5 ms·

So I admit I haven't skimmed the paper yet. Is it as comprehensive a map of DNA-encoded transcription factors as can be had? I ask because I can understand the

by sounds 13y ago

So I admit I haven't skimmed the paper yet. Is it as comprehensive a map of DNA-encoded transcription factors as can be had? I ask because I can understand the way DNA codes for proteins–as a software-head–but the article definitely gets me with this "second language" and I want to think more about how the overlapping meanings might have developed (and how that might be apply to digital code). Other than "start," "stop," "intron," "exon," and "suppress" (aka disable), what do the transcription factors do in terms of actual cell proteins? I admit that a really detailed description of cell protein processes will be beyond my depth. :)

waterlesscloud 13y ago
I know next to nothing about this field, but I just sort of brute-forced my way through the OP paper with the help of wikipedia. It's a painful process. :-) But it has piqued my curiosity about the field. Are there any good introductory books/websites for the curious layman to get a grounding?
- skosuri 13y ago
  Depends on what level. Wikipedia is actually quite good for this sort of thing. The classic book as a basic overview of molecular biology in general is the Molecular Biology of the Cell [1]. A really cool website to look at on cutting edge studies such as these is the ENCODE consortium's publications in Nature [2]. They simultaneously published 30 papers last year, and this is a really cool meta page that explains the ENCODE project and what they found. [1]. http://www.ncbi.nlm.nih.gov/books/NBK21054/ http://www.ncbi.nlm.nih.gov/books/NBK21054/ [2]. http://www.nature.com/encode/ http://www.nature.com/encode/
- skosuri 13y ago
  Oh and for Codon usage in general, a really good review can be found here [1]. [1]. http://people.cs.vt.edu/~heath/VTMENA/CodonBias/PlotkinKudla_NatureReviewsGenetics_2011.pdf http://people.cs.vt.edu/~heath/VTMENA/CodonBias/PlotkinKudla...
- mvleming 13y ago
  The Machinery of Life [1] is a fantastic book, I was one of those curious laymen and boy did it saturate my thirst for learning about microbiology. I think it's close to the time I read it again. [1] http://www.amazon.com/The-Machinery-Life-David-Goodsell/dp/0387849246 http://www.amazon.com/The-Machinery-Life-David-Goodsell/dp/0... Edit: Whoops, no Markdown support. Revised link to use footnote.
- wrongc0ntinent 13y ago
  Robert Sapolsky[0] has his Stanford course on human behavioral biology on youtube, almost in its entirety. The whole course is very accessible and much broader than the topic here, but if you wanna go straight for the molecular genetics section, it's parts 4 & 5. Amazing lecturer. [0] http://en.wikipedia.org/wiki/Robert_Sapolsky http://en.wikipedia.org/wiki/Robert_Sapolsky
- smartial_arts 13y ago
  "Tales from Genome" course on Udacity is pretty awesome, although at times painfully anal/detailed with regards to assignments. I think you can skip those, though. https://www.udacity.com/course/bio110 https://www.udacity.com/course/bio110
kissickas 13y ago
In case you were unaware, another really amazing example of overlapping meainings is in "normal" DNA code, where genes can overlap themselves. Because a stop codon would kill transcription, these are either on alternate reading frames (i.e. the codons begin on a different one of the three possibilities) or on the antisense side of the gene (the opposite base pairs from those corresponding to the original gene). It blows my mind that such an incredible amount of stuff could go wrong if there is just a single mutation, and that such an incredibly delicate system arose through evolution and survived billions of years of natural selection.
- Myrmornis 13y ago
  That's only known in viral genomes, right?
haldujai 13y ago
I may be misinterpreting what you said but it seems as though you consider 'start' 'stop' 'intron' 'exon' 'suppress' as properties of transcription factors. If this is not the case I apologize but to clear any misconception those are all properties of the DNA sequence, transcription factors BIND to DNA sequences but are not part of the gene itself.[0] I'm also not quite sure what is meant by suppress. Really all transcription factors do is up-regulate or down-regulate genes, i.e. make more proteins from this gene or make less. However simple that seems this is actually an extremely important function. Other than enabling cells to respond to say, heat, stress, or tension by making proteins that help them cope, gene regulation levels are important in differentiation. This is the process by which cells 'pick' a destiny, i.e. brain cells, kidney cells, etc. Generally, you can think of every cell in your body (with some limited exceptions) as containing all genes in your body, so how does the cell know what type of cell it should be? One way is by transcription factors, which will tell the cell make more of this kidney-related protein, or other proteins that help determine its fate. They have also been implicated in memory formation and almost everything else you can think of. But for the actual process of what transcription factors do they just regulate how much protein should be made from this gene. [0]http://en.wikipedia.org/wiki/Transcription_factor http://en.wikipedia.org/wiki/Transcription_factor
