Conditional Probability

In summary, the problem at hand involves determining the number of X particles needed to activate 3 out of 9 sites, taking into account the irreversible binding of X and Y particles. The probability of binding to active and inactive sites is calculated using a hypergeometric model, and the worst case scenario is that 6 X particles are needed to activate 3 sites. In the more complex scenario with the addition of Y particles, the number of X particles needed increases due to the binding of Y to both active and inactive sites. This explains why the potency of a drug can be affected initially with the addition of a small concentration of antagonist.
  • #1
mtc1973
112
1
Okay so I have a complex setup that I hope I can convey.

I have 9 sites to which X can bind. 6 out of the 9 sites are active and 3 out of the 9 sites are inactive. I need 3 of the active sites to be bound to get the response I am looking for - which we will call EMAX.

So when I add a single X - the chance of it binding to an active site is 6/9 the chance of it binding to an inactive site is 3/9.

My probability knowledge is shaky - bear with me.

Assume that the binding is irreversible. So how many X do I need to add to be sure I have activated 3 active sites. Or more precisely, how many X do I need to add to get a >95% chance that 3 active sites are bound.

Then I want to go more complicated. Say I add 3 Y - which inactivates the sites. The chances are that 2 active sites will be inactivated and 1 inactive site will still be inactive with Y bound.

Now under these new conditions - how much X do I need to add to be sure 3 remaining active sites are occupied?

So I know it will be a probability - so I guess let's say that how much X do I need to add to have a greater than 95% chance that 3 active sites are now bound with X to get EMAX
 
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  • #2
PS - this ain't homework! On an intrinsic level its clear to me that in the new condition more X has to be added to ensure that 3 active sites are bound (I hope my intrinsic thoughts are correct!) - but I want to be able to put a number on it.

FYI - this is a real word problem I am trying to figure. The problem comes from trying to explain why the potency of a drug is affected initially (but not the efficacy) when you add a small concentration of antagonist to a population of receptors that have spare receptors included. Then as you increase antagonist concentration you finally see a fall in EMAX - as there are not enough active sites left for agonist to work at.

My probability maths does not extend beyond coin flipping and dice rolling - hence I hope any explanations are at a level I can appreciate!
 
  • #3
For probability 1, it's fairly easy to see that you need at least 6 X. After all, the worst case scenario is that you bind all the inactive sites first, and then the next 3 X that you place are active.

For the rest, this is precisely the model that I (and probably a lot of other people too) use when thinking about hypergeometric probabilities. Suppose that you place n X on arbitrary sites. You can do this in [itex]\binom{9}{n}[/itex] ways (the number of ways you can choose n sites to bind to from the 9 available. The probability that k are bound to any of the 3 inactive sites and n - k to active sites is then
[tex]\frac{\binom{3}{k} \binom{6}{n - k}}{\binom{9}{n}}[/tex]
 
  • #4
OKay - there are stupid questions even though we tell everyone there ain't - so just so I am totally clear and making no assumptions - define k.
 
  • #5
k is the number (out of the n sites that you are binding to an X) of X that are bound to an inactive site.

For example, the probability that if you place 4 X's and exactly 1 is on an inactive site and 3 are on an active site can be calculated by plugging in n = 4, k = 1.

Of course, you are not interested in a single value of k but in all possible values (question back: what are the allowed values?)
 
  • #6
well in situation 1 k = 3 i guess, but then in situation 2 when we add the antagonist - it is more difficult - since antagonist can either bind to an inactive receptor and do nothing (for the first antagonist particle there is a 3/9 chance of that), but then there is a possibility that the antagonist binds to an active receptor and inactivates it - and thus the pool of inactive receptors (k) will increase. Then we also have to think about the fact that I have 3 sequential antagonists binding and so that then changes the k pool too?

Am i making sense?
 
  • #7
I didn't really get that far yet, I thought we'd solve the simpler problem first.
If the binding of X's and Y's is independent you can find the probability that not 3 but 3 + d sites are deactivated and for every value of d you can find the probability that the X's will activate 3 sites (where the case d = 0 is the simpler one that I was looking at).
 
  • #8
I should add though that your worst case scenario take on the problem will let me explain it beautifully. As you say in instance 1 you need 6 agonist - 3 to use up the inactive sites and the next 3 are guaranteed to fill active sites.
Extending that into the second scenario - when i first add 3 antagonist particles the likeliest result is 2 active receptors are bound to 1 inactive receptor (probability is 3 inactive being bound 1/84, 3 active being bound is 20/84 - not sure how to discriminate 2 active 1 inactive or 2 inactive 1 active - think it is 45/84 that we have 2 active 1 inactive and 18/84 that 2 inactive and 1 active) - thus the total pool of inactive receptors is now 5. Therefore 5+3 agonist is required worst case to get the same response.
That doesn't actually calculate the probabilities as I initially thought I would have to do - but it explains far more simply why we need more agonist in the presence of antagonist to get the same response as when no antagonist is present.
 
  • #9
And yes - the binding of X and Y is independent and each event does not change the binding of the other. So a single receptor can have an X and a Y bound. But would not be active.
 

1. What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is often denoted as P(A|B), where A is the event we are interested in and B is the event that has already happened.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the intersection of the two events by the probability of the given event. This can also be written as P(A|B) = P(A∩B) / P(B).

3. What is the difference between conditional probability and joint probability?

Conditional probability is the probability of one event occurring given that another event has already occurred, while joint probability is the probability of two events occurring together. Joint probability does not take into account any prior knowledge about the occurrence of one event.

4. How is conditional probability used in real life?

Conditional probability is used in many real-life scenarios, such as weather forecasting, medical diagnosis, and risk assessment. For example, a doctor may use conditional probability to determine the likelihood of a patient having a certain disease based on their symptoms and medical history.

5. What are some common misconceptions about conditional probability?

One common misconception about conditional probability is the belief that if two events are related, one must have caused the other. However, correlation does not necessarily imply causation. Another misconception is the idea that conditional probability can only be used to predict future events, when in fact it can also be used to analyze past events and make inferences about their causes.

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