "Voltage." Assign the original temperature signal to "Ligand." For "Mac Rates" analysis of recorded data, you have to save the data as a text file, load it into a spreadsheet, add a column calculated as e.g. "=1/B$1" (if temperature is in column B), then open it back up in qub and assign the new column (a/d channel) to "Voltage."

Nonlinear ligand relationship?

I understand that ligand-regulated transitions can only be specified with a linear relationship: const * Ligand *e(V-V0) is the general expression for the rates. Is there a way one can specify more complicated relationship. In Cardiac modeling seems to be customary to have sigmoidal-like function for the kinetic rates...

How do Kinetic Constraints work?

After running MIL on data for individual concentrations of ligand (dose dependence) I figure that although the forward rates (ligand dependent steps) increase, they are not proportional to the magnitude of concentration change of ligand. And also the backward rates due to ligand dissociation also change to a little extent (in the ideal world it should not).

Therefore I wish to enforce some kinetic constraints but quite confused regarding the one to use. What I need to know is the function of 'fix rates, scale, rates. fix exp, scale exp' etc. and the one most suitable for me.

I know this is a subjective question but any help is appreciated! Also, is there anywhere else where I can read up on this to get more clarity on the subject?

One classic use of constraints is to model multiple independent binding sites. For example, three independent binding sites might look like this:

3*a 2*a a c C0 <---------> C1 <---------> C2 <----------> C3 <------------> O b 2*b 3*b d

When three sites are available (C0) the chance of one binding is three times as much as when there is only one site available (C2). To maintain these proportions, add these constraints:

scale rates 1 2 2 3 scale rates 3 2 2 1 scale rates 1 2 3 4 scale rates 4 3 2 1

Each constraint reduces the number of free parameters by one, so there will be 4=8-4 free parameters, as expected (a, b, c, and d).

If for any reason you do not want to optimize a particular rate constant, you can hold it constant. To keep the rate from state 4 to state 5 constant, add the constraint:

fix rate 4 5

Exponential constraints (k1 -- voltage sensitivity) work the same way.

If your model has cycles, e.g. state 1 connected to state 2 connected to state 3 connected to state 1; you can require detailed balance by adding the constraint:

balance loop 1 2 3

QuB will keep the product of the forward rate constants equal to the product of the reverse rates. That would be consistent with equilibrium -- a loop driven by outside energy would not be balanced.

All these constraints represent assumptions. You can optimize with and without, and see if the constraint makes the likelihood worse.

In your situation, might there be more than one binding site? If we condense the five-state model above to three states, both k1->2 and k2->1 will be a messy function of a and b, with non-linear dependence on concentration. It could provide evidence for or against different connection schemes if the backward rates vary more or less with concentration.

Another way to limit your search, though not a "constraint," is to run MIL with data source: file list. Mark any concentration-dependent rates (check P). Right-click the MIL button, choose "file list," put a check next to any open data file to be included in the fit, and enter the Ligand concentration for each. MIL will find the best overall rate constants. Instead of a slightly varying backwards rate constant, you will see one, sort of a maximum likelihood average.

QUB Express can handle concentration steps within one file, if you record a trace (signal, a/d channel) of concentration alongside the current.

For more details, see appendix 2 of this paper: www.ucl.ac.uk/Pharmacology/dc-bits/colquhoun-biophysj-04.pdf

Following their approach, we can write each constraint system as a matrix equation Ax=B. Now form the matrix C by concatenating columns of A and B: C = [A | B]. Two constraint systems C1 and C2 are equivalent if C1 can be transformed to C2 by elementary row operations: en.wikipedia.org/wiki/Elementary_row_operations

How to model symmetrical ligand binding sites?

I have managed to sucessfully analyze my data from different experimental conditions using QuB. These results are definitely superior to the ones generated by commercially available softwares like Tac and TacFit. I have compared them over many stretches of raw data.

The reason why QuB appeals to me is because its so useful for objective fitting of dwell time histograms and direct modelling of data. Now RyR is a channel which has not yet been fully characterized in terms of its detailed gating mechanisms. We know its a homotetramer and has 4 symmetrical ligand binding sites. With this knowledge how should I proceed with the modeling?

With the preliminary stuff that I have done there are 3-4 open and closed states each but how will I know what transitions (connections b/w states) are the most appropriate? I know that this will have to be based on the physical reality on how the channels gate in practice etc but is there any objective way of doing this?

Identical binding sites are usually modeled as a chain:

4k 3k 2k k 1 ----------- 2 ----------- 3 ----------- 4 ------------ 5 k' 2k' 3k' 4k'

with all the binding rates proportional to each other, and maybe also with the unbinding rates. k12 = 4*k45 because there are four times as many available binding sites. Once you have set up these ratios, you can use Kinetic Constraints (Model properties) to "scale rate" between k12 and k23, k12 and k34, k12 and k45, k21 and k54, etc. This will preserve their ratios during MIL, and if you check the box "enforce constraints" it will preserve the ratios when you edit by hand.

It is also valid to optimize without constraints. If the unconstrained one has significantly higher likelihood, it suggests the binding sites are not independent. It's also valid to try different models -- especially if you have recordings at various ligand concentrations -- rates that aren't involved in binding shouldn't change too much across concentrations, if the connections in the model are right.

(I'm not an experimental scientist but...) People say, the fewer states the better. If you could e.g. saturate it so the ligands stay bound, and the histograms indicate one open and closed state each, then you could first solve the gating rates and go from there.

How does QuB deal with microscopic reversability?

When you change one rate, or during rate optimization, how does QuB deal with microscopic reversibility?

In QuB, microscopic reversability is optional. By default it is ignored -- the rate constants can take any values. In model properties, kinetic constraints, there is a button to "Balance loops," which identifies the fundamental cycles in the model (>= 3 states) and constrains them so their product clockwise equals their product counter-clockwise. (In QUB Express, this button is in the Models table.)

With the constraint(s) added, you can right-click in the background of the model and choose "Enforce constraints." This changes the rate constants as needed to fit the constraints. To have the constraints enforced every time you edit a rate constant, check the box "Enforce constraints" in Model properties, under Rates. Algorithms such as MIL and Mac Rates will always honor the constraints.

Constrain a rate within bounds?

I was wondering how could i constraint rates for a model were once the receptor reached a full occupancy it can pass throw three pre-open states and opens from them to three different openings. What i need to constraint in some way are beta and alpha for each transition because i suppose that the frist opening should be less stable than the the second one, and this less stable than the third one... am i clear? (i.e.: O1=100us, O2=2ms and O3=100ms)

Unfortunately, we can't constrain a rate to stay within certain bounds. The only constraints are Fix (hold constant) and Scale (constant ratio between two rates), along with detailed balance for loops.

It may help to fix all but one rate constant, so the program is fitting only one rate constant at a time. In QUB Express, right-click the rate constant, choose Add Constraint -> Fix rate. Or you could constrain the ratios between the three opening rates (scale a::b, then scale b::c, and a::c is implied). Then remove the constraints one by one.

Non-independent channels

Is there a way within QuB to model several channels that are not independent due to positive or negative cooperativity? Something like making the transition rates for each channel dependent on the states of the other channels.

Yes. Use the Model Merge function. It takes two or more source models, and generates the cartesian product. An example:

A <--> B merged with X <--> Y <--> Z AX <--> AY <--> AZ | | | BX <--> BY <--> BZ

Model Merge also generates constraints in the product model, so that k(AX, AY) == k(BX, BY), and k(AX, BX) == k(AY, BY), etc. You can selectively remove these constraints to allow for cooperative behavior.

Of course, it gets more confusing with several models.

QuB Classic: click "Model Merge" or "mmerge" on the right, under "Modeling" (you may have to click the header "Modeling" to make the Model Merge button visible, or alternatively, use the Actions menu -> Modeling -> Model Merge. To remove constraints, right-click in the background of the model -> Properties..., Kinetic Constraints Tab.

QUB Express: in the Models panel, click the menu icon next to the File menu ("Modeling Tools") -> Model Merge. To remove constraints, go to the Tables panel, Kinetic Constraints tab.

Missed events

Regarding the missed events, how exactly does QuB deal with them? Can you lead me to a part in the website that refers to this? I've always understood that setting a Td (dead time) for the recording is enough to deal with it, but just wanted to make sure this is correct.

I have read somewhere that QuB uses a corrected form of the Roux and Sauve (1985) method to correct for missed events. Is this so ?