limpon 13y ago
I'll try to give you a description of what transcription factors do and what the importance of Stamatoyannopoulos finding is. I hope it doesn't sound too weird. Just noticed, how long my "brief" description is, so here is a TL;DR: Comparing cells to computer programs: A program figures out by itself which functions to call based on the byte sequence of the function in memory and changes these preferences based on the environment its running in. As you know, the DNA in our cells encodes the blueprint for all proteins that exist in the human body. I'm trying to compare this to string encoding, but might be completely wrong, because string encoding is more difficult to me than DNA encoding ;) The DNA is our hard drive. This hard drive holds the information for all proteins. A protein will be a string. Our alphabet is made out of 21 characters, they are called amino acids. Each character (amino acid) is encoded as a 3bit sequence on the DNA, the so called codon. Opposite to the binary system, we have a base4 system, meaning each bit can have 4 different states (A,T,C,G), the nucleic acids. So, given a byte sequence from the hard drive (gene on the DNA), we always look at codons (three bits) and can then translate those codons into amino acids. ATG translates to Methionine, GGA translates to Glycine etc.... Having 4^3 (64) different codons but only 21 amino acids, some amino acids can be represented by several different codons. On top of that, some codons have a special meaning, such as "\n". Those special codons are called "stop codon", because it marks the end of a protein and the program stops decoding from this point on. In addition, there is also a "start codon" that marks the begin of a string. When a cell wants to make a certain protein, it copies the byte-sequence from the DNA and translates it into the actual amino acid sequence and as such forms proteins (prints a string in our example). The problem we're now facing is that every cell in your body (with a few exceptions, of course) has the exact same DNA sequence. But still, nature is able to somehow make different celltypes, such as a skin cell, blood cell, neuron, you get the point. The way how cells "know" which proteins to make is that the hard drive doesn't only hold encoded strings but also some byte sequences in between that don't encode any strings. These sequences are called regulatory sequences. These sequences attract so called transcription factors. Transcription factors are proteins that you could understand as a pointer. They will point to some genes, but not to others and thus instruct the cell which byte sequences should be translated into strings. These regulatory sequences can not only attract transcription factors, but also suppressive factors. Those are proteins that prevent transcription factors from binding and thus ensure that certain proteins are not made. There are hundreds of different transcription factors and repressors encoded on the DNA, but only a handful exist as protein in each given cell and they define the difference between celltypes. They come and go as the cell's environment changes as well. So, going back to our string analogy, say you have saved thousands of strings on your harddrive but only want to show a subset of those to the user (eg language selection). You start your program by passing some information about which strings to choose. The same happens here as well, only that the information which strings to chose is somehow intrinsic but still different between celltypes. And on top of that you not only have distinct languages, but the languages overlap and language A uses string 1,2,3; language B uses string 2,5,6; and language C uses string 1,2,6 etc. Because of this complexity it is very difficult to understand how the cell "knows" which proteins are right. Stamatoyannopoulos now for the first time proved that even though two different codons encode the same amino acids they might attract different transcription factors. To get an understanding of the importance of this finding, imagine having a character encoding, such as DNA, where two different byte sequences encode the exact same string. Now imagine having a program that automatically chooses which strings to display based on which byte sequences encode certain characters. And now, image that the DNA doesn't only encode strings, but also functions and closures, pointers, variables etc. So each time a part of the DNA is translated, the state of the program changes. Nature is vey complex and we're just at the beginning of understanding the code. We have methods where we can out-comment some part of the DNA and see how the program changes (mutations). In addition, we recently learned how to see which part of the DNA is being used in certain cells. And in this paper, they refined this method by actually looking at all pointers in a cell and figuring out which part of the DNA they pointed at. And they did this for 81 different cell types to find networks that act together in certain settings.

