Nice video, Mike. We also use the stochastic model when the number of molecules is low here. The noise or randomness plays a crucial role. Good video. Keep it up
Thanks Yamen! Yeah I think the randomness is especially important in low copy number situations. For example, the difference between 1 or 0 transcripts is typically more important than the difference between 50 or 49 transcripts.
Thank you - clear, concise, and to the point. I think I could use a stochastic model idea for manufacturing. We have tons of randomness in the process, everything from materials to operator variation, to when things are pulled from a freezer before use.
Thanks for watching! And yeah I think there's an entire field of stochastic modeling devoted specifically to industrial applications like manufacturing, and how to make things as efficient as possible when operating under uncertain conditions. Interesting stuff!
Yeah there's a actually a hypothesis in economics that stock prices tend to follow a random walk: en.wikipedia.org/wiki/Random_walk_hypothesis so good observation! Thanks for watching 🙂
Hi Federico! Hmm that's an interesting question. I think that's true from a philosophical standpoint, in the sense that a lot of the things we model as "random" aren't truly random and are actually deterministic, but they're being determined by deeper, more complex things that we don't have enough information about so we just treat them as if they're random. Like in the gene expression bursting example, we model the bursts of gene expression as occurring randomly, but if we were to make the model more complex and track the exact position of every transcription factor floating around in the cytoplasm then the bursts of gene expression would NOT be random, but would happen when the TFs bind to the DNA. Another example -- technically we could say that the result of a coin flip isn't random, because it's determined by the starting position of the coin and the laws of physics. But tracking all of those things to model a coin flip as a deterministic process would be insanely hard, so we just treat it as random since we don't have enough information to predict the outcome either way. So I think that's an interesting philosophical point. However in practice, the goal of our models is not to be perfect, but to be useful. So a lot of the time, treating these things as random is useful for us when we don't have enough information to track the true deterministic causes of them, or we just don't want that precise a level of detail when approximating the thing as a random variable will do. So sometimes it might make sense to try to convert a stochastic model to deterministic by tracking more variables, but other times it might be better to just keep the model simple by incorporating randomness. Thanks for watching! 🙂
Nice video, Mike. We also use the stochastic model when the number of molecules is low here. The noise or randomness plays a crucial role. Good video. Keep it up
Thanks Yamen! Yeah I think the randomness is especially important in low copy number situations. For example, the difference between 1 or 0 transcripts is typically more important than the difference between 50 or 49 transcripts.
Thank you - clear, concise, and to the point.
I think I could use a stochastic model idea for manufacturing. We have tons of randomness in the process, everything from materials to operator variation, to when things are pulled from a freezer before use.
Thanks for watching! And yeah I think there's an entire field of stochastic modeling devoted specifically to industrial applications like manufacturing, and how to make things as efficient as possible when operating under uncertain conditions. Interesting stuff!
The stochastic indicator on stock charts makes more sense to me now, thanks.
Yeah there's a actually a hypothesis in economics that stock prices tend to follow a random walk:
en.wikipedia.org/wiki/Random_walk_hypothesis
so good observation! Thanks for watching 🙂
Helpful video. Thank you.
Thanks for watching Kehinde 🙂
Short and helpful. Is random a sign of you don't know some variables? If I add new variables can I transform a model from stochastic to determinist?
Hi Federico! Hmm that's an interesting question. I think that's true from a philosophical standpoint, in the sense that a lot of the things we model as "random" aren't truly random and are actually deterministic, but they're being determined by deeper, more complex things that we don't have enough information about so we just treat them as if they're random.
Like in the gene expression bursting example, we model the bursts of gene expression as occurring randomly, but if we were to make the model more complex and track the exact position of every transcription factor floating around in the cytoplasm then the bursts of gene expression would NOT be random, but would happen when the TFs bind to the DNA.
Another example -- technically we could say that the result of a coin flip isn't random, because it's determined by the starting position of the coin and the laws of physics. But tracking all of those things to model a coin flip as a deterministic process would be insanely hard, so we just treat it as random since we don't have enough information to predict the outcome either way.
So I think that's an interesting philosophical point. However in practice, the goal of our models is not to be perfect, but to be useful. So a lot of the time, treating these things as random is useful for us when we don't have enough information to track the true deterministic causes of them, or we just don't want that precise a level of detail when approximating the thing as a random variable will do. So sometimes it might make sense to try to convert a stochastic model to deterministic by tracking more variables, but other times it might be better to just keep the model simple by incorporating randomness.
Thanks for watching! 🙂