For missed events, we say that events shorter than Tdead are not reliably detected. We join each short event with the prior event (and with the next event too, if it is of the same class), to get a record with no events shorter than Tdead. Then we compute the log-likelihood of the model generating the events, given that all short events were missed. The mathematics are based on Roux and Sauve, and are given in the appendix of the original MIL paper (Qin, Auerbach, Sachs 1996), linked from here:

www.qub.buffalo.edu/wiki/Papers_about_QuB.html

although the formula has changed slightly, to the one given here:

www.qub.buffalo.edu/src/qub-express/api/qubfast/max_ll_util.cpp.html

(scroll down to QtoQe)

Basically, MIL derives modified rate constants, which govern the process as observed (with no short events). So, missed events correction is a property of MIL (MIL Rates). Each time you press the MIL button, it creates a copy of the idealization and erases its missed events, as above. It doesn't erase them on screen, so you can try a different Tdead without re-idealizing. In the Results window, MIL's histograms come from the corrected copy, and the PDF curves come from the modified rates.

"Idealize" has an option "apply dead time to idealization" which erases the events from the on-screen record. This may be useful, e.g. if the short events are all noise spikes, but if you later make the dead time smaller, MIL will give the wrong answer.

"Idealize" has another option "apply dead time to statistics" which computes Results such as lifetime (P open) using the corrected record.

Fitting simultaneously at different voltages

Do you know whether QuB allows for fitting simultaneously different recordings from the same patch at different voltages with the same model? (obtaining thus a value of k0 and voltage sensitivity) How is this done?

QuB can do this. You record each voltage level in a separate file, open all of them, and idealize each one separately, then fit together.

*Classic:* add voltage sensitivity to the model, by editing a rate and checking the box "Q". "k1" is the voltage sensitivity, which can be a negative number. To fit all the files together, right-click MIL, choose Data Source: file list, put a check next to each file name ("Use"), and enter each file's voltage in the table. You could also use the file list for different concentrations of Ligand, using the "P" checkbox.

*Express:* Right-click a rate and choose "Voltage-sensitive", then enter a reasonable starting guess for k1, the voltage sensitivity constant (maybe +/- .05?). To fit all the files together, go to the Modeling:MILRates panel and select "multi-file". This will use data from all open files with the specified group number. Assign group numbers in the "Data" table.

MIL fitting with agonist

I have a few single-channel recordings with three concentrations (0, 1 and 10 uM agonist/antagonist) from the same patch (i.e., 2 minutes without modulator, 2 minutes with 1 uM and 2 minutes with 10uM). I was wondering whether it is possible, with QuB, to fit simultaneously these three recordings with the same model to estimate the effect of the different modulator's concentrations on the rate constants, considering that the channel gates without modulator. And if it is, how is this done?

I actually have the values for the voltage sensitivities, which I calculated using the Q using recordings at different voltages and same other conditions. Could I then, since it's going to be the same model for the agonists fitting, introduce the value for the Q and k1 from those calculations and, once I've identified the rate that are agonist dependent, fit the recordings with different agonist concentrations (same voltage)? Would this allow the separation of k0 and k1?

*QuB Classic:*

Open all three, right-click MIL, and choose "file list"; check the box next to each file, and enter its "Ligand" in the table:

www.qub.buffalo.edu/wiki/Tutorial-Single_Channel_Kinetics.html

*QUB Express:*

In the Modeling:MILRates panel, choose "Multi-data", and assign the "Group" of open data files in the Data table.

Of course, you have to know or guess which rate(s) are sensitive to Ligand. You'd mark them by editing that rate constant and checking the box "P". If you want, you can edit the name of the ligand, or use the default name "Ligand".

There is a trick to find which rates are sensitive to ligand: for each rate, check the box "Q" and rename "Voltage" to "LogLigand". Then in MIL properties, in the file list (QUB Express: in the Constants table), enter log(ligand conc.) e.g. log(10) = 2.3, log(1) = 0, log(0) = ? ~= -5? When you run MIL, the exponential rate constant k1 should get bigger for sensitive rates, and approach zero for insensitive rates. Then, when you've found which rates are sensitive, turn off the "Q"s and use "P" where appropriate.

The effect of voltage is the same on all three files, so any variations are due to Ligand (or LogLigand). Paths that are not sensitive, or which have k1 ~ 0, have the same kinetics in all files. Since voltage is constant, MIL can't separate k0 and k1; it puts the combined rate constant into k0. To estimate k0 and k1 separately, you'd need recordings at multiple voltages.

With the LogLigand trick, MIL should find that the agonist-dependent paths have k1=1.0, since

k = k0 * P * exp(k1 * Q) = k0 * P = k0 * exp(1.0 * log(P))

I would expect k1 to take values other than 0 and 1, if the model has the wrong number of states, or the wrong connections.

The model will need multiple paths to an open state, and presumably multiple open states. To start, you could designate one path for voltage gating, and leave "Q" un-checked.

MIL can actually model agonist, antagonist, and voltage, all in one model. Just use different "Ligand" names for each reagent. The limitation (for now) is that, within one file, all variables must be constant, and entered into the file list.

Once you've identified the agonist-dependent rate constant(s), you can introduce the Q and k1 from before. However, at constant voltage, fitting can only make them worse (it can get the same effect by adjusting k0 or k1). To constrain a "Q" rate so the fitter holds it constant:

*QuB Classic:*

- R-click model background -> Properties
- Kinetic Constraints tab
- click "Fix exp" to highlight it
- next to "States", enter the From and To state numbers, e.g. "1 2" (no quotes)
- click "Add"

*QUB Express:*

- Right-click the rate constant and choose "Add Constraint", "FixExp".

Advice for my channels?

I am a PhD student working on single channel biophysics of the human cardiac ryanodine receptor Ca2+ release channel and feel that I would greatly benefit from using QuB to analyse my data. Under my experimental conditions, there are very few and brief channel openings and the 50% threshold method of event detection (Tac/TacFit) is not of much use and therefore QuB programs for idealization can come in handy.

I have managed to have a play around with QuB using my data traces and have somewhat idealized my data but I'm not very sure about what I'm doing. I find the online instruction manual a bit too vast and beyond my league! I would be immensely grateful if I could have a gist of the essential steps that I should follow from the start till model building (and what the program does in the backgound).

QuB has been used successfully to analyse very similar data in your lab and I'm very interested in knowing how they were done.

1. PNAS, JAn 6, 2009, vol 106, 115-120- Purohit et al. 2.JGP May 2000, vol 115, 621-635, Grosman et al.

They have a couple core techniques:

1. Circular re-estimation of idealization and rate constants:

To start the process, highlight a short section with a few events, right click in the model background and "Grab all amps." Then right-click "Idealize", make sure SKM and Re-estimation are turned on, and run it. Then alternate between A and B until everything is stable:

- run MIL -- if there are more humps in the histogram than states of that color, add a state and run again
- run Idealize with Re-estimation turned off (SKM still on)

2. Burst/cluster modeling

Assuming you have more than one black state, one of them is longer-lived than the rest (it has small rate constants exiting). We'll call this the "interburst" state, and list the bursts of events which are separated by interburst events. Actually, we can't tell which state any specific event is in, so we pick a Tcrit, and events longer than Tcrit are assumed to be interburst. MIL reports a Tcrit, and draws it as a red line on the histograms. See also this answer.

To make the list of bursts, click "ChopIdl", enter Tcrit, and run. Now you have a "selection list" of bursts; to analyze just the bursts choose Data Source: list. Some possibilities:

- "Stat" does all the same histograms and measurements that Idealize does, per burst, into the Results window
- after "Stat", in the Select tab of the Results window, plot e.g. Popen (aka "occupancy 2") of all bursts, then use color to divide the list into low Popen, high Popen, and outliers. Right-click in the color chooser to generate a new list of just one color bursts.
- "Extract" a list of bursts to a new segmented data file
- - "Classify" calculates log-likelihood of each burst against multiple models, and makes lists of those best with model A, best with model B, etc.

3. "rate-equilibrium free energy relationship", DNA point mutations, etc... the more outside theory and information you can bring to bear, the more informative and reliable QuB's answers are.

Also see here.