by sounds 13y ago

waterlesscloud 13y ago

I know next to nothing about this field, but I just sort of brute-forced my way through the OP paper with the help of wikipedia. It's a painful process. :-) But it has piqued my curiosity about the field. Are there any good introductory books/websites for the curious layman to get a grounding?

skosuri 13y ago
Depends on what level. Wikipedia is actually quite good for this sort of thing. The classic book as a basic overview of molecular biology in general is the Molecular Biology of the Cell [1]. A really cool website to look at on cutting edge studies such as these is the ENCODE consortium's publications in Nature [2]. They simultaneously published 30 papers last year, and this is a really cool meta page that explains the ENCODE project and what they found. [1]. http://www.ncbi.nlm.nih.gov/books/NBK21054/ http://www.ncbi.nlm.nih.gov/books/NBK21054/ [2]. http://www.nature.com/encode/ http://www.nature.com/encode/
skosuri 13y ago
Oh and for Codon usage in general, a really good review can be found here [1]. [1]. http://people.cs.vt.edu/~heath/VTMENA/CodonBias/PlotkinKudla_NatureReviewsGenetics_2011.pdf http://people.cs.vt.edu/~heath/VTMENA/CodonBias/PlotkinKudla...
mvleming 13y ago
The Machinery of Life [1] is a fantastic book, I was one of those curious laymen and boy did it saturate my thirst for learning about microbiology. I think it's close to the time I read it again. [1] http://www.amazon.com/The-Machinery-Life-David-Goodsell/dp/0387849246 http://www.amazon.com/The-Machinery-Life-David-Goodsell/dp/0... Edit: Whoops, no Markdown support. Revised link to use footnote.
wrongc0ntinent 13y ago
Robert Sapolsky[0] has his Stanford course on human behavioral biology on youtube, almost in its entirety. The whole course is very accessible and much broader than the topic here, but if you wanna go straight for the molecular genetics section, it's parts 4 & 5. Amazing lecturer. [0] http://en.wikipedia.org/wiki/Robert_Sapolsky http://en.wikipedia.org/wiki/Robert_Sapolsky
smartial_arts 13y ago
"Tales from Genome" course on Udacity is pretty awesome, although at times painfully anal/detailed with regards to assignments. I think you can skip those, though. https://www.udacity.com/course/bio110 https://www.udacity.com/course/bio110

kissickas 13y ago

In case you were unaware, another really amazing example of overlapping meainings is in "normal" DNA code, where genes can overlap themselves. Because a stop codon would kill transcription, these are either on alternate reading frames (i.e. the codons begin on a different one of the three possibilities) or on the antisense side of the gene (the opposite base pairs from those corresponding to the original gene). It blows my mind that such an incredible amount of stuff could go wrong if there is just a single mutation, and that such an incredibly delicate system arose through evolution and survived billions of years of natural selection.

Myrmornis 13y ago
That's only known in viral genomes, right?

haldujai 13y ago

I may be misinterpreting what you said but it seems as though you consider 'start' 'stop' 'intron' 'exon' 'suppress' as properties of transcription factors. If this is not the case I apologize but to clear any misconception those are all properties of the DNA sequence, transcription factors BIND to DNA sequences but are not part of the gene itself.[0] I'm also not quite sure what is meant by suppress. Really all transcription factors do is up-regulate or down-regulate genes, i.e. make more proteins from this gene or make less. However simple that seems this is actually an extremely important function. Other than enabling cells to respond to say, heat, stress, or tension by making proteins that help them cope, gene regulation levels are important in differentiation. This is the process by which cells 'pick' a destiny, i.e. brain cells, kidney cells, etc. Generally, you can think of every cell in your body (with some limited exceptions) as containing all genes in your body, so how does the cell know what type of cell it should be? One way is by transcription factors, which will tell the cell make more of this kidney-related protein, or other proteins that help determine its fate. They have also been implicated in memory formation and almost everything else you can think of. But for the actual process of what transcription factors do they just regulate how much protein should be made from this gene. [0]http://en.wikipedia.org/wiki/Transcription_factor http://en.wikipedia.org/wiki/Transcription_factor

