First off, thanks for the responses. I didn't really expect to get a "course" in this thread. However, if you have pointers to good sources on the subjects you mentioned, I would greatly appreciate it.
I'm not quite sure what you're looking for here, but will try to do the best that I can.
With regards to ANOVA, I didn't do any research at all into its history and the only background that I have in it is statistics. I believe population geneticists probably use it more. As far as I remember, it's a test of significance. i.e. the probability with which the null hypothesis can be rejected. Calculating the Z-values/p-values using a t-test enables people to determine if values are significantly different from each other, and therefore draw conclusions. The analysis takes into account the sample size and therefore a smaller difference is required for significance to be reached if the sample size is larger. i.e. An average difference of 2 may be significant in a population of 1000000, but the average difference observed in a population of 100 may need to be much bigger to achieve statistical significance. There are various assumptions at play here, which I won't go into because I'm not a statistics major and would only embarrass myself by speaking in general terms.
That's OK. I am well versed in the various ANOVAs and the Design of Experiments. I was just hoping to get a better idea of what Fisher was actually working on when he invented these things.
BTW, t-tests work on means. ANOVAs work on variances.
I find these types of history lessons very enlightening…like reading the Principia, The Origin of Species, Edison's Diary, Tesla's autobiography, Watson's and Crick's independent accounts of the events that led to the discovery of the structure of DNA, etc.
The term "homology", like "species" and "gene", carries several different definitions and emphasis according to which biological subfield you're from. I am not sure what you are asking. If you're talking about evolutionary biology, the definition of the terms above would be very different than if you were talking about genetics or ecology.
Upon further thought (tell me if I'm wrong) you may be talking about conserved sequences of genes through evolution. Is that right?
I believe this is pretty much the question I was asking. But I am such a n00b to these things that I have a hard time making the questions more crisp.
It's quite simple, in that case. People find that a particular gene, when cloned, transcribes a particular protein. They name the protein. They create knockouts in other species, e.g. mice. They find that a phenotype is observed, consistent with the speculated function of the protein. They do a search across genomes mapped in various different species and find that previously un-assigned stretches of sequences that originally had no known function are very similar in sequence. i.e. mathematically the chance that they were so similar by random chance is very very small.
Ah, thanks. I believe that was the insight I was looking for. If you have pointers to the actual math/algorithms used, would really appreciate it.
They call these genes "orthologs" of the original gene that was characterised, and assume that they produce a similar protein with similar function. "Homologs" are the same allele of the same gene in the same species, and when an individual carries 2 copies of the same allele, they are "homologous" at that particular gene locus.
This actually confused me a bit, because my own research turned up different meaning from what you listed. The meaning of Orthologs pretty much matches what I read, I think. But I thought Homologs referred to general similarity through common ancestry…and that what you were calling Homologs were called "paralogs". Perhaps it depends on the source defining the words?
I didn't study evolutionary biology or genetics in detail (zero interest) but can give you a general outline of the terms as they are used in genetics.
"gene" = DNA that encodes a protein, is transferred from generation to generation (how is outlined in the central dogma of molecular biology).
"alleles" = different forms of the same gene that can cause the development of an abnormal/different phenotype.
"genetic drift" = changes in allele composition in a population, i.e. not following mendelian genetics. This can be due to several reasons, including (but not limited to) selection.
"gene flow" = transfer of alleles between populations.
I did take a basic Biology course, and most of what you listed seems like review (I had just forgotten).
"Gene flow," however, is new.
When you say: "transfer of alleles between populations," do you mean transfer of alleles from one generation to the next, or a population at one instance in time, and the population at another instance in time (that is much of the 1st population may be re-represented in the second.)?
It's really difficult to explain all of this without context, or the assumptions/conditions of the above definitions. But if I was going to type it all out, it would be like conducting a basic genetics course over the Internet, and I'm not going to do that. Also note that the above definitions are actually highly artificial constructs... Whether the modeling has any relation to reality is highly debatable.
Thanks for your effort nonetheless. If you know of good sources, please let me know.
As far as the reliability of the models, I figured as much considering how much the models leave out. Wikipedia says the simple models leave out:
recombination, natural selection, and gene flow or population structure. Even though I don't know what recombination and population structure really are, that list seems like a lot to leave out.
It may be a pipe dream, but part of the reason I am asking these questions is to see if I can use my applied math skills and programming skills (and perhaps some physics) to develop new and useful models.
On cellular electrophysiology... I have the basic class note taken during an undergrad course. Unfortunately the class did not use a textbook so I can't just refer you to something direct. The action potential is described by Hodgkin and Huxley's equation. If you can understand what goes into the equation, then everything else should follow.
Ah. It is certainly not a traditional transmission line like model since it has built in voltage and current sources. And no inductive or capacitive components (though the gating mechanisms seem like they creates implicit capacitances).
It also seems like this model does a good job since it is (according to Wikipedia at least) "widely regarded as one of the great achievements of 20th-century biophysics."
Well, the only thing not touched on are the Medical Imaging Algorithms. Hopefully, someone else can address that.