Modeling whole-cell currents from different protocols

Is it possible to estimate a markov model for an ionic current from a dataset comprising whole-cell currents recorded with different protocol (voltage ladder with one and two pulses)?
Let me give you an example, I want to study the LCC current in cardiac myocyte, I know there are Voltage-dependent activation, inactivation and Ca2+-dependent inactivation. Say I run n=4 activation experiments (2 pulse protocol, V-clamp), n=4 inactivation protocol (2 pulse, V-clamp) and a third experiment with different concentrations of intracellular Ca2+ chelator (always n=4). Can I group the 12 experiments in one dataset, specify a particular model topology and estimates the rates that best fit it?

QuB can estimate kinetic and conductance parameters of a markov model, from whole-cell (macroscopic) current recorded with any stimulation protocol (ligand(s), voltage, pressure), as long as you have the stimulus recorded as an analog signal along with the current. The algorithm is given here:

Milescu, L.S., G. Akk, and F. Sachs. 2005. Maximum likelihood estimation of ion channel kinetics from macroscopic currents. Biophysical J. 88(4):2494-2515.

I recommend trying QUB Express first; here are some videos of whole-cell analysis:

www.qub.buffalo.edu/lambda/qub-express-mac-demo/

www.qub.buffalo.edu/lambda/QUB%20Express%20Intro/

www.qub.buffalo.edu/wiki/index.php/Video_Lessons

To analyze all files together, to get a single set of rate constants, use the MacRates panel's "multi-data" option (QUB Express) [QuB Classic: "file list"], which sums the log likelihood of all files with a certain Group number. Open all of the files first, then assign their Group number in the Data table.

Difference between MIL and HJCFIT

QuB uses the full likelihood method, the same used by Colquhoun's HJCFIT. So, is the only difference between these two programs the missed events correction method?

There are two other significant differences:

- QuB has a variety of other algorithms and utilities, including macroscopic/whole-cell maximum-likelihood-rate-optimization (Mac Rates); burst detection, statistics, and clustering; dynamic clamp; data acquisition and waveform output; and baseline correction
- HJCFIT is defined only for bursts of openings. Its entry probability formulas assume the bursts are separated by longer closures, and can correct for this.
- HJCFit is defined only for models with binary conductance, so it can't analyze data with more than two levels (closed/open). QUB/MIL can handle substates, and any number of conductance levels.

Thanks to open-source licensing, we have incorporated the HJCFIT likelihood function as an option in QUB Online, so you can compare them directly. (In "Single Molecule" mode, click "Optimize", "Show advanced settings", "use HJCFIT likelihood when available". ***when available**: if your model has more than two colors (black/red), we'll have to use MIL.

PDF fitting to dwell time histograms

I'm trying to do a dwell-time analysis on a recording. I've idealized it and done a MIL rate estimation, which has produced dwell-time histograms and what I take to be the probability density functions superimposed on top.

What is the equation of the pdfs? I see the Tau and Amp values for each curve but I'm trying to work out what equation they correspond to.

I need to find out what the mean open and closed times are, and assume that I will be able to recover them from the equations. I also want the equations so that I can plot the fits in other software.

I see from the MIL web page that PDF(t) = -Amp/Tau*exp(-t/Tau), but I don't see how a single exponential term can fit the peak on my histogram.

This page derives MIL's pdfs from Q, the matrix of rate constants. Q[i,j] is the probability per second of a transition from state i to state j. Q[i,i] is chosen so each row sums to 0. Essentially, it is the integral of the exponential distribution, over the width of the bin.

The pdf for one class (open or closed) is the sum of exponential components, one component per state. If you have a two-state model, C <-----> O, the open pdf has one component, and its Tau is the mean open time. (The closed pdf also has one component.) It sounds like your histogram has more than one peak, so you might rather MIL with two open states, or fit with two exponential components, to get a mean short-opening time and a mean long-opening time.

Be aware that MIL's histograms and pdfs are both modified by missed-events correction. Events shorter than td (upper-right) are joined with their prior neighbor for binning purposes, and the pdfs are derived from "effective" rate constants (given that short events are not detected).

For traditional histograms and fitting, try the Histogram window (View -> Histogram). The third pane (green) makes duration histograms. These histograms also come from the Data Source, so make sure to choose "file" if you want a whole-file histogram. To pick closed (class 0) or open (class 1), right-click for properties. Then click the green button to update the histogram.

To fit the histogram to exponential pdfs, right-click the histogram, choose "Curve fitting." For "Curve," choose "Exponential (log x bin)." To fit the parameters "Amp" and "Tau," check those boxes, and un-check the parameters "LogBase" (since the histograms use log base 10), "Base" (no baseline offset), and "x0" (no x offset), and make sure they're 10, 0, and 0 respectively. Then click "Fit."

After closing the fit window, you can copy or save the histogram as a table of numbers, by right-clicking it and choosing one of the "Copy..." or "Extract..." items.

QUB Online

QUB Express

How to run a script at startup?

I was wondering if there was a way to launch the scripts at the launch of qub-express?, preferably with some arguments?
With this I could conceivably run experiments in qub-express completely from the command line.

To run a script at startup:

$ qub-express -s my_startup_script.py arg1 arg2

The arguments are available in global var "args":

>>> print args ['arg1', 'arg2']

If the script needs to wait, e.g. for a file to finish loading, there are a few options:

- Call it later on the GTK+ event loop: instead of

>>> myfunc(arg1, arg2) write >>> gobject.idle_add(myfunc, arg1, arg2) or >>> gobject.timeout_add(1000, myfunc, arg1, arg2) # in milliseconds

- "doze" for a few seconds. This is like sleep except it processes events in the background:

>>> doze(1.0) # in seconds

- Some changes take place later on another thread, e.g. setting the value of a curve-fitting parameter. If you can find the qubx.task.Robot responsible, call its .sync() method to doze until outstanding requests finish. (www.qub.buffalo.edu/src/qub-express/api/)
- call QubX.pause() to wait for the user to press Play in the script panel

Conductance/Activation plot

I thought I'd throw out an idea I've had for a while where I think it would be helpful for QUB-Express to be able to display both the recorded current and simulated current in terms of conductance or % activation (relative to the reversal potential and total conductance), rather than just current.

I think these plots would really help in understanding how the model is behaving, make it easier to determine how many channels/conductance is required, and even help pick up on problematic portions of recordings, a few weeks ago I had a recording with a section displaying negative conductance due to a subtraction error, this error would have been obvious when looking at conductance graph.

Along these same lines a state occupancy graph would also be helpful in understanding how the model is behaving. I want to track the specific state occupancies so I was looking for a way to access the Pt matrix. They're calculated in qubx_model.cpp but I'm not sure how to access the model object they're being stored in.

In the global namespace (qubx.global_namespace), the onscreen qubx.model.QubModel object is QubX.Models.file (the qubx.modelGTK.ModelView object is QubX.Models.view). The model-related tables are QubX.Models.file.states, QubX.Models.file.classes, QubX.Models.file.rates, and QubX.Models.file.constraints_kin. They are indexed by [row_index, field_name], e.g.

k0 = QubX.Models.file.rates[0, 'k0']

See qubx.table for more. Matrix-related functions in qubx.model include:

ModelToP0(QubModel): returns the vector[Nstate] of constant entry probabilities from the States table ("Pr"), normalized to sum to 1.0, as a numpy.ndarray.

ModelToQ(QubModel, stimulus=None): returns the numpy.matrix (Nstate x Nstate) of rate constants, with diagonals such that each row sums to zero. Off diagonal entries are calculated k = k0 * Ligand * exp(k1 * Voltage); if any rates are ligand- or voltage-sensitive, provide optional arg stimulus: a dict(stimulus name -> value), or it will simply use k = k0.

QtoA(Q, dt): returns the sampled transition probability matrix (Nstate x Nstate) after dt seconds.

QtoPe(Q): returns the numpy.array (Nstate) of equilibrium occupancy probabilities.

you can add an artificial signal computed from existing signals:

- in the Signals table, click "Add computed signal..."
- Enter a name for the signal, then type a function of the other signal names, e.g.

Current / (Voltage - (-60))

- Instead of a function, you can type the name of an array

You can edit the "computed as" field in the Signals table at any time. To remove the computed signal, delete that row from the Signals table.

Alternatively, you define an artificial signal using Tools:Extract, Signals: Custom. Pro: faster, since the function is calculated once and saved on disk. Con: must save a copy (extract) of original data file.