limpon 13y ago

I'll try to give you a description of what transcription factors do and what the importance of Stamatoyannopoulos finding is. I hope it doesn't sound too weird. Just noticed, how long my "brief" description is, so here is a TL;DR: Comparing cells to computer programs: A program figures out by itself which functions to call based on the byte sequence of the function in memory and changes these preferences based on the environment its running in. As you know, the DNA in our cells encodes the blueprint for all proteins that exist in the human body. I'm trying to compare this to string encoding, but might be completely wrong, because string encoding is more difficult to me than DNA encoding ;) The DNA is our hard drive. This hard drive holds the information for all proteins. A protein will be a string. Our alphabet is made out of 21 characters, they are called amino acids. Each character (amino acid) is encoded as a 3bit sequence on the DNA, the so called codon. Opposite to the binary system, we have a base4 system, meaning each bit can have 4 different states (A,T,C,G), the nucleic acids. So, given a byte sequence from the hard drive (gene on the DNA), we always look at codons (three bits) and can then translate those codons into amino acids. ATG translates to Methionine, GGA translates to Glycine etc.... Having 4^3 (64) different codons but only 21 amino acids, some amino acids can be represented by several different codons. On top of that, some codons have a special meaning, such as "\n". Those special codons are called "stop codon", because it marks the end of a protein and the program stops decoding from this point on. In addition, there is also a "start codon" that marks the begin of a string. When a cell wants to make a certain protein, it copies the byte-sequence from the DNA and translates it into the actual amino acid sequence and as such forms proteins (prints a string in our example). The problem we're now facing is that every cell in your body (with a few exceptions, of course) has the exact same DNA sequence. But still, nature is able to somehow make different celltypes, such as a skin cell, blood cell, neuron, you get the point. The way how cells "know" which proteins to make is that the hard drive doesn't only hold encoded strings but also some byte sequences in between that don't encode any strings. These sequences are called regulatory sequences. These sequences attract so called transcription factors. Transcription factors are proteins that you could understand as a pointer. They will point to some genes, but not to others and thus instruct the cell which byte sequences should be translated into strings. These regulatory sequences can not only attract transcription factors, but also suppressive factors. Those are proteins that prevent transcription factors from binding and thus ensure that certain proteins are not made. There are hundreds of different transcription factors and repressors encoded on the DNA, but only a handful exist as protein in each given cell and they define the difference between celltypes. They come and go as the cell's environment changes as well. So, going back to our string analogy, say you have saved thousands of strings on your harddrive but only want to show a subset of those to the user (eg language selection). You start your program by passing some information about which strings to choose. The same happens here as well, only that the information which strings to chose is somehow intrinsic but still different between celltypes. And on top of that you not only have distinct languages, but the languages overlap and language A uses string 1,2,3; language B uses string 2,5,6; and language C uses string 1,2,6 etc. Because of this complexity it is very difficult to understand how the cell "knows" which proteins are right. Stamatoyannopoulos now for the first time proved that even though two different codons encode the same amino acids they might attract different transcription factors. To get an understanding of the importance of this finding, imagine having a character encoding, such as DNA, where two different byte sequences encode the exact same string. Now imagine having a program that automatically chooses which strings to display based on which byte sequences encode certain characters. And now, image that the DNA doesn't only encode strings, but also functions and closures, pointers, variables etc. So each time a part of the DNA is translated, the state of the program changes. Nature is vey complex and we're just at the beginning of understanding the code. We have methods where we can out-comment some part of the DNA and see how the program changes (mutations). In addition, we recently learned how to see which part of the DNA is being used in certain cells. And in this paper, they refined this method by actually looking at all pointers in a cell and figuring out which part of the DNA they pointed at. And they did this for 81 different cell types to find networks that act together in certain settings.