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I'm working on trying to implement a hidden markov model to model the affect of a specific protein that can cut an RNA when the ribosome is translating the RNA slowly.

Some brief background:
The ribosome translates mRNA into protein
The ribosome can occasionally pause on the mRNA due to things such as secondary structure
There is a protein (call it protein $X$) that can cleave the mRNA when the ribosome is paused.

I'd like to build an HMM that can model this process, however It's been difficult to find references. I've seen that some people use absorbing states in continuous time HMM that model disease progression, for example here, however I'd like to do this for a discrete case. Most simply, my HMM would look like:
HMM model figure with the arrows modeling the transitions.

The emissions would be things like secondary structure free energy (a continuous parameter), and which codon is being translated (discrete parameter).

The "cut" state is an absorbing state that cannot be recovered from. Ideally I'd like to be able to run the same sequence through this model and sometimes become "cut" with a certain probability. In the end, I'd like this model to be able to predict whether or not a sequence will be "cut" enough to observe a significant difference between it and a sequence that is less likely to become "cut".

Any help/references would be very much appreciated. Thanks!

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  • $\begingroup$ Specifically for which aspect of this problem do you want a reference? There are copious sources describing discrete time Hidden Markov models being used to model processes in genetics (the most common use is gene-finding, which is obviously a different problem from this one). $\endgroup$ – Chill2Macht May 4 '16 at 22:17
  • $\begingroup$ The main thing I'm having trouble finding help with is the modeling of an absorbing state. Other than that, handling both a discrete parameter (codons) and a continuous parameter (folding energy) is also something I'm not entirely sure of how to deal with. $\endgroup$ – lstbl May 4 '16 at 22:19
  • $\begingroup$ Birth and death processes are hidden Markov models, but they are the standard example of Markov processes with an absorbing state. I don't understand though exactly what problem there could be with modeling an absorbing state - just set all transition from the state to 0, and the self-transition probability to 1, and the state is absorbing. As for the discrete vs. continuous parameter issue, the solution would depend upon the specifics of the model. How would the energy affect the choice of codons (the codon affecting the energy doesn't seem like it would be problematic -- just have as many $\endgroup$ – Chill2Macht May 4 '16 at 22:24
  • $\begingroup$ energy functions as there are codons. $\endgroup$ – Chill2Macht May 4 '16 at 22:24
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I don't know if these will help you or not, but it looks like you might want a bivariate distribution for your emission probabilities (one variable being the folding energy and the other parameter being the codons).

Here are some references I found quickly regarding the construction of a joint distribution for which one variable is continuous and the other discrete:

Mixture of Continuous and Discrete Random Variables

joint distribution, discrete and continuous random variables

joint probability distribution of one discrete, one continuous random variable

Density/probability function of discrete and continuous random variables

A basic doubt on joint distribution

Joint pdf of discrete and continuous random variables

This reference comes from a textbook on Bayesian data analysis, which, given your choice of a Bayesian network for a model, might be particularly helpful:

Introduction to Bayesian Statistics

Depending on the depth of your statistics background, the following two references with regards to modeling joint distributions of continuous and discrete data may be more or less helpful:

Longitudinal Data Analysis

Topics in Modelling of Clustered Data

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