At minimum, the function must operate on individual floating point numbers. It can optionally achieve higher performance by operating on arrays as well. The reason relates to our use of numpy arrays, which keep numbers in native "unboxed" form. It can be very slow to convert each array element to a python number object, multiply by other python numbers, then write the result into an array. Numpy avoids this by providing vectorized functions implemented in native code, e.g.

my_array *= 2

can be 100x faster than

for i, x in enumerate(my_array): my_array[i] = 2*x

In addition, numpy functions such as log, exp, pow, sin, etc... can take numbers or arrays as arguments. See http://docs.scipy.org/doc/numpy/reference/ufuncs.html. As a result,

def f(x): return 2*numpy.exp(-x / t)

is valid when x is a number or when x is a numpy.array. (If x is an array, it returns an array with the same dimensions, computed element-by-element.)

For more complicated functions, you may have to test whether x is an array:

is_array = hasattr(x, 'shape')

If your function does not work with arrays (raises an exception), I believe the program falls back and calls it on each element.

Calculate net charge?

I was wondering if there is a way to calculate the net charge from macroscopic currents in qub?

I think the net charge is the integral of current: charge = integral|from a to b|(I dt). Or in discrete computer terms: charge = sum(I[i] * delta_t). Using the mean value theorem for integrals, or the fact that mean(I) = (1/N)*sum(I[i]), we can substitute to get charge = N*delta_t*mean(I) = duration(in seconds)*mean(I).

You can customize QUB Express's measurement script to calculate this and other quantities:

- In the Data panel (lower half), click the ruler (Measurement) icon and choose "Measurement Script" -> "Edit Script..." It will load the measurement script into the Admin:Scripts panel.
- Find the line:
measured['%s Mean' % signal.name] = samples_included.mean()

- and below it, add:
measured['Net %s' % signal.name] = measured['Duration']/1000 * measured['%s Mean' % signal.name]

- Make sure to indent with the same number of spaces. We divide Duration by 1000 to convert from milliseconds to seconds.
- Save the script

Now if you have a signal named "Current", "Net Current" should be included in all of QUB Express's measurements in the future.

Of course, it's important to set the baseline correctly first.

Not as automatically. You can read the "Avg" and "Length" from the data tooltip, then type them into the Calculator field of the Report window; e.g. with Avg=2.0 and Length=1.0: type "1.0/1000 * 2.0" to get the answer.

In QuB Classic, it's not automatic. You can read the "Avg" and "Length" from the data tooltip, then type them into the Calculator field of the Report window; e.g. with Avg=2.0 and Length=1.0: type "1.0/1000 * 2.0" to get the answer.

Defining voltage signal?

I opened some data on QuBexpress and I am trying to optimise the rate constants of a model of these data. Thing is, QuB doesn't let me set my "voltage" signal (the protocol that was used in the experiment and that I reproduced under simulation). Under Modeling>Stimulus, my variable "voltage" is linked to "channel 0" instead of the voltage protocol I established. How does it work ?

The protocol in the Simulation panel is usually used for simulations, but it can also be used to add a voltage signal to an existing data data file. After building the simulation protocol, right-click the voltage waveform inside the Simulation panel and choose "Add signal to data file." The voltage signal will appear in the Data panel. Verify that it is correct, then Save As, and continue analyzing the new file. (In the original file, voltage is a live reflection of what's in the Simulation panel, but the saved copy preserves a snapshot of when it was saved.)

QUB express vs SIM

Does QUB express do anything that SIM in QUB cannot?

Short answer: No. Anything you can do with QUB Express, you can do with QUB. However, QUB express might require fewer clicks. See this video:

www.qub.buffalo.edu/lambda/qub-lesson-04

(I show all the steps to make the same simulation in QUB Express and QUB)

Excluding clusters by pOpen?

I don't know how to exclude for the MIL analysis those clusters which have a kinetic profile so far from the media. The criteria i'm used to make this selection is pOpen, but when i click the dot in the "occupancy 2" chart in order to unselect the corresponding cluster MIL is still making the kinetic analysis with the whole data... is there any way to do this selection?

When you Idealize or "chop" idealization, it generates measurements into a table e.g. Segments or List. Then you can plot some per-segment value such as Popen (occupancy probability of the open class) vs. segment index, and apply color(s) to that plot, in order to exclude outliers or break the data into multiple populations.

**Using QuB (classic):**

To analyze just the segments you've colored red in "Results: Select", right-click the red square on the left edge and choose "Make selection list..." It will make a list of only the red segments. Then choose Data Source: list and continue analyzing with the new red list.

**Using QUB Express:**

"Select" is renamed Figures: Charts. In version 0.9, it can plot info from the Segments or List table. (Chop is in the Lists menu, at upper-right of the Data.) After coloring points from the Segments table, use Tools: Extract with Source:other, Segments: group 1, to create a new data file with only the red segments, then continue analyzing the new file.

If you've used Figures:Charts to color some points from the List table, look at List in the table view, and click "Copy sels to list," which will make a new list with only the red selections. Then use Tools:Extract with Source:list to create a new data file, and continue analyzing that file.

Delay in modeled Voltage

I'm having an issue modelling a set of traces. The activation spike is quite short lived, 0.005 s after the voltage step the current has already come down by half, yet the QUB approximation of the voltage doesn't change until 0.01 s after the voltage step, missing almost all of the current. As a result the model has no stimulus to respond to and is unable to get a fit.

Does anyone know why this might be happening? Is there any way to change this behaviour and simulate the voltage more accurately?

In the panel Modeling:Stimulus, you can set "latency" to a negative number of milliseconds, in order to shift the voltage signal to the left. For short pulses, make sure "min dur" is small enough to characterize it accurately.

Run from source code on Mac

I downloaded the source code on my mac. How do I get it running?

First, install XCode from the App Store. Then open the Terminal and install Homebrew:

$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Homebrew's default gtk+ works with an X server, but we want native Quartz graphics, so we use specific taps:

$ brew install marcsowen/gtkquartz/pygtk gsl homebrew/python/scipy boost

As of this writing, the marcsowen/gtkquartz tap is not compatible with osx 10.10. To make it compatible, go to /usr/local/Library/Taps/marcsowen/homebrew-gtkquartz/ and edit two files: In gtk+.rb, change the 'url' on line 5 to version 2.24.25, and in pango.rb, change the 'url' on line 5 to version 1.36.8. Also delete the 6th line of both files (the line starts with 'sha256'). Then remove and reinstall both recipes:

$ brew remove gtk+ pango && brew install marcsowen/gtkquartz/gtk+ marcsowen/gtkquartz/pango

To select the right compiler, add these to the ".profile" file in your home directory:

export QUBX_CCP="/usr/local/bin/g++-4.9" export QUBX_PYTHON="/usr/local/bin/python2.7"

and restart your terminal, or type "source ~/.profile". The system should be ready for QUB Express:

$ cd qub-express $ make clean $ make all

To make an app (bundle) and disk image in the dist folder:

$ make bundle $ make dmg

QuB Classic

Energy landscapes

Wanted to know if I could derive some sort of thermodynamic expression from my kinetic schemes (from rate constants)? Will the Energy landscapes function in QUB classic help me do this? If so, what is the concept behind it. I am dealing with a C-----O scheme and sometimes a C1------O1---------O2-----------C2 gating scheme. All are equilibrium reactions and no ligand binding/unbinding is involved (purely gating).

The energy landscapes look OK qualitatively but can I get any quantitative meaning out of them? Is it in any way related to REFER?

It is the same mathematics used in REFER, e.g.

How to Turn the Reaction Coordinate into Time Anthony Auerbach

www.ncbi.nlm.nih.gov/pmc/articles/PMC2151657/

The C-O reaction can be viewed as an energy diagram, where the y axis is energy and the x axis is the "reaction coordinate," which can be thought of as the extent to which the reaction has occurred, on a scale from 0 to 1. There is no a priori reason to assume any particular overall shape of the TR energy barrier, but the simplest picture is a parabolic peak at the intersection of the parabolas that define the end-state energy wells. However, other barrier shapes are also consistent with an apparent two-state reaction, as long as events that occur during crossing are too brief to be measured.

So the parabolic shape is somewhat arbitrary. The most certain thing is the relative height of the energy wells (states). As given here:

www.kshitij-iitjee.com/Thermodynamics

www.kshitij-iitjee.com/Kinetics

The difference in free energy from state i to state j is delG = -RT ln(Kij/Kji). To compute barrier height, we have to split the exiting rate constant K into K = A exp(-Ea/RT), so Ea = RT( ln(A) - ln(K) ). In QuB, "A" is the "reference rate", which you choose (arbitrarily) to be larger than any possible K.

A talk on REFER (Rate-equilibrium free energy relationship): videocast.nih.gov/summary.asp?Live=2397

There is no definitive answer, just a bunch of possibilities.
You can refer to
*Biophysical Journal, Volume 92, Issue 2, 15 January 2007, Page 696*
or
*
PNAS vol. 102 no. 5 Anthony Auerbach, 1408-1412
*
for a non-standard interpretation of phi.

A rate constant is =A*exp(-dG+/kT). dG+ is the height of the TS barrier (usually assumed to be the point of the parabola intersection); kT have their usual meanings. A* is the 'prefactor', and is the bugger. There are several things in there, none of which are measurable. It's black, so people usually assume that it is constant, usually 1, and just ignore it. In the above 2 references I decided to put ALL of the rate constant change into A* and leave dG+ constant. It is a possibility, but cannot be proved.

Global fitting

Could anyone please explain the whole process of global fitting across a range of ligand concentrations step by step?
Also, is it useful to use data recorded at saturating concentrations of ligand (high Po) for attempting model building rather than lower concentrations?

Noise not simulated?

I have used qub for the first time in several years on a new fujitsu notebook running windows 7 64 bit. Whenever now try to simulate single channel currents the programme generates what look like idealised traces. The sampling rate was set at 10 kHz.

This looks like probably a scaling issue. QuB stores data as integers, so it's important to choose a good "scaling" factor:

stored_int = round(pA * scaling)

To adjust the scaling, right-click "Simulate" and choose "new data file." After clicking OK (Simulate), scaling is in the next dialog (new data file options).

Our newer program, QUB Express, uses floating-point formats to avoid most scaling issues.

QuB crashes during MIL

When I run MIL, the program turns off ("QuB.exe has stopped working").

With QuB version 2.0.0.34, this was resolved by installing the Microsoft redistributable libraries. Please tell us if this fix doesn't work for you.

Fix bins in amp histograms?

Is there a way to fix the bin-width and bin-position when analysing the amplitudes? Is there a way to extract the not normalized histogram?

I need to compare events which contain several / different sublevels per event, thus I want to compare the different histogramms. The same scaling would help a lot.

Try the Histogram window (View menu -> Histogram). The first panel makes amplitude histograms; right-click, properties for options. In particular, turn off "Autoscale." Then press OK, and click the little blue icon to update the histogram. Note that it operates on data from the "Data Source." To extract the histogram, right-click it and "copy data", "extract" (text file), or "extract to excel".

Fix noise Idl/Base

In the Idl/Base screen there is an option to 'Fix Noise', which from my understanding should prevent the model from re-estimating the std deviation for the amplitudes. Everytime I click this, it seems to ignore the command, and the model is fit with all the std-deviations changed (checked by looking at the results window). The command seems to work with SKM idealization under the Idealize option. Any suggestions?

The "Fix Noise" option is not used anymore in IDL/Base. Instead, one can apply amplitude or noise constraints using the constraint editor in the model.

To access the constraint editor, RightClick on the model, choose "Properties", and select the "Amplitude Constraints" panel.

The amplitude and noise of the closed states ("black") are regarded as a reference. The amplitude and noise of open states (other than "black") can be seen as absolute values, or as "excess" relative to the reference values. Thus, the constraints can be formulated in terms of absolute, or excess values. By definition, the excess amplitude and noise of the closed states are both zero.

The constraints available in the editor have the following meaning:

Fix Amp/Var:

- fixes the absolute amplitude/variance of a conductance class;
- takes one argument: the index of the class (1 - "black", 2 - "red"...)

Fix exc Amp/Var:

- fixes the excess amplitude/variance of a class, relative to the reference class (the "black" states). The absolute Amp/Var may change upon reestimation but the difference (the "excess") is fixed;
- takes one argument: the index of the class;
- does not apply to the closed states.

Fix diff exc Amp/Var:

- fixes the difference in excess amplitude/variance between two classes. The absolute Amp/Var of either class may change upon reestimation but the difference between them remains fixed;
- takes two arguments: the indexes of the two classes.

Scale exc Amp/Var

- keeps fixed the ratio between the excess Amp/Var of two conductance classes;
- takes two arguments: the indexes of the two classes.

Scale diff exc Amp/Var

- keeps fixed the ratio between the differences in excess Amp/Var for two pairs of classes;
- takes four arguments: the indexes of the two pairs of classes.

For example, when idealizing data with 3 subconductance levels that are assumed to be of equal amplitude, one can use a set of two "Scale diff exc Amp" constraints:

2 1 1 0 3 2 2 1

With these constraints, the re-estimation of amplitudes will make sure that the difference between "black" and "red" (the first level, 0-1) is the same as between "red" and "blue" (the second level, 1-2), and the same as between "blue" and "green" (the fully open level, 2-3).

Computing cluster duration

I was wondering if there is an easy way to get a cluster duration histogram after a single channel simulation?

Here is a trick: change the colors in the model so all of the in-burst states are red, and all the inactive states are black. Now the simulation will be "open" for the whole burst, and you can look at the open-duration histogram.

When you simulate, make sure to check "Idealized data: Class". Then choose Data Source: file, and go to the Histogram window. Right-click the third panel and choose "properties." Select class "1" (sorry, in this window 0==closed/black, 1==open/red) and adjust Bin count and log [log10 x axis]. Then close the properties and click the green button to draw the histogram.

Right-click the histogram to copy to the clipboard, or to Extract it to a text file or excel spreadsheet.

When you're working with real data, you can use a similar trick. First idealize. The next steps will modify the idealization, so you might want to save and work on a copy. Next, find the bursts using ChopIdl, e.g. terminating with a dwell in class 1 longer than Tcrit ms. (To get an estimate of Tcrit, run MIL with a 2-state model and "show iterations.") Now there is a selection list of "chopped events"; choose Data Source: List, click "EditIdl", and "change class 1 events to class 2."

At this point the idealized line is "open" for the whole burst. Use the Histogram window, as above, to see the open-duration histogram.

NPo vs Time histogram

Hi there, I'm wondering if QuB can be used to generate a histogram of NPo on the y axis versus time on the X axis for a long recording I have. I want to monitor NPo changes during a recording of about 8 minutes, splitting up the recording into 10 to 20 second long bins for the purposes of measuring NPo several times per minute. Is this possible?

QuB can chop data into 20 second chunks, measure P0 of each, and make a table of start time and P0 per chunk:

- make sure the whole file is Idealized.
- set Data Source: file, and click Chop Data (skip 0, get 20000 msec, skip 0)
- set Data Source: list, and click Stat (the Stat button may be hidden; to show it, click the Modeling header in the right-hand button bar).
- In the Results window, "Segments" tab, find the column "occupancy 2". Highlight the whole column, right-click, Copy. Paste into a program that can make a histogram.

How to fit the gaps between each cluster

I am analyzing cluster. Could anyone tell me how to get the gaps between each cluster?

First, Idealize your data.

Then, create a list of bursts. To create it by hand, use the Actions under "Lists": First "Add List", then for each burst, highlight it and "Add Sel".

To quickly create a list of bursts, use ChopIdl. It will list all of the data regions whose closed events are shorter than Tcrit.

When you have a list of bursts, choose Data Source: list, and run "Stat" (Actions -> Modeling). In the results window, go to the "Segments" tab, and look at the column "interburst". Alternatively, go to the "Select" tab, and "show var..." -> 'interburst'. Interburst is the number of milliseconds since the end of the last burst. If there was no prior burst in the same data segment, 'interburst' is the number of milliseconds since the beginning of the data segment.

Counting the number of pulses in the file

I am trying to count the number of pulses in the file. I am using Qub for the first time. I used the model option where I assigned two states, one for the baseline noise and the other for the pulse noise. When i idealized the trace, I did see a histogram of my resukts which I extracted to Excel. But I do not understand the yaxis which is counts/total. I need the exact count of the total number of pulses, but I do not know how to convert the counts/pulse to actual counts

After idealizing, go to the Results window and click the tab "Segments." Scroll the table to the right and you should see columns including "DwellCount" -- the combined number of baseline and pulse events; "nevent 1" -- the number of baseline events; and "nevent 2" -- the number of pulse events. Also, "occupancy 2" -- fraction of total time spent in pulse state; and "lifetime 2" -- mean duration (milliseconds) of a pulse event.

Manually altering an SKM idealization

I generally find the idealization by SKM to be quite good.

However, sometimes SKM doesn't detect an "event" that I might want to call an event, or detects an event, but gets the duration of the event wrong. I would like to be able to manually insert new events and be able to alter the duration/amplitude of events detected by SKM. Does QUB allow you to make these kinds of manipulations easily?

Two basic operations are in the data's right-click menu:

Join Idl -- extend the event which is at the beginning of the selection across the entire selection

Delete Idl -- remove idealization from the selection

You can do more with the "Edit Idl" button (under Preprocessing on the right). For example, to insert several open events quickly:

- make a new selection list (Lists: Add List)
- highlight each event and press the S key (shortcut for Lists: Add Sel)
- set Data Source: list (upper right)
- Edit Idl: fill blanks with class 2

Exporting baseline corrected data

I'm having a little trouble exporting/extracting baseline corrected data. I have just a single channel and after correction for baseline, i click extract, choose ASC as my format, data source=whole file, pre-process data=as such, baseline. I notice that the output file has all integer numbers instead of a precision of 2 decimal places of my original data. So if I now plot this somewhere else, it looks weird, not to mention the loss of precision. However if I open it in QuB, everything looks fine. I was wondering if this is normal/by design/a result of doing a baseline correction, or maybe I'm just doing something really dumb.

I think maybe your data lost some precision on the way into QuB. Was the input file also text? When you open a text file, one of the number it asks for is "scaling." For historical reasons, QuB represents data internally as integers, and converts them to real numbers by

real = int / scaling

So when you enter the scaling factor, it effectively quantizes the data to increments of (1/scaling). Next time you open the input file in QuB, click into the "scaling" box; a calculator will appear with the maximum possible value and the resolution. Hopefully if you change the resolution to 0.01, Extract will work properly. Please let us know if it doesn't.

Exporting data from QuB

I would like to simulate data with QUB and then use the simulated data in other applications (e.g. pClamp) apart from QUB. I have been trying to save the simulated data as .dat, .wav, .txt, etc... but none of these files seem readable by the pClamp software (which reads .abf, .dat, etc...). I was wondering whether the is there any option to export files from QUB into a format readable by pClamp

Extract the simulated recording ('Ext' button) into ASC format without checking any of the options of the 'output format' box and checking the box 'Join Segments' ('Only active channel' also checked) - I select in the window that appears next '4 bytes' and check 'Float' (although this is only to show the ASC data into QUB again, I think). This generates a TXT file.

Open the TXT file using the pClamp software - the program I use is called Clampfit and the version is 9.2.0.09 (I hope all this doesn't change with the version). In the window that appears next I check 'Gap-free' and 'Add a time column', assigning the correct sampling interval value. This imports the recording into Clampfit so that we can see the same recording we had in QUB now in Clampfit's window.

Last, save the recording as (File/Save as... option) an ABF file.

Messed up Report window

The text in the report window is totally garbled, even when I restart QuB.

To fix the report window, delete the file c:\program files\qub\console.txt. This file preserves the contents of the report window across runs, but it is probably corrupted.

Selecting clusters

Is it possible to manually select segments (the best clusters) from a single channel data file, and join these segments (maybe in a new file!?). The important thing for us is to obtain amplitude histograms (and dwell time histograms) that includes data from all the selected segments (but only from these segments!).

To build a list of clusters:

- click Lists:New List
- with each good cluster: select it and click Lists:Add Sel

To join them together in a new data file:

- choose Data Source: list
- click Preprocessing:Extract
- choose Output format: QDF (or DAT), and check "join segments"
- double-check the output file name, and click OK

--or--

To see amplitude and duration histograms for the listed clusters:

- choose Data Source: list
- View -> Histogram -- the first pane is amplitude, the third pane is duration
- right-click in a pane -> Properties to adjust bounds, choose a class of events, etc.
- click the graph icon at upper-left of a pane to re-generate the histogram from the Data Source

For duration histograms, you have to Idealize the data first (click Modeling:Idealize).

Concentration-dependent rates reset to zero?

When we simulate from a model, we find that if you have made binding steps concentration depend, these rates will be set to zero as soon as you hit 'Sim'. Why? A bug?

It's kind of a feature: What you're seeing is not k0, but k0*Ligand at the active point in the data. So if Ligand varies (it's assigned to a data channel in data properties: data: experimental conditions), clicking different parts of the pulse changes the effective rate constant shown in the model. If you'd rather see the raw k0, go to model properties: display and check the boxes "write rates along arrows" and "show k1".

Method of choosing amps/std dev

We noticed that the idealization is exquisitely sensitive to the amplitudes and standard deviations selected in the model and can change the number of states required. We have come across several different methods for choosing amps/std dev to put in our model and were wondering which one is best.

We can use:

- AMP (under modeling) on either the whole file (which takes a very long time because our files have greater than 10,000 events) or on a list of clusters- this produces high std dev
- make a list and create an amplitude histogram and curve fit- this can require more than 2 components to describe open and closed
- grab a representation from the file- this might bias the outcome

What do you recommend?

Typically, we AMP a section, idealize the whole file w/ SKM (re-estimating the amps and rates), fit with MIL, then re-SKM (no estimation of amps or rates), and then fit with MIL, etc. until everything is stable.

Copy and paste IBW

I'm a new Qub user and a problem has been puzzling me for a while recently. I wish to edit the experimental data ( it's a binery data which has a file suffix of .ibw) with the delete, cut and paste option, but none of this is optional in the right-click menu (the options are grey). When it come to the data which is simulated by Qub, however , these editings are becoming usable. Why? I just want to combine the middle parts of dozens of file into one big file. That's easy for me to analyze as yielding histograms and using MIL to estimate rate constants. I also tried to edit the idealized data saved by Qub and it's just not working.
What can I do about it?

As you have seen, we have written copy and paste for some types of files, but not all of them. The answer, for sampled data like IBW, is to change the file into a supported format like QDF. In Qub, "Extract" the whole file to QDF format, and copy/paste using the new file.

For idealized data you'll need a text editor like notepad. In Qub, do "File->Idealized Data->Save...", then open that file using notepad.

Occupancy and lifetime with Idl/Base

Do you know how to get the occupancy and lifetime data when idealizing using Idl/Base (gamma/Baum-Welch Algorithm)? I do get those values when I idealize with Idealize (SKM).

After idealizing, click the "Stat" button (on the right under Modeling; if it's hidden click the Modeling header). It does the lifetime etc. measurements and histograms using existing idealization. Right-click Stat to choose whether to apply the dead time (drop short events) before measuring.

Output curve fitting parameters?

I just have a quick question about the curve fitting tool in QuB. Is there a way to output the parameters from that window into a spreasheet or table of some sort?

There is if you use MultiFit. After doing "Fit All", and closing the fitting window, go to the Results window (View -> Results), Segments tab. You can drag across this table to select, then right-click: "Copy".

Make a selection list from Data Overlay

Is there a way using the data overlay window to add your selections to a selection list or can you just make new selection lists from this window.

- Building a new list from pieces of an old list, one at a time: Choose the source list in the overlay window, then create or choose a destination list in the Data window. When you click a trace in the overlay, this selects it in the Data window. To add this selection to the dest. list, click in the lower pane of the Data window (to give it input focus) and press 's'.
- Building a new list from corresponding pieces of an old list, all at once: Choose the source list in the overlay window. Optionally drag a rectangle over the overlay: this excludes all data before and after the rectangle, as well as any selections (traces) that are entirely outside the rectangle. Remove the checkmark from any selections that should be entirely excluded from the new list. Right-click in the lower pane and choose "new list of selected"
- Like (2) but implicit: Choose Data Source: list. As long as there is a list loaded into the overlay window, 'Data Source: list' refers to just those parts that would be included in "new list of selected". This is visible as a blue background in the Data window, if 'data properties: draw selection list' is on.

Data Acquisition

What is the buffer length?

What is the buffer length? and which value should i use?

QuB receives acquired data into a buffer (memory region). Because of OS and disk delays, it's not possible to write each sample to disk when it arrives, so we operate on one buffer full of samples at a time. If the buffer length is too short, and QuB can't write one buffer to disk before the next one arrives, you may lose data (you'll know because the file is shorter than it should be). The default buffer length of 1000 ms is generally sufficient.

Digidata

I am trying to set up some dynamic clamp experiments and was wondering whether QuB would work with a Digidata 1322A. At a first pass, QuB doesn't seem to recognize the Digidata in the Hardware Configuration window - "search" does not result in any identified hardware.

QuB does not currently support Digidata or other hardware from Molecular Devices. If Molecular Devices provides a programming interface, it is possible for a programmer to build a QuB acquisition plugin. For an example, see the folder QUBACQNM in the QuB source zip.

Scaling factor

I've noticed that when I manually enter a scaling factor, it will always become replaced by a default value. Is this value determined by the input calibration factors, or is it a value which I need to manually set for input acquisition?

The scaling factor has units integers-per-volt. QUB calculates this from your acquisition setup -- integer range e.g. 16 bits = 32768, divided by voltage range e.g. +10 - -10 = 20.

In addition, there is a second scaling factor, for each A/D channel. This one has units of volts-per-unit, and is sometimes called the "calibration factor." So for example, if a 3V signal from the amplifier means 1pA, the calibration factor is 3.

You can change scaling and calibration factors after the fact, in data properties. You can set up calibration factors for future experiments in Options -> Acquisition Channels, or with Options -> Calibration Assistant.

When you create a new file for acquisition, it totally ignores the user-input scaling and writes the correct value for your acq setup. After that, you can change it and QUB won't know better. I think all files acquired on the same system should have the same scaling (not necessarily the same calibration), so it should be easy to check and fix.

Fitness

Fitting an amplitudes histogram Beta distribution in Fitness

The beta distribution is here:

en.wikipedia.org/wiki/Beta_distribution

I can happily add equations to Fitness in general, but the beta distribution requires implementation of the gamma function. When I enter "gamma(a)" into the curve box... it is in red, and I am presuming not recognised. I have installed Python 2.7 which includes that function in its library... but it is still not recognised?

The gamma function is included in Fitness as part of scipy. To enable it, edit the python startup script:

- Python menu -> Environment
- append this line:

from scipy.special import gamma

- for example, mine says:

from math import * def after_initialize(): pass from scipy.special import gamma

- press Reload, and close the Python Environment window.
- if you had to define a strange custom function (e.g. recursive), this would be the place for it.

After editing the script, we can type in the Beta function:

gamma(a+b)/(gamma(a)*gamma(b)) * (x**(a-1)) * ((1-x)**(b-1))

Note: they later modified the gamma function, "to add some Gaussian noise and normalise for the 'width' of the data (Beta distributions only run from 0 1, I think)":

ampC*gamma(a+b)/(gamma(a)*gamma(b)) * ((x/width)**(a-1)) * ((1-(x/width))**(b-1)) +ampB * exp( -(x/width - Mean)**2 / (2 * Std**2) )

Misc.

Understanding the transition matrix

I've been trying to replicate the models behaviour under matlab and am having trouble understanding how the models run under certain conditions.

My understanding is that the rate matrix Q, is multiplied by the state distribution probability vector, P, to get the change in distribution.

dP/dt = P*Q

However, if one has k0=10, and k1=0 then q_ij = 10*e^0=10,

If there's a q_ij > 10 than it's easy to get an element of P > 1, even with a dt=0.001 if k1=1 it doesn't take much to get a large q_ij again.

So how does QUB deal with these scenarios, or am I missing a step somewhere?

How to test which model is better?

Hi, i want to know how could i test for a set of kinetic models which of them could explain better my experimental data evaluating if there is any correletion for openings and closings. I mean, if i have a data which could be fitted by these two models:

Model 1: C1<--->C2<--->O1 or Model 2: C1<--->O1<--->C2

Is there any way to destinguish between them, for instance, an analysis to test the "correlation" between C1 and O1?

There is no theoretical way to tell them apart using simple equilibrium data. However, if you optimize with multiple recordings at different concentrations of ligand, or voltage/pressure levels, the models should be distinguishable by log likelihood. If you optimize the correct model one data file at a time, most rates should agree, with only one or two changing with concentration; in other models, all rates might change. Similarly, point mutations of the channel have been seen to affect only one or two rate constants.

Max iterations in MIL

I was wondering how much is a good number to set in MIL for "Max iter."?

I got a complex model with 3 closed and 3 open states without agonist or voltage stimulus. When I set the Max iter number to 10 and run Mil, I get strange constants with very high SD. But if I increase the Max iter higher than 10 (50 or 100), the program suggests very strange rate constants for some transitions (1e-5 or slower) or too fast (1e+6 or faster) for other transitions. How could I interpret this?

I think it can happen when there is not enough data to precisely quantify the rate constant. If no likely transitions are observed, the rate constant will approach a limit of 0.0; if transitions seem to happen much faster than the sampling rate, the rate constant may approach positive infinity, because of the approximate missed events correction.
One approach is to add constraints to the model -- to fix the problematic rates at constant values, and optimize the others. In QuB Classic, go to Model Properties -> Kinetic Constraints; in QUB Express, right-click the rate constant and choose Add Constraint.
In principle, more iterations are always better. The most likely rate constants are those which have been iterated until no more improvement is possible. In practice, the optimizer can get stuck, so we keep max iterations between 50 and 100, and run MIL repeatedly until there is no improvement in log-likelihood.

Amp and area of open and closed components

I'm trying to figure out how to get the "area" or "weight" of each component from the Curve fitting window (relative area or %). After Exponential fitting i have values for tau (duration in ms) and Amp but, how could i get... let say, % of each component?

Correcting false closings

started to record and analyze events using qub, i have a simple question to start to improve the idelization. I realized that the program misunderstand some closings, and that is due to clusters have so brief closings within. How can i correct those closings? i suppose that this would be do by hand, in the same way that i correct the multiple openings using the "J" key to join Idl, but i don't know how... if it is possible!

This is a difficult question...

The simplest answer is to go to the open state's properties, and make Std smaller, then re-Idealize.

The rate constants also have an effect on idealization. In particular, fast opening and closing rates cause it to detect more short events, so you could try smaller rate constants. Some people idealize first with a simple model, use MIL to find a more likely model, then use that model to re-idealize.

The re-estimation options in Idealize can also help. "Fix kinetics" locks in your rate constants. "Fix std", class 2, will lock in your custom open std.

There are yet more options in Idl/Base (idealize with optional baseline correction) -- try various "re-estimation" and "most likely sequence" options, and be aware the "Fix" settings have no effect.

Due to finite rise times, events shorter than some threshold can't be detected reliably. The MIL likelihood algorithm uses a "dead time" -- events shorter than Tdead (td) are discarded, and the math is adjusted. This happens invisibly each time you click MIL Rates, so you can adjust td (at upper right) and see how MIL behaves with different dead times. You will still see the short events on screen, unless you lock in a particular dead time and permanently discard the short events, by Idealizing with the option "apply dead time to idealization." So if the false events are shorter than the dead time, which is typically 2 to 3 samples, then you can ignore them because MIL ignores them too.

Of course if they are rare enough you can 'J'oin them, but that can be a lot of work.

Using QUB to transform ABF

I have a series of (gap-free) data collected as ABFs, and I'd like to pre-process them by converting current into conductance, and then bring them back into ClampFit for event detection. pClamp doesn't seem to let me do any "column arithmetic" on the ABFs, whereas Origin can, but doesn't save files back into ABF. Is it possible to use QuB to do this conversion?

QuB can't do column arithmetic or save as ABF. However, it can read a text file from Origin, detect events (idealize), and do further analyses such as histogram fitting and maximum likelihood modeling.

www.qub.buffalo.edu/wiki/Single_Channel_Measurements.html#The_Idealization_Process

www.qub.buffalo.edu/wiki/Tutorial-Single_Channel_Measurements.html

QuB Filter

What type of filter is used in QuB? It appears to be a 3 pole bessel.

QuB filters by convolution with a Gaussian in the frequency domain. In data properties, you can choose the "moving average" filter, which is faster but more approximate.

Select and K Means

What we wanted to do was to select "length" (after chopping the file) and use k-means to determine the number of groups that burst lengths fell into and then determine the mean of each group. We estimated that there would be 4-5 burst length groups per condition. To be sure we used the "k-means random" function with k=7 and tested all <= k for 25 iterations. We analyzed the output values and chose 5 groups (k=5). This felt like a somewhat arbitrary choice though. What do the numbers in the blue report represent after k-means is run? There is always a large number (sum squared deviation?) and then a bunch of numbers separated by commas in brackets.

Once we chose 5 groups, we re-ran the file with k=5 for 25 iterations and recorded the mean for each group (we are fitting >1200 lengths). What we noticed however was that if we re-ran the k-means at k=5 for another 25 iterations that it produced different numbers. We tried again, and again a different result. We also tried this for other variables such as "openings per burst" (nevent2) and noticed a similar amount of variability. Are we misusing k-means? I was under the impression that k-means output the "best" grouping after all the iterations are complete. Is this the case?

The numbers in the blue report are sum squared deviation, followed by the color of each datapoint in the best trial. I don't know how to definitively choose K, but I have heard the suggestion to plot K vs SSD and look for the inflection point.

Two sources of variability in the output:

- K-Means is an example of an Expectation Maximization algorithm; as such it converges slowly and gets caught in local minima. Try increasing the number of trials (starting colorations).
- arbitrary color-numbering: [1, 0, 0] == [0, 1, 1]

Difficult data

Why do we get such varied idealized fits to simulated data (filtered at 2 kHz) and real data (also filtered at 2 kHz)? Please note the many transitions between conductance states that is picked out of the noise (!?) in the real data, which you do not see in the simulated data. (Please also note that we very much doubt that the many transitions in the experimental data are real).
We can not get our multi open state model (3 different conductance states) to fit our data (MIL does not reach a meaningful solution) and we think that this may be part of the problem. Obvious reasons could naturally be that the experimental data are too noise (not clean/good enough), or that the model is simply wrong. Any comments?

Fitting data with conductance states that are close in amplitude is difficult. It looks like the states differ by less than 1 pA.

When fitting "difficult" data, it's important to take care in assigning amplitudes and standard deviations for the states (both matter for SKM). You can use the amp tool to assign amplitudes (I think it works for more than one conductance), or do it manually with the grab function. It is very important to run amp or use grab at the same digital filter (or unfiltered) you run SKM at to idealize the data.

I would try to collect data at 2.5 times the frequency of the physical filter, ie filter at 10 kHz and record at 25 kHz, or filter at 5 kHz and record at 12.5 kHz, or filter at 2 kHz and record at 5 kHz.

Set the dead time to 2.5 samples.

Filter at low frequency for display purposes only. Select a conductance state and "grab" unfiltered (or at the highest number digital filter possible), repeat for all your states.

When you run SKM, also run it unfiltered. Or both grab and SKM both at the highest number filter possible for good results. Make sure the idealization approximately follows what you would do by eye.

When running MIL, it works best in practice to start with simple models and build to more complex models. Start with C-O1-O2-O3 with your three conductance states first. Then build your more complex model one state by one. Make sure all the components are visible in the histogram at each step.

SKM also depends on the model and the rates that are in it. Once you have a reasonable idealization and fit, it may be useful to reSKM and MIL.

I'm not an expert on fitting binding, but I think it works best if you fit across concentrations, is this what you're doing? For the purposes of fitting at a single concentration, just set the rates to scale appropriately for your model. When I fit binding, it can be difficult to keep track of the units, make sure the units are correct for the concentration to give appropriate units on the rates.

Transitions that are not in the model

Why do we see transitions between one open state conductance straight into another conductance level, even when the model (see below) defines that the ion channel has to go through a closed state before entering another open state with another conductance level? (state 4, 5 and 7 have different conductance levels)

With infinitely fast sampling we would see every transition; however, the exponential distribution of event durations means several events are faster than the sampling interval. This plays out mathematically: take a three-state model that starts in state one, with connections between states one and two, and two and three. The initial occupancy probability vector P0 is [1, 0, 0]. The rate constant matrix Q has zeros at 1,3 and 3,1, since 1 and 3 aren't directly connected, but the probability of being in state 3 after one sample is nonzero (it's the third entry in the vector P=P0 * exp(Q*dt)).

About Tcrit

I have a question about Tcrit. On this data file I am running MIL Rates on my data. When I add another black closed state (5 total) there is no Tcrit reported in the blue report window. When I remove a black closed state (4 total) a Tcrit is reported (as see in this file). What do you suggest doing in this situation to solve for Tcrit?

Could anyone please explain how the MIL program estimates the Tcrit and why does it give us more than one value?

In one paper in which QuB was used, it says 'Tcrit was estimated as the intersection of areas between the second and the third closed state durations'- now is there a particular reason for choosing 2nd and third closed durations?

How to distinguish models?

I first simulated 10 segments of data at equilibrium as indicated in the tutorial "Simulation-Simple", then I tryed the tutorial "Basic MIL" of "Single Channel Kinetics" by building the model C-O-C. Afterwards, I repeated MIL by building the following different model: C-C-O.

Now, if I wouldn't know from which starting model I obtained the simulated data file, how could I discriminate between the two tested models C-O-C (actually the right one) and C-C-O and tell which model fits data better?

Another question: why the shapes of the dwell closed times histograms drawn in the "Results" window and in the "Histogram" window (in both cases "count/total vs Duration(log10)" are clearly different?

There are three possible reasons the histograms are different.

- Dead time > 0: "Results" histograms use the dead time to remove short events; "Histogram" ones don't.
- "Smooth binning" in MIL options: it distributes each event into multiple bins to counteract the effect of sampling; the "Histogram" window doesn't.
- "Sqrt ordinate" in MIL options: I think this is to exaggerate the shape of the graph; it's common in the literature, but again, the "Histogram" window does not do this.

The question about how to discriminate between models is more difficult. I think it has been proven, you can't discriminate between two models at equilibrium if they have the same number and color of states. So you need to get it out of equilibrium, or use some other trick.

If the channel binds to a ligand or something like ATP, one of the rates is a binding step that should be proportional to ligand concentration. Try recording the channel at different concentrations: the correct model should be the same at all concentrations, except for one rate that varies proportional to concentration.

Other things you can vary include voltage, pressure, and even point mutations. Also, you can use a stimulus to drive the channel into a particular state, then mark that as the start state in the model. If the models have loops (cycles) you can "Balance all loops" under model: properties: kinetic constraints.

I would recommend looking through our list of Research Using QuB

www.qub.buffalo.edu/wiki/Research_using_QuB.html

to see how other people have done it.

Idealizing glycine sublevels

I've never undertaken kinetics analysis of single-channel data, so please excuse my (most likely) naive questions. I would like to look at changes in open and close times for some glycine receptor data. My patches generally have double openings present and I have tried to excise these from the data using the delete function. When I used the erase function, I found that the "erased events" returned whenever I saved the data file.

My first question is that when the data is idealized the amplitudes of the openings appear to be underestimated. Glycine receptors do exhibit sub-conductance states. How do I model these correctly?

Secondly, when the data is idealized, brief closures (ie those that don't return completely to baseline) are ignored. As a result, the closed times appear to be longer than they are. Is it possible to set some sort of threshold to recognize these brief closures?

Sorry about the delete function. I've heard it doesn't always work right. Instead, try "delete idl" (leaves a gap in the idealization) or "join idl" (replaces selected idealization with the level at the beginning of the selection, e.g. closed).

Sublevels and brief closings can hopefully both be detected with a bigger model. For sublevels, add a state with a new color, e.g. blue, select a piece of data at the sublevel, right-click the blue state and "grab." Connect it as you see it in the data; i.e. between closed and open, if it happens during the transition. For brief closings, add a closed state, connected to the open state, with a really fast exit rate. The idealization is somewhat sensitive to rate constants, so it might get better as you tweak them. Also experiment with the idealization options: "fix kinetics" (rates), "fix amp", "fix std", and "re-estimate" (if re-estimate is un-checked, all rates, amps and stds are fixed).

Some missed events are okay. They're inevitable due to finite rise time in your equipment, so the rate constant optimizers MIL and MSL discard all events shorter than the cutoff "td" (dead time), in the upper-right corner. Then they adjust mathematically for their absence. The adjustment is valid as long as the missed events take up a small fraction of total time, and the rate constants are slower than 1/td.

If you really want to get the brief ones, though, you could try modeling them as their own color of substate, then use the "Edit Idl" button to change e.g. class 4 events into class 1.

Event amplitude histograms

Is there a way to generate a histogram showing the event amplitude as a
function of event duration and/or count as a function of current
amplitude?

QuB can save a table of each event with its mean current (as a text file):

File -> Idealized Data -> Save idealized with event stats...

[*QUB Express:* Tools:Idealization:Save...]

Hopefully you have a stats program which can take it from